text:
journal = {LPI Contributions},
  year = {2012},
  volume = {1679},
  pages = {4247},
  month = June,
  eid = {4247},
  owner = {ken},
  timestamp = {2013.08.06}
}

@ARTICLE{DeBono2005,
  author = {{De Bono}, Mario and Maricq, Andres Villu},
  title = {{Neuronal substrates of complex behaviors in C. elegans.}},
  journal = {Annual Review of Neuroscience},
  year = {2005},
  volume = {28},
  pages = {451--501},
  number = {28},
  abstract = {A current challenge in neuroscience is to bridge the gaps between
	genes, proteins, neurons, neural circuits, and behavior in a single
	animal model. The nematode Caenorhabditis elegans has unique features
	that facilitate this synthesis. Its nervous system includes exactly
	302 neurons, and their pattern of synaptic connectivity is known.
	With only five olfactory neurons, C. elegans can dynamically respond
	to dozens of attractive and repellent odors. Thermosensory neurons
	enable the nematode to remember its cultivation temperature and to
	track narrow isotherms. Polymodal sensory neurons detect a wide range
	of nociceptive cues and signal robust escape responses. Pairing of
	sensory stimuli leads to long-lived changes in behavior consistent
	with associative learning. Worms exhibit social behaviors and complex
	ultradian rhythms driven by Ca(2+) oscillators with clock-like properties.
	Genetic analysis has identified gene products required for nervous
	system function and elucidated the molecular and neural bases of
	behaviors.},
  bdsk-url-1 = {http://www.ncbi.nlm.nih.gov/pubmed/16022603},
  bdsk-url-2 = {http://dx.doi.org/10.1146/annurev.neuro.27.070203.144259},
  doi = {10.1146/annurev.neuro.27.070203.144259},
  issn = {0147006X},
  keywords = {animal,animal physiology,animals,behavior,behavioral,behavioral methods,caenorhabditis
	elegans,caenorhabditis elegans proteins,caenorhabditis elegans proteins
	metabolism,calcium signaling,calcium signaling physiology,genetics,learning,learning
	physiology,locomotion,locomotion physiology,models,nervous system
	physiological phenomena,neurological,neuronal plasticity,neuronal
	plasticity physiology,neurons,neurons physiology,sexual behavior,synaptic
	transmission,synaptic transmission physiology},
  pmid = {16022603},
  publisher = {Annual Reviews},
  url = {http://www.ncbi.nlm.nih.gov/pubmed/16022603}
}

@ARTICLE{Dean1999,
  author = {Dean, J. and Kindermann, T. and Schmitz, J. and Schumm, M. and Cruse,
	H.},
  title = {Control of Walking in the Stick Insect: From Behavior and Physiology
	to Modeling},
  journal = {Autonomous Robots},
  year = {1999},
  volume = {7},
  pages = {271--288},
  owner = {fwyffels},
  quality = {1},
  timestamp = {2013.02.07}
}

@ARTICLE{Degallier2010,
  author = {Degallier, Sarah and Ijspeert, Auke Jan},
  title = {Modeling discrete and rhythmic movements through motor primitives:
	a review},
  journal = {Biological Cybernetics},
  year = {2010},
  volume = {103},
  pages = {319--338},
  number = {4},
  month = oct,
  acmid = {1858543},
  address = {Secaucus, NJ, USA},
  bdsk-url-1 = {http://dx.doi.org/10.1007/s00422-010-0403-9},
  date-added = {2013-05-24 17:40:18 +0000},
  date-modified = {2013-05-24 17:40:18 +0000},
  doi = {10.1007/s00422-010-0403-9},
  issn = {0340-1200},
  issue_date = {October 2010},
  keywords = {Central pattern generators, Discrete movements, Dynamical systems,
	Force fields, Muscle synergies, Rhythmic movements},
  numpages = {20},
  owner = {ken},
  publisher = {Springer-Verlag New York, Inc.},
  timestamp = {2013.08.06},
  url = {http://dx.doi.org/10.1007/s00422-010-0403-9}
}

@ARTICLE{Degallier2011,
  author = {Degallier, S. and Righetti, L. and Gay, S. and Ijspeert, A.J.},
  title = {Toward simple control for complex, autonomous robotic applications:
	combining discrete and rhythmic motor primitives},
  journal = {Autonomous Robots},
  year = {2011},
  volume = {31},
  pages = {155--181},
  owner = {fwyffels},
  quality = {1},
  timestamp = {2013.02.18}
}

@ARTICLE{Delcomyn2009,
  author = {Delcomyn, F.},
  title = {Walking in Invertebrates},
  journal = {Encyclopedia of Neuroscience},
  year = {2009},
  pages = {479--484},
  owner = {fwyffels},
  quality = {1},
  timestamp = {2013.02.03}
}

@ARTICLE{Dietterich2000,
  author = {Dietterich, T. G.},
  title = {Hierarchical Reinforcement Learning with the MAXQ Value Function
	Decomposition},
  journal = {Journal of Artificial Intelligence},
  year = {2000},
  volume = {13},
  pages = {227-303},
  date-added = {2013-05-24 17:40:18 +0000},
  date-modified = {2013-05-24 17:40:18 +0000},
  owner = {ken},
  timestamp = {2013.08.06}
}

@INPROCEEDINGS{Diftler2011,
  author = {M. Diftler and et. al},
  title = {Robonaut 2: The First Humanoid Robot in Space},
  booktitle = {EEE Int'l Conf. on Robotics and Automation},
  year = {2011},
  address = {Shanghai},
  date-added = {2013-05-24 17:40:18 +0000},
  date-modified = {2013-05-24 17:40:18 +0000},
  owner = {ken},
  timestamp = {2013.08.06}
}

@ARTICLE{Djouadi2005,
  author = {Djouadi, By S and Pons, R Motro J C and Crosnier, B},
  title = {{Dynamics and control of tensegrity systems}},
  journal = {Journal of Aerospace Engineering},
  year = {2005},
  volume = {11},
  pages = {37--44},
  number = {2},
  publisher = {Springer}
}

@ARTICLE{Djouadi1998,
  author = {S. Djouadi and R. Motro and J.C. Pons and B. Crosnier},
  title = {Active control of tensegrity systems},
  journal = {Journal of Aerospace Engineering},
  year = {1998},
  volume = {11},
  pages = {37-44},
  date-added = {2013-05-24 17:40:18 +0000},
  date-modified = {2013-05-24 17:40:18 +0000},
  issue = {2},
  owner = {ken},
  timestamp = {2013.08.06}
}

@ARTICLE{rc_Duddington1981IEEESpectrum,
  author = {Doddington, G R and Schalk, T B},
  title = {{Computers - Speech Recognition - Turning Theory to Practice}},
  journal = {\{IEEE\} Spectr.},
  year = {1981},
  volume = {18},
  pages = {26--32},
  number = {9}
}

@TECHREPORT{Dubowsky2004,
  author = {Steven Dubowsky and Karl Iagnemma},
  title = {Microbots for Large-Scale Planetary Surface and Subsurface Exploration},
  institution = {Massachusetts Institute of Technology},
  year = {2004},
  date-added = {2013-05-24 17:40:18 +0000},
  date-modified = {2013-05-24 17:40:18 +0000},
  owner = {ken},
  timestamp = {2013.08.06}
}

@PHDTHESIS{Dutoit2009a,
  author = {Dutoit, X.},
  title = {Reservoir computing for intelligent mobile systems},
  school = {KU Leuven},
  year = {2009},
  owner = {fwyffels},
  timestamp = {2012.09.11}
}

@ARTICLE{Dutoit2009,
  author = {Dutoit, Xavier and Schrauwen, Benjamin and Van Campenhout, Jan and
	Stroobandt, Dirk and Van Brussel, Hendrik and Nuttin, Marnix},
  title = {Pruning and Regularization in Reservoir Computing},
  journal = {Neurocomputing},
  year = {2009},
  volume = {72},
  pages = {1534--1546},
  owner = {fwyffels},
  timestamp = {2009.01.05}
}

@BOOK{Dyson1979,
  title = {Disturbing The Universe},
  publisher = {Harper and Row},
  year = {1979},
  author = {Freeman Dyson},
  address = {New York, NY},
  date-added = {2013-05-24 17:40:18 +0000},
  date-modified = {2013-05-24 17:40:18 +0000},
  owner = {ken},
  timestamp = {2013.08.06}
}

@ARTICLE{excit_Eguia2002PhysRevE,
  author = {Eguia, M C and Dawson, S P and Mindlin, G B},
  title = {{Computing with excitable systems in a noisy environment}},
  journal = {Physical Review E},
  year = {2002},
  volume = {65},
  pages = {--},
  number = {4},
  abstract = {In this work we show that excitable units with biologically inspired
	couplings are capable of performing any logic operation in a noisy
	environment without synchronization.},
  keywords = {chemical implementation neural networks machines}
}

@ARTICLE{Elman1990,
  author = {Elman, J.L.},
  title = {Finding Structure in Time},
  journal = {Cognitive Science},
  year = {1990},
  volume = {14},
  pages = {179--211},
  owner = {fwyffels},
  timestamp = {2012.09.20}
}

@ARTICLE{Engel2004b,
  author = {Engel, Yaakov and Mannor, Shie and Meir, Ron},
  title = {{The Kernel recursive least squares algorithm}},
  year = {2004},
  abstract = {We present a non-linear kernel-based version of the Recursive Least
	Squares (RLS) algorithm. Our Kernel-RLS (KRLS) algorithm performs
	linear regression in the feature space induced by a Mercer kernel,
	and can therefore be used to recursively construct the minimum meansquared-
	error regressor. Sparsity of the solution is achieved by a sequential
	sparsication process that admits into the kernel representation a
	new input sample only if its feature space image cannot be suciently
	well approximated by combining the images of previously admitted
	samples. This sparsication procedure is crucial to the operation
	of KRLS, as it allows it to operate on-line, and by eectively regularizing
	its solutions. A theoretical analysis of the sparsication method
	reveals its close anity to kernel PCA, and a data-dependent loss
	bound is presented, quantifying the generalization performance of
	the KRLS algorithm. We demonstrate the performance and scaling properties
	of KRLS and compare it to a stateof- the-art Support Vector Regression
	algorithm, using both synthetic and real data. We additionally test
	KRLS on two signal processing problems in which the use of traditional
	least-squares methods is commonplace: Time series prediction and
	channel equalization.},
  bdsk-url-1 = {http://eprints.pascal-network.org/archive/00000909/},
  keywords = {computational,information theoretic learning with statistics,theory
	\& algorithms},
  url = {http://eprints.pascal-network.org/archive/00000909/}
}

@ARTICLE{Engel2004c,
  author = {Engel, Y and Mannor, S and Meir, R},
  title = {{The Kernel Recursive Least-Squares Algorithm}},
  journal = {IEEE Transactions on Signal Processing},
  year = {2004},
  volume = {52},
  pages = {2275--2285},
  number = {8},
  abstract = {We present a nonlinear version of the recursive least squares (RLS)
	algorithm. Our algorithm performs linear regression in a high-dimensional
	feature space induced by a Mercer kernel and can therefore be used
	to recursively construct minimum mean-squared-error solutions to
	nonlinear least-squares problems that are frequently encountered
	in signal processing applications. In order to regularize solutions
	and keep the complexity of the algorithm bounded, we use a sequential
	sparsification process that admits into the kernel representation
	a new input sample only if its feature space image cannot be sufficiently
	well approximated by combining the images of previously admitted
	samples. This sparsification procedure allows the algorithm to operate
	online, often in real time. We analyze the behavior of the algorithm,
	compare its scaling properties to those of support vector machines,
	and demonstrate its utility in solving two signal processing problems-time-series
	prediction and channel equalization.},
  bdsk-url-1 = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=1315946},
  bdsk-url-2 = {http://dx.doi.org/10.1109/TSP.2004.830985},
  doi = {10.1109/TSP.2004.830985},
  issn = {1053587X},
  publisher = {Citeseer},
  url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=1315946}
}

@MISC{Engine,
  author = {Open Dynamics Engine},
  title = {http://www.ode.org/},
  date-added = {2013-05-24 17:40:18 +0000},
  date-modified = {2013-05-24 17:40:18 +0000},
  owner = {ken},
  timestamp = {2013.08.06}
}

@INPROCEEDINGS{Enner2012,
  author = {Enner, F. and Rollinson, D. and Choset, H.},
  title = {{Simplified motion modeling for snake robots}},
  booktitle = {2012 IEEE International Conference on Robotics and Automation},
  year = {2012},
  pages = {4216--4221},
  month = may,
  publisher = {Ieee},
  bdsk-url-1 = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6225163},
  bdsk-url-2 = {http://dx.doi.org/10.1109/ICRA.2012.6225163},
  doi = {10.1109/ICRA.2012.6225163},
  file = {:windows/Documents and Settings/Brian/My Documents/Dropbox/Papers/Snake Robots/ICRA2012\_Enner.pdf:pdf},
  isbn = {978-1-4673-1405-3},
  owner = {ken},
  timestamp = {2013.08.06},
  url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6225163}
}

@ARTICLE{Estrada2006,
  author = {G. G. Estrada and et al.},
  title = {Numerical form-finding of tensegrity structures},
  journal = {International Journal of Solids and Structures},
  year = {2006},
  volume = {43},
  pages = {6855-6868},
  bdsk-url-1 = {http://dx.doi.org/10.1016/j.ijsolstr.2006.02.012},
  date-added = {2013-05-24 17:40:18 +0000},
  date-modified = {2013-05-24 17:40:18 +0000},
  doi = {10.1016/j.ijsolstr.2006.02.012},
  owner = {ken},
  timestamp = {2013.08.06}
}

@ARTICLE{Ferree1999,
  author = {Ferr\'{e}e, T C and Lockery, S R},
  title = {{Computational rules for chemotaxis in the nematode C. Elegans.}},
  journal = {Journal of Computational Neuroscience},
  year = {1999},
  volume = {6},
  pages = {263--277},
  number = {3},
  abstract = {We derive a linear neural network model of the chemotaxis control
	circuit in the nematode Caenorhabditis elegans and demonstrate that
	this model is capable of producing nematodelike chemotaxis. By expanding
	the analytic solution for the network output in time-derivatives
	of the network input, we extract simple computational rules that
	reveal how the model network controls chemotaxis. Based on these
	rules we find that optimized linear networks typically control chemotaxis
	by computing the first time-derivative of the chemical concentration
	and modulating the body turning rate in response to this derivative.
	We argue that this is consistent with behavioral studies and a plausible
	mechanism for at least one component of chemotaxis in real nematodes.},
  bdsk-url-1 = {http://www.ncbi.nlm.nih.gov/pubmed/10406137},
  institution = {Electrical Geodesics, Inc., Riverfront Research Park, Eugene, OR
	97403, USA.},
  pmid = {10406137},
  publisher = {Springer},
  url = {http://www.ncbi.nlm.nih.gov/pubmed/10406137}
}

@ARTICLE{Fest2004,
  author = {Fest, Etienne and Shea, Kristina and Smith, Ian F C and Asce, M},
  title = {{Active Tensegrity Structure}},
  journal = {Journal of Structural Engineering},
  year = {2004},
  volume = {130},
  pages = {1454--1465},
  number = {10},
  abstract = {Most active structures involve direct control of single parameters
	when there is a closed form relationship between the response required
	and the control parameter. Building on a previous study of an adjustable
	structure, this paper describes geometric active control of a reusable
	tensegrity structure that has been enlarged to five modules with
	improved connections and is equipped with actuators. Closely coupled
	strut and cable elements behave nonlinearly (geometrically) even
	for small movement of the 10 telescopic struts. The control criterion
	for maintaining the upper surface slope has no closed form relationship
	with strut movement. The behavior of the structure is studied under
	25 load cases. A newly developed stochastic search algorithm successfully
	identifies good control commands following computation times of up
	to 1 h. Sequential application of the commands through sets of partial
	commands helps to avoid exceeding limits during intermediate stages
	and adds robustness to the system. Reuse of a previously calculated
	command reduces the response time to less than 1 min. Feasible storage
	and reuse of such commands confirm the potential for improving performance
	during service.},
  doi = {10.1061/(ASCE)0733-9445(2004)130},
}

@ARTICLE{Fiers2012,
  author = {Fiers, Martin and {Van Vaerenbergh}, Thomas and Caluwaerts, Ken and
	{Vande Ginste}, Dries and Schrauwen, Benjamin and Dambre, Joni, and
	Bienstman, Peter},
  title = {{Time-domain and frequency-domain modeling of nonlinear optical components
	on circuit-level using a node-based approach}},
  journal = {Journal of the Optical Society of America B},
  year = {2012}
}

@ARTICLE{Fiete2006,
  author = {Fiete, Ila R and Seung, H Sebastian},
  title = {{Gradient learning in spiking neural networks by dynamic perturbation
	of conductances.}},
  journal = {Physical Review Letters},
  year = {2006},
  volume = {97},
  pages = {5},
  number = {4},
  abstract = {We present a method of estimating the gradient of an objective function
	with respect to the synaptic weights of a spiking neural network.
	The method works by measuring the fluctuations in the objective function
	in response to dynamic perturbation of the membrane conductances
	of the neurons. It is compatible with recurrent networks of conductance-based
	model neurons with dynamic synapses. The method can be interpreted
	as a biologically plausible synaptic learning rule, if the dynamic
	perturbations are generated by a special class of ``empiric'' synapses
	driven by random spike trains from an external source.},
  bdsk-url-1 = {http://arxiv.org/abs/q-bio/0601028},
  file = {:home/kcaluwae/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Fiete, Seung - 2006 - Gradient learning in spiking neural networks by dynamic perturbation of conductances.pdf:pdf},
  institution = {Kavli Institute for Theoretical Physics, University of California,
	Santa Barbara, California 93106, USA.},
  publisher = {APS},
  url = {http://arxiv.org/abs/q-bio/0601028}
}

@ARTICLE{Findley1996,
  author = {Findley, D. F. and Monsell, B. C. and Bell, W. R. and Otto, M. C.
	and Chen, B. C.},
  title = {New capabilities and methods of the X-12-ARIMA seasonal-adjustment
	program},
  journal = {Journal of Business and Economic Statistics},
  year = {1996},
  volume = {16},
  pages = {127--152},
  month = {2},
  owner = {fwyffels},
  timestamp = {2009.05.11}
}

@ARTICLE{Flash2005,
  author = {Flash, T. and Hochner, B.},
  title = {Motor primitives in vertebrates and invertebrates},
  journal = {Current opinion in neurobiology},
  year = {2005},
  volume = {15},
  pages = {660--666},
  owner = {fwyffels},
  quality = {1},
  timestamp = {2013.01.31}
}

@ARTICLE{Flemons2012,
  author = {Tom Flemons},
  title = {The Bones of Tensegrity},
  journal = {http://www.intensiondesigns.com/bones\_of\_tensegrity},
  year = {2012},
  date-added = {2013-05-24 17:40:18 +0000},
  date-modified = {2013-05-24 17:40:18 +0000},
  owner = {ken},
  timestamp = {2013.08.06}
}

@ARTICLE{Freund1997,
  author = {Freund, Yoav and Schapire, Robert E},
  title = {{A Decision-Theoretic Generalization of On-Line Learning and an Application
	to Boosting}},
  journal = {Journal of Computer and System Sciences},
  year = {1997},
  volume = {55},
  pages = {119--139},
  number = {1},
  abstract = {. We consider the problem of dynamically apportioning resources among
	a set of options in a worst-case on-line framework. The model we
	study can be interpreted as a broad, abstract extension of the well-studied
	on-line prediction model to a general decision-theoretic setting.
	We show that the multiplicative weight-update rule of Littlestone
	and Warmuth 10 can be adapted to this model yielding bounds that
	are slightly weaker in some cases, but applicable to a considerably
	more general class of ...},
  bdsk-url-1 = {http://linkinghub.elsevier.com/retrieve/pii/S002200009791504X},
  bdsk-url-2 = {http://dx.doi.org/10.1006/jcss.1997.1504},
  doi = {10.1006/jcss.1997.1504},
  issn = {00220000},
  publisher = {Springer},
  url = {http://linkinghub.elsevier.com/retrieve/pii/S002200009791504X}
}

@ARTICLE{Friston2010,
  author = {Friston, Karl},
  title = {{The free-energy principle: a unified brain theory?}},
  journal = {Nature Reviews Neuroscience},
  year = {2010},
  volume = {11},
  pages = {127--138},
  number = {2},
  abstract = {A free-energy principle has been proposed recently that accounts for
	action, perception and learning. This Review looks at some key brain
	theories in the biological (for example, neural Darwinism) and physical
	(for example, information theory and optimal control theory) sciences
	from the free-energy perspective. Crucially, one key theme runs through
	each of these theories - optimization. Furthermore, if we look closely
	at what is optimized, the same quantity keeps emerging, namely value
	(expected reward, expected utility) or its complement, surprise (prediction
	error, expected cost). This is the quantity that is optimized under
	the free-energy principle, which suggests that several global brain
	theories might be unified within a free-energy framework.},
  bdsk-url-1 = {http://www.ncbi.nlm.nih.gov/pubmed/20068583},
  file = {::},
  institution = {The Wellcome Trust Centre for Neuroimaging, University College London,
	Queen Square, London, WC1N 3BG, UK. k.friston@fil.ion.ucl.ac.uk},
  keywords = {animals,brain,brain physiology,cognition,cognition physiology,humans,learning,learning
	physiology,nerve net,nerve net physiology,perception,perception physiology,psychological
	theory},
  pmid = {20068583},
  publisher = {Nature Publishing Group},
  url = {http://www.ncbi.nlm.nih.gov/pubmed/20068583}
}

@ARTICLE{Friston2007,
  author = {Friston, Karl J and Stephan, Klaas E},
  title = {{Free-energy and the brain}},
  journal = {Synthese},
  year = {2007},
  volume = {159},
  pages = {417--458},
  number = {3},
  abstract = {If one formulates Helmholtz's ideas about perception in terms of modern-day
	theories one arrives at a model of perceptual inference and learning
	that can explain a remarkable range of neurobiological facts. Using
	constructs from statistical physics it can be shown that the problems
	of inferring what cause our sensory input and learning causal regularities
	in the sensorium can be resolved using exactly the same principles.
	Furthermore, inference and learning can proceed in a biologically
	plausible fashion. The ensuing scheme rests on Empirical Bayes and
	hierarchical models of how sensory information is generated. The
	use of hierarchical models enables the brain to construct prior expectations
	in a dynamic and context-sensitive fashion. This scheme provides
	a principled way to understand many aspects of the brain's organisation
	and responses.In this paper, we suggest that these perceptual processes
	are just one emergent property of systems that conform to a free-energy
	principle. The free-energy considered here represents a bound on
	the surprise inherent in any exchange with the environment, under
	expectations encoded by its state or configuration. A system can
	minimise free-energy by changing its configuration to change the
	way it samples the environment, or to change its expectations. These
	changes correspond to action and perception respectively and lead
	to an adaptive exchange with the environment that is characteristic
	of biological systems. This treatment implies that the system's state
	and structure encode an implicit and probabilistic model of the environment.
	We will look at models entailed by the brain and how minimisation
	of free-energy can explain its dynamics and structure.},
  bdsk-url-1 = {http://www.ncbi.nlm.nih.gov/pubmed/19325932},
  bdsk-url-2 = {http://dx.doi.org/10.1007/s11229-007-9237-y},
  doi = {10.1007/s11229-007-9237-y},
  issn = {00397857},
  pmid = {19325932},
  url = {http://www.ncbi.nlm.nih.gov/pubmed/19325932}
}

@ARTICLE{Fujiia2006,
  author = {Fujiia, M. and Yoshiia, S. and Kakazub, Y.},
  title = {Movement Control of Tensegrity Robot},
  journal = {Intelligent Autonomous Systems 9: IAS-9},
  year = {2006},
  volume = {9},
  pages = {290},
  date-added = {2013-05-24 17:40:18 +0000},
  date-modified = {2013-05-24 17:40:18 +0000},
  owner = {ken},
  publisher = {Ios PressInc},
  timestamp = {2013.08.06}
}

@ARTICLE{Fukuoka2009,
  author = {Fukuoka, Y. and Kimura, H.},
  title = {Dynamic locomotion of a biomorphic quadruped Tekken robot using various
	gaits: walk, trot, free-gait and bound},
  journal = {Applied Bionics and Biomechanics},
  year = {2009},
  volume = {6},
  pages = {1--9},
  owner = {fwyffels},
  quality = {1},
  timestamp = {2013.02.07}
}

@ARTICLE{Fukuoka2003,
  author = {Fukuoka, Y. and Kimura, H. and Cohen, A.H.},
  title = {Adaptive Dynamic Walking of a Quadruped Robot on Irregular Terrain
	Based on Biological Concepts},
  journal = {The International Journal of Robotics Research},
  year = {2003},
  volume = {22},
  pages = {187--202},
  owner = {fwyffels},
  timestamp = {2012.07.19}
}

@ARTICLE{Fuller1961,
  author = {Buckminster Fuller},
  title = {Tensegrity},
  journal = {Portfolio and Art News Annual},
  year = {1961},
  volume = {4},
  pages = {112-127},
  date-added = {2013-05-24 17:40:18 +0000},
  date-modified = {2013-05-24 17:40:18 +0000},
  owner = {ken},
  timestamp = {2013.08.06}
}

@BOOK{BuckminsterFuller1975,
  title = {Synergetics: Explorations in the Geometry of Thinking},
  publisher = {Scribner},
  year = {1975},
  author = {Fuller, R. B.},
  month = jan,
  isbn = {002541870X},
  keywords = {tensegrity},
  mendeley-tags = {tensegrity},
  type = {Book},
}

@ARTICLE{Funato2008a,
  author = {Funato, T. and Kurabayashi, D.},
  title = {Network Structure for Control of Coupled Multiple Nonlinear Oscillators},
  journal = {IEEE Transactions on Systems, Man, and Cybernetics Part B},
  year = {2008},
  volume = {38},
  pages = {675--681},
  owner = {fwyffels},
  timestamp = {2011.02.01}
}

@ARTICLE{Funato2008,
  author = {Funato, T. and Kurabayashi, D. and Nara, M. and Aonuma, H.},
  title = {Switching Mechanism of Sensor-Motor Coordination Through an Oscillator
	Network Model},
  journal = {IEEE Transactions on Systems, Man, and Cybernetics Part B},
  year = {2008},
  volume = {38},
  pages = {764--770},
  owner = {fwyffels},
  timestamp = {2011.02.01}
}

@ARTICLE{Futakata2008,
  author = {Futakata, Y and Iwasaki, T},
  title = {Formal analysis of resonance entrainment by central pattern generator},
  journal = {Journal of Mathematical Biology},
  year = {2008},
  volume = {57},
  pages = {183--207},
  number = {2},
  month = aug,
  note = {{PMID:} 18175118},
  bdsk-url-1 = {http://www.ncbi.nlm.nih.gov/pubmed/18175118},
  bdsk-url-2 = {http://dx.doi.org/10.1007/s00285-007-0151-1},
  date-added = {2013-05-24 17:40:18 +0000},
  date-modified = {2013-05-24 17:40:18 +0000},
  doi = {10.1007/s00285-007-0151-1},
  issn = {0303-6812},
  keywords = {Animals, Biological Clocks, Computer Simulation, Feedback, Locomotion,
	Models, Neurological, Neurons},
  owner = {ken},
  timestamp = {2013.08.06},
  url = {http://www.ncbi.nlm.nih.gov/pubmed/18175118}
}

@INPROCEEDINGS{Futakata2008a,
  author = {Futakata, Y. and Iwasaki, T.},
  title = {Entrainment of central pattern generators to natural oscillations
	of collocated mechanical systems},
  booktitle = {Proceedings of the 47th IEEE Conference on Decision and Control},
  year = {2008},
  pages = {5220--5225},
  publisher = {{IEEE}},
  bdsk-url-1 = {http://www.bibsonomy.org/bibtex/2a5f33098aa0eaa207a29a2a18a2f686d/dblp},
  bdsk-url-2 = {http://dx.doi.org/10.1109/CDC.2008.4739198},
  date-added = {2013-05-24 17:40:18 +0000},
  date-modified = {2013-05-24 17:40:18 +0000},
  doi = {10.1109/CDC.2008.4739198},
  isbn = {978-1-4244-3123-6},
  owner = {ken},
  timestamp = {2013.08.06},
  url = {http://www.bibsonomy.org/bibtex/2a5f33098aa0eaa207a29a2a18a2f686d/dblp}
}

@ARTICLE{Gabriel2011,
  author = {Gabriel, J.P. and Ausborn, J. and Ampatzis, K. and Mahmood, R. and
	Ekl\"of-Ljunggren, E. and Manira, A.E.},
  title = {Principles governing recruitment of motoneurons during swimming in
	zebrafish},
  journal = {Nature Neuroscience},
  year = {2011},
  volume = {14},
  pages = {93--99},
  owner = {fwyffels},
  quality = {1},
  timestamp = {2013.02.03}
}

@ARTICLE{Galicki1999,
  author = {Galicki, M. and Leistritz, L. and Witte, H.},
  title = {Learning continuous trajectories in recurrent neural networks with
	time-dependent weights},
  journal = {IEEE Transactions on Neural Networks},
  year = {1999},
  volume = {10},
  pages = {741--756},
  owner = {fwyffels},
  quality = {1},
  timestamp = {2013.02.08}
}

@ARTICLE{Gams2009,
  author = {Gams, A. and Ijspeert, A.J. and Schaal, S. and Lenarcic, J.},
  title = {On-line learning and modulation of periodic movements with nonlinear
	dynamical systems},
  journal = {Autonomous Robots},
  year = {2009},
  volume = {27},
  pages = {3--23},
  owner = {fwyffels},
  timestamp = {2011.02.01}
}

@ARTICLE{Ganguli02122008,
  author = {Ganguli, Surya and Huh, Dongsung and Sompolinsky, Haim},
  title = {Memory traces in dynamical systems},
  journal = {Proceedings of the National Academy of Sciences},
  year = {2008},
  volume = {105},
  pages = {18970-18975},
  number = {48},
  abstract = {To perform nontrivial, real-time computations on a sensory input stream,
	biological systems must retain a short-term memory trace of their
	recent inputs. It has been proposed that generic high-dimensional
	dynamical systems could retain a memory trace for past inputs in
	their current state. This raises important questions about the fundamental
	limits of such memory traces and the properties required of dynamical
	systems to achieve these limits. We address these issues by applying
	Fisher information theory to dynamical systems driven by time-dependent
	signals corrupted by noise. We introduce the Fisher Memory Curve
	(FMC) as a measure of the signal-to-noise ratio (SNR) embedded in
	the dynamical state relative to the input SNR. The integrated FMC
	indicates the total memory capacity. We apply this theory to linear
	neuronal networks and show that the capacity of networks with normal
	connectivity matrices is exactly 1 and that of any network of N neurons
	is, at most, N. A nonnormal network achieving this bound is subject
	to stringent design constraints: It must have a hidden feedforward
	architecture that superlinearly amplifies its input for a time of
	order N, and the input connectivity must optimally match this architecture.
	The memory capacity of networks subject to saturating nonlinearities
	is further limited, and cannot exceed . This limit can be realized
	by feedforward structures with divergent fan out that distributes
	the signal across neurons, thereby avoiding saturation. We illustrate
	the generality of the theory by showing that memory in fluid systems
	can be sustained by transient nonnormal amplification due to convective
	instability or the onset of turbulence.},
  bdsk-url-1 = {http://dx.doi.org/10.1073/pnas.0804451105},
  doi = {10.1073/pnas.0804451105}
}

@INPROCEEDINGS{Gay2013,
  author = {Gay, S\'ebastien and Santos-Victor, Jos\'e and Ijspeert, Auke},
  title = {Learning Robot Gait Stability using Neural Networks as Sensory Feedback
	Function for Central Pattern Generators},
  booktitle = {\textnormal{To appear in} IROS},
  year = {2013}
}

@INPROCEEDINGS{gay2010integration,
  author = {Gay, S{\'e}bastien and D{\'e}gallier, Sarah and Pattacini, Ugo and
	Ijspeert, Auke and Victor, Jos{\'e} Santos},
  title = {Integration of vision and central pattern generator based locomotion
	for path planning of a non-holonomic crawling humanoid robot},
  booktitle = {IROS},
  year = {2010},
  pages = {183--189},
  bdsk-url-1 = {http://dx.doi.org/10.1109/IROS.2010.5648788},
  doi = {10.1109/IROS.2010.5648788}
}

@ARTICLE{excit_Giudici1997PhysRevE,
  author = {Giudici, M and Green, C and Giacomelli, G and Nespolo, U and Tredicce,
	J R},
  title = {{Andronov bifurcation and excitability in semiconductor lasers with
	optical feedback}},
  journal = {Physical Review E},
  year = {1997},
  volume = {55},
  pages = {6414--6418},
  number = {6},
  abstract = {We experimentally investigate the dynamical behavior of a semiconductor
	laser with optical feedback. We show that noise plays an important
	role close to the instability threshold, while determinism controls
	the so-called coherence collapse regime. We identify the bifurcation
	which is at the origin of the low frequency fluctuations. It is the
	result of a collision between a stable fixed point and a saddle point
	(Andronov's bifurcation). We provide experimental proof that the
	laser with optical feedback behaves as an excitable medium.},
  keywords = {coherence collapse external-cavity instabilities i}
}

@ARTICLE{Gong2010,
  author = {Gong, D. and Yan, J. and Zuo, G.},
  title = {A review of gait optimization based on evolutionary computation},
  journal = {Applied Computational Intelligence and Soft Computing},
  year = {2010},
  volume = {2010},
  owner = {fwyffels},
  timestamp = {2011.05.16}
}

@ARTICLE{GraellsRovira2009,
  author = {Graells Rovira, A. and Mirats-Tur, J. M.},
  title = {{Control and simulation of a tensegrity-based mobile robot}},
  journal = {Robotics and Autonomous Systems},
  year = {2009},
  volume = {57},
  pages = {526-535},
  number = {5},
  month = may,
  bdsk-url-1 = {http://dx.doi.org/10.1016/j.robot.2008.10.010},
  doi = {10.1016/j.robot.2008.10.010},
  owner = {ken},
  timestamp = {2013.08.06}
}

@ARTICLE{Grillner2006,
  author = {Grillner, Sten},
  title = {Biological pattern generation: the cellular and computational logic
	of networks in motion},
  journal = {Neuron},
  year = {2006},
  volume = {52},
  pages = {751--766},
  owner = {fwyffels},
  timestamp = {2009.01.08}
}

@ARTICLE{Grillner1985,
  author = {Grillner, S.},
  title = {Neurobiological Bases of Rhythmic Motor Acts in Vertebrates},
  journal = {Science},
  year = {1985},
  volume = {228},
  pages = {143--149},
  owner = {fwyffels},
  timestamp = {2012.07.19}
}

@ARTICLE{Grillner2002,
  author = {Grillner, S. and Wallen, P.},
  title = {Cellular bases of a vertebrate locomotor system-steering, intersegmental
	and segmental co-ordination and sensory control},
  journal = {Brain research reviews},
  year = {2002},
  volume = {40},
  pages = {92--106},
  owner = {fwyffels},
  quality = {1},
  timestamp = {2013.01.31}
}

@ARTICLE{Grillner1985a,
  author = {Grillner, S. and Wallen, P.},
  title = {Central Pattern Generators for Locomotion, with Special Reference
	to Vertebrates},
  journal = {Annual Review of Neuroscience},
  year = {1985},
  volume = {8},
  pages = {233--261},
  owner = {fwyffels},
  quality = {1},
  timestamp = {2013.02.03}
}

@ARTICLE{Grillner1979,
  author = {Grillner, S and Zangger, P},
  title = {{On the central generation of locomotion in the low spinal cat.}},
  journal = {Experimental Brain Research},
  year = {1979},
  volume = {34},
  pages = {241--261},
  number = {2},
  abstract = {A central network of neurones in the spinal cord has been shown to
	produce a rhythmic motor output similar to locomotion after suppression
	of all afferent inflow. The experiments were performed mainly in
	acute spinal cats (th. 12), which had received DOPA i.v. and the
	monoamine oxidase inhibitor Nialamide. In some preparations all dorsal
	roots supplying the spinal cord were transected, in others phasic
	afferent activity was suppressed by curarization. The activity was
	recorded as neurograms from nerve filaments or as electromyograms.
	It is concluded that: 1. alternating activity between flexors and
	extensors of foot, ankel, knee, and hip of one limb can still occur
	2. the duration of the flexor discharges vary less with the cycle
	duration than the extensor discharges 3. different flexor muscles
	may retain individual patterns 4. the activity at different joints
	can be dissociated 5. there is at least one network for each limb.
	6. the coordination between the two hindlimbs can be alternating
	as in walking or be more closely spaced as in galloping 7. alternating
	activity in the ankle remains even when only segments L6, L7 and
	S1 are intact.},
  bdsk-url-1 = {http://www.springerlink.com/index/T165L22632048615.pdf},
  keywords = {cat deafferentation,locomotion central generator},
  pmid = {421750},
  publisher = {Springer},
  url = {http://www.springerlink.com/index/T165L22632048615.pdf}
}

@ARTICLE{Guest2010,
  author = {Guest, S D},
  title = {{The stiffness of tensegrity structures}},
  journal = {IMA Journal of Applied Mathematics},
  year = {2010},
  volume = {76},
  pages = {57--66},
  number = {1},
  bdsk-url-1 = {http://kth.diva-portal.org/smash/get/diva2:418858/FULLTEXT01},
  bdsk-url-2 = {http://dx.doi.org/10.1093/imamat/hxq065},
  doi = {10.1093/imamat/hxq065},
  issn = {02724960},
  url = {http://kth.diva-portal.org/smash/get/diva2:418858/FULLTEXT01}
}

@INPROCEEDINGS{Guestrin2002,
  author = {C. Guestrin and M. Lagoudakis and R. Parr},
  title = {Coordinated Reinforcement Learning},
  booktitle = {Proceedings of the 19th International Conference on Machine Learning},
  year = {2002},
  date-added = {2013-05-24 17:40:18 +0000},
  date-modified = {2013-05-24 17:40:18 +0000},
  owner = {ken},
  timestamp = {2013.08.06}
}

@INCOLLECTION{Hansen2006,
  author = {Hansen, Nikolaus},
  title = {{The CMA evolution strategy: A comparing review}},
  booktitle = {Towards a New Evolutionary Computation},
  publisher = {Springer},
  year = {2006},
  editor = {Lozano, J A and Larra\~{n}aga, P and Inza, I and Bengoetxea, E},
  volume = {102},
  number = {2006},
  series = {Studies in Fuzziness and Soft Computing},
  chapter = {4},
  pages = {75--102},
  abstract = {Derived from the concept of self-adaptation in evolution strategies,
	the CMA (Covariance Matrix Adaptation) adapts the covariance matrix
	of a multi-variate normal search distribution. The CMA was originally
	designed to perform well with small populations. In this review,
	the argument starts out with large population sizes, reflecting recent
	extensions of the CMA algorithm. Commonalities and differences to
	continuous Estimation of Distribution Algorithms are analyzed. The
	aspects of reliability of the estimation, overall step size control,
	and independence from the coordinate system (invariance) become particularly
	important in small populations sizes. Consequently, performing the
	adaptation task with small populations is more intricate.},
  bdsk-url-1 = {http://www.springerlink.com/content/v732u25412123j4g},
  bdsk-url-2 = {http://dx.doi.org/10.1007/3-540-32494-1%5C_4},
  doi = {10.1007/3-540-32494-1\_4},
  isbn = {9783540290063},
  issn = {14349922},
  url = {http://www.springerlink.com/content/v732u25412123j4g}
}

@ARTICLE{Hansen2003,
  author = {Hansen, N. and M\"uller, S.D and Koumoutsakos, P.},
  title = {Reducing the time complexity of the derandomized evolution strategy
	with covariance matrix adaptation (CMA-ES)},
  journal = {Evolutionary Computation},
  year = {2003},
  volume = {11},
  pages = {1--18},
  owner = {fwyffels},
  timestamp = {2012.12.03}
}

@ARTICLE{Hansen2001,
  author = {Hansen, N. and Ostermeier, A.},
  title = {Completely Derandomized Self-Adaptation in Evolution Strategies},
  journal = {Evolutionary Computation},
  year = {2001},
  volume = {9},
  pages = {159--195},
  owner = {fwyffels},
  timestamp = {2011.02.15}
}

@ARTICLE{Hartline1979,
  author = {Hartline, D.K.},
  title = {Pattern generation in the lobster (Panulirus) stomatogastric ganglion.
	II. Pyloric network simulation},
  journal = {Biological Cybernetics},
  year = {1979},
  volume = {33},
  pages = {223--236},
  owner = {fwyffels},
  quality = {1},
  timestamp = {2013.02.03}
}

@ARTICLE{Hartline1979a,
  author = {Hartline, D.K. and Gassie, D.V.},
  title = {Pattern generation in the lobster (Panulirus ) stomatogastric ganglion.
	I. Pyloric neuron kinetics and synaptic interactions},
  journal = {Biological Cybernetics},
  year = {1979},
  volume = {33},
  pages = {209--222},
  owner = {fwyffels},
  quality = {1},
  timestamp = {2013.02.03}
}

@BOOK{Hastie2001,
  title = {{The Elements of Statistical Learning}},
  publisher = {Springer},
  year = {2001},
  author = {Hastie, T and Tibshirani, R and Friedman, J},
  volume = {27},
  number = {2},
  pages = {764},
  series = {Springer Series in Statistics},
  abstract = {During the past decade there has been an explosion in computation
	and information technology. With it have come vast amounts of data
	in a variety of fields such as medicine, biology, finance, and marketing.
	The challenge of understanding these data has led to the development
	of new tools in the field of statistics, and spawned new areas such
	as data mining, machine learning, and bioinformatics. Many of these
	tools have common underpinnings but are often expressed with different
	terminology. This book describes the important ideas in these areas
	in a common conceptual framework. While the approach is statistical,
	the emphasis is on concepts rather than mathematics. Many examples
	are given, with a liberal use of color graphics. It should be a valuable
	resource for statisticians and anyone interested in data mining in
	science or industry. The book's coverage is broad, from supervised
	learning (prediction) to unsupervised learning. The many topics include
	neural networks, support vector machines, classification trees and
	boosting--the first comprehensive treatment of this topic in any
	book. This major new edition features many topics not covered in
	the original, including graphical models, random forests, ensemble
	methods, least angle regression path algorithms for the lasso, non-negative
	matrix factorization, and spectral clustering. There is also a chapter
	on methods for wide data (p bigger than n), including multiple testing
	and false discovery rates. Trevor Hastie, Robert Tibshirani, and
	Jerome Friedman are professors of statistics at Stanford University.
	They are prominent researchers in this area: Hastie and Tibshirani
	developed generalized additive models and wrote a popular book of
	that title. Hastie co-developed much of the statistical modeling
	software and environment in R/S-PLUS and invented principal curves
	and surfaces. Tibshirani proposed the lasso and is co-author of the
	very successful An Introduction to the Bootstrap. Friedman is the
	co-inventor of many data-mining tools including CART, MARS, projection
	pursuit and gradient boosting.},
  bdsk-url-1 = {http://www.springerlink.com/index/D7X7KX6772HQ2135.pdf},
  bdsk-url-2 = {http://dx.doi.org/10.1111/j.1467-985X.2004.298%5C_11.x},
  booktitle = {The Mathematical Intelligencer},
  doi = {10.1111/j.1467-985X.2004.298\_11.x},
  file = {::},
  isbn = {0387952845},
  issn = {03436993},
  url = {http://www.springerlink.com/index/D7X7KX6772HQ2135.pdf}
}

@ARTICLE{Hauser,
  author = {Hauser, Helmut and Ijspeert, Auke J and F\"{u}chslin, Rudolf M. and
	Pfeifer, Rolf and Maass, Wolfgang},
  title = {{Towards a theoretical foundation for morphological computation with
	compliant bodies.}},
  journal = {Biological Cybernetics},
  year = {2012},
  volume = {105},
  pages = {355--370},
  month = jan,
  abstract = {The control of compliant robots is, due to their often nonlinear and
	complex dynamics, inherently difficult. The vision of morphological
	computation proposes to view these aspects not only as problems,
	but rather also as parts of the solution. Non-rigid body parts are
	not seen anymore as imperfect realizations of rigid body parts, but
	rather as potential computational resources. The applicability of
	this vision has already been demonstrated for a variety of complex
	robot control problems. Nevertheless, a theoretical basis for understanding
	the capabilities and limitations of morphological computation has
	been missing so far. We present a model for morphological computation
	with compliant bodies, where a precise mathematical characterization
	of the potential computational contribution of a complex physical
	body is feasible. The theory suggests that complexity and nonlinearity,
	typically unwanted properties of robots, are desired features in
	order to provide computational power. We demonstrate that simple
	generic models of physical bodies, based on mass-spring systems,
	can be used to implement complex nonlinear operators. By adding a
	simple readout (which is static and linear) to the morphology such
	devices are able to emulate complex mappings of input to output streams
	in continuous time. Hence, by outsourcing parts of the computation
	to the physical body, the difficult problem of learning to control
	a complex body, could be reduced to a simple and perspicuous learning
	task, which can not get stuck in local minima of an error function.},
  bdsk-url-1 = {http://www.ncbi.nlm.nih.gov/pubmed/22290137},
  bdsk-url-2 = {http://dx.doi.org/10.1007/s00422-012-0471-0},
  doi = {10.1007/s00422-012-0471-0},
  issn = {1432-0770},
  pmid = {22290137},
  url = {http://www.ncbi.nlm.nih.gov/pubmed/22290137}
}

@ARTICLE{HauserH.;PfeiferR.;IjspeertA.J.&Maass,
  author = {Hauser, Helmut and Ijspeert, Auke J. and F\"{u}chslin, Rudolf M.
	and Pfeifer, Rolf and Maass, Wolfgang},
  title = {{The role of feedback in morphological computation with compliant
	bodies}},
  journal = {submitted to Biological Cybernetics},
  year = {2012}
}

@BOOK{Hebb1949,
  title = {{The organization of behavior}},
  publisher = {Wiley},
  year = {1949},
  author = {Hebb, D O},
  booktitle = {The organization of behavior}
}

@ARTICLE{pc_Hecht2007LaserFocusWorld,
  author = {Hecht, J},
  title = {{Photonic crystals make nanocavity lasers}},
  journal = {Laser Focus World},
  year = {2007},
  volume = {43},
  pages = {95--98},
  number = {10}
}

@ARTICLE{Herkenhoff2003,
  author = {Herkenhoff, K. E. and Squyres, S. W. and Bell, J. F. and Maki, J.
	N. and Arneson, H. M. and Bertelsen, P. and Brown, D. I. and Collins,
	S. A. and Dingizian, A. and Elliott, S. T. and Goetz, W. and Hagerott,
	E. C. and Hayes, A. G. and Johnson, M. J. and Kirk, R. L. and McLennan,
	S. and Morris, R. V. and Scherr, L. M. and Schwochert, M. A. and
	Shiraishi, L. R. and Smith, G. H. and Soderblom, L. A. and Sohl-Dickstein,
	J. N. and Wadsworth, M. V.},
  title = {Athena Microscopic Imager investigation},
  journal = {Journal of Geophysical Research: Planets},
  year = {2003},
  volume = {108},
  number = {E12},
  bdsk-url-1 = {http://dx.doi.org/10.1029/2003JE002076},
  doi = {10.1029/2003JE002076},
  issn = {2156-2202},
  keywords = {Mars Exploration Rover, Microscopic Imager, Athena, NASA, camera},
  owner = {ken},
  timestamp = {2013.08.06},
  url = {http://dx.doi.org/10.1029/2003JE002076}
}

@ARTICLE{Hermans2012,
  author = {Hermans, M. and Schrauwen, B.},
  title = {Recurrent Kernel Machines: Computation with Infinite Echo State Networks},
  journal = {Neural Computation},
  year = {2012},
  volume = {24},
  pages = {104--133},
  owner = {fwyffels},
  timestamp = {2012.09.25}
}

@ARTICLE{Hermans2011,
  author = {Hermans, Michiel and Schrauwen, Benjamin},
  title = {{Recurrent Kernel Machines: Computing with Infinite Echo State Networks.}},
  journal = {Neural Computation},
  year = {2011},
  volume = {24},
  pages = {104--133},
  number = {1},
  abstract = {Echo state networks (ESNs) are large, random recurrent neural networks
	with a single trained linear readout layer. Despite the untrained
	nature of the recurrent weights, they are capable of performing universal
	computations on temporal input data, which makes them interesting
	for both theoretical research and practical applications. The key
	to their success lies in the fact that the network computes a broad
	set of nonlinear, spatiotemporal mappings of the input data, on which
	linear regression or classification can easily be performed. One
	could consider the reservoir as a spatiotemporal kernel, in which
	the mapping to a high-dimensional space is computed explicitly. In
	this letter, we build on this idea and extend the concept of ESNs
	to infinite-sized recurrent neural networks, which can be considered
	recursive kernels that subsequently can be used to create recursive
	support vector machines. We present the theoretical framework, provide
	several practical examples of recursive kernels, and apply them to
	typical temporal tasks.},
  bdsk-url-1 = {http://www.mitpressjournals.org/doi/abs/10.1162/NECO%5C_a%5C_00200},
  bdsk-url-2 = {http://dx.doi.org/10.1162/NECO%5C_a%5C_00200},
  doi = {10.1162/NECO\_a\_00200},
  issn = {1530888X},
  pmid = {21851278},
  publisher = {MIT Press 55 Hayward Street, Cambridge, MA 02142-1315 email: journals-info@mit.edu},
  url = {http://www.mitpressjournals.org/doi/abs/10.1162/NECO\_a\_00200}
}

@INPROCEEDINGS{Hermans2010,
  author = {Hermans, M. and Schrauwen, B.},
  title = {Memory in reservoirs for high dimensional input},
  booktitle = {Proceedings of the International Joint Conference on Neural Networks},
  year = {2010},
  owner = {fwyffels},
  timestamp = {2012.10.02}
}

@ARTICLE{Hermans2010a,
  author = {Hermans, M. and Schrauwen, B.},
  title = {Memory in linear recurrent neural networks in continuous time},
  journal = {Neural Networks},
  year = {2010},
  volume = {23},
  pages = {341--355},
  owner = {fwyffels},
  quality = {1},
  timestamp = {2012.12.27}
}

@ARTICLE{rc_Hermans2010NeuralNetworks,
  author = {Hermans, M and Schrauwen, B},
  title = {{Memory in linear recurrent neural networks in continuous time}},
  journal = {Neural Networks},
  year = {2010},
  volume = {23},
  pages = {341--355},
  number = {3},
  abstract = {Reservoir Computing is a novel technique which employs recurrent neural
	networks while circumventing difficult training algorithms. A very
	recent trend in Reservoir Computing is the use of real physical dynamical
	systems as implementation platforms, rather than the customary digital
	emulations. Physical systems operate in continuous time, creating
	a fundamental difference with the classic discrete time definitions
	of Reservoir Computing. The specific goal of this paper is to study
	the memory properties of such systems, where we will limit ourselves
	to linear dynamics. We develop an analytical model which allows the
	calculation of the memory function for continuous time linear dynamical
	systems, which can be considered as networks of linear leaky integrator
	neurons. We then use this model to research memory properties for
	different types of reservoir. We start with random connection matrices
	with a shifted eigenvalue spectrum, which perform very poorly. Next,
	we transform two specific reservoir types, which are known to give
	good performance in discrete time, to the continuous time domain.
	Reservoirs based on uniform spreading of connection matrix eigenvalues
	on the unit disk in discrete time give much better memory properties
	than reservoirs with random connection matrices, where reservoirs
	based on orthogonal connection matrices in discrete time are very
	robust against noise and their memory properties can be tuned. The
	overall results found in this work yield important insights into
	how to design networks for continuous time. (C) 2009 Elsevier Ltd.
	All rights reserved.},
  keywords = {reservoir computing continuous time memory functio}
}

@ARTICLE{Hill1938,
  author = {Hill, A V},
  title = {{The heat of shortening and the dynamic constants of muscle}},
  journal = {Proceedings of the Royal Society of London Series B Biological Sciences},
  year = {1938},
  volume = {126},
  pages = {136--195},
  number = {843},
  bdsk-url-1 = {http://www.jstor.org/stable/82135},
  issn = {00804649},
  publisher = {JSTOR},
  url = {http://www.jstor.org/stable/82135}
}

@ARTICLE{nn_Hill2002IEEETransNeurNet,
  author = {Hill, M and Edward, E and Frietman, E and de Waardt, H and Dorren,
	H J S and Khoe, G},
  title = {{All Fiber-Optic Neural Network Using Coupled SOA Based Ring Lasers}},
  journal = {\{IEEE\} Trans. Neural Netw.},
  year = {2002},
  volume = {13},
  number = {6}
}

@ARTICLE{Hoerl1970,
  author = {Hoerl, A. and Kennard, R.},
  title = {Ridge Regression: Biased Estimation for Nonorthogonal Problems},
  journal = {Technometrics},
  year = {1970},
  volume = {12},
  pages = {55-67},
  owner = {fwyffels},
  timestamp = {2012.01.27}
}

@INPROCEEDINGS{Hoerzer2010,
  author = {Hoerzer, G. M.},
  title = {{Reward-modulated Hebbian Learning is able to induce Coherent Patters
	of Activity and Simple Memory Functions in initially Chaotic Recurrent
	Neural Networks}},
  booktitle = {Workshop on Cognitive and Neural Models for Automated Processing
	of Speech and Text (CONAS)},
  year = {2010},
  address = {Ghent}
}

@ARTICLE{Hoerzer2012,
  author = {Hoerzer, G. M. and Legenstein, R. and Maass, W.},
  title = {{Emergence of complex computational structures from chaotic neural
	networks through reward-modulated Hebbian learning}},
  journal = {Submitted},
  year = {2012}
}

@INPROCEEDINGS{HoffmannAMAM2011,
  author = {Hoffmann, M. and Schmidt, N. and Nakajima, K. and Iida, F. and Pfeifer,
	R.},
  title = {Perception, motor learning, and speed adaptation exploiting body
	dynamics: case studies in a quadruped robot},
  booktitle = {Proceedings of the International Symposium on Adaptive Motion in
	Animals and Machines},
  year = {2011},
  owner = {Matej},
  timestamp = {2011.09.02}
}

@CONFERENCE{Hoffmann2012,
  author = {Hoffmann, M. and Schmidt, N. and Pfeifer, R. and Engel, A. and Maye,
	A.},
  title = {Using sensorimotor contingencies for terrain discrimination and adaptive
	walking behavior in the quadruped robot Puppy},
  booktitle = {From animals to animats 12},
  year = {2012},
  owner = {fwyffels},
  quality = {1},
  timestamp = {2013.01.08}
}

@ARTICLE{Hogan1985,
  author = {Hogan, N.},
  title = {{Impedance control: An approach to manipulation: Part I - Theory}},
  journal = {Transactions of the ASME},
  year = {1985},
  volume = {107},
  pages = {1--7},
  number = {March 1985},
  file = {:windows/Documents and Settings/Brian/My Documents/Dropbox/Papers/Tensiegrities/Impedance Control/hogan1985\_theory.pdf:pdf},
  owner = {ken},
  timestamp = {2013.08.06}
}

@ARTICLE{Hogan1985a,
  author = {Hogan, N.},
  title = {{Impedance control: An approach to manipulation: Part III - Applications}},
  journal = {Transactions of the ASME},
  year = {1985},
  volume = {107},
  pages = {17--24},
  number = {March 1985},
  file = {:windows/Documents and Settings/Brian/My Documents/Dropbox/Papers/Tensiegrities/Impedance Control/hogan1985\_applications.pdf:pdf},
  owner = {ken},
  timestamp = {2013.08.06}
}

@ARTICLE{Hogan1985b,
  author = {Hogan, N.},
  title = {{Impedance control: An approach to manipulation: Part II - Implementation}},
  journal = {Transactions of the ASME},
  year = {1985},
  volume = {107},
  pages = {8--16},
  number = {March 1985},
  bdsk-url-1 = {http://ieeexplore.ieee.org/xpls/abs%5C_all.jsp?arnumber=4788393},
  file = {:windows/Documents and Settings/Brian/My Documents/Dropbox/Papers/Tensiegrities/Impedance Control/hogan1985\_implementation.pdf:pdf},
  owner = {ken},
  timestamp = {2013.08.06},
  url = {http://ieeexplore.ieee.org/xpls/abs\_all.jsp?arnumber=4788393}
}

@INPROCEEDINGS{HolmesParker2011,
  author = {C. HolmesParker and A. Agogino},
  title = {Agent-Based Resource Allocation in Dynamically Formed CubeSat Constellations},
  booktitle = {Proceedings of the Tenth International Joint Conference on Autonomous
	Agents and Multi-Agent Systems},
  year = {2011},
  address = {Taipei, Taiwan},
  month = {May},
  date-added = {2013-05-24 17:40:18 +0000},
  date-modified = {2013-05-24 17:40:18 +0000},
  owner = {ken},
  timestamp = {2013.08.06}
}

@ARTICLE{Holzmann2010,
  author = {Holzmann, G. and Hauser, H.},
  title = {Echo State Networks with Filter Neurons and a Delay\&Sum Readout},
  journal = {Neural Networks},
  year = {2010},
  volume = {23},
  pages = {244--256},
  owner = {fwyffels},
  timestamp = {2012.11.26}
}

@ARTICLE{Huang2006,
  author = {Huang, G.B. and Zhu, Q.Y. and Siew, C.H.},
  title = {Extreme learning machines: Theory and applications},
  journal = {Neurocomputing},
  year = {2006},
  volume = {70},
  pages = {489--501},
  owner = {fwyffels},
  timestamp = {2012.12.03}
}

@ARTICLE{Huxley1957,
  author = {Huxley, A F},
  title = {{Muscle structure and theories of contraction}},
  journal = {Progress in Biophysics Molecular Biology},
  year = {1957},
  volume = {7},
  pages = {255--\&},
  bdsk-url-1 = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve%5C&db=PubMed%5C&dopt=Citation%5C&list%5C_uids=13485191},
  issn = {00964174},
  url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list\_uids=13485191}
}

@PHDTHESIS{Iida2005,
  author = {Iida, F.},
  title = {Cheap Design and Behavioral Diversity for Autonomous Adaptive Robots},
  school = {University of Zurich},
  year = {2005},
  owner = {fwyffels},
  quality = {1},
  timestamp = {2013.01.08}
}

@ARTICLE{Iida2009,
  author = {Iida, F. and Minekawa, Y. and Rummel, J. and Seyfarth, A.},
  title = {Toward a human-like biped robot with compliant legs},
  journal = {Robotics and Autonomous Systems},
  year = {2009},
  volume = {57},
  pages = {137--144},
  owner = {fwyffels},
  timestamp = {2011.12.05}
}

@ARTICLE{Iida2006,
  author = {Iida, F and Pfeifer, R},
  title = {{Sensing through body dynamics}},
  journal = {Robotics and Autonomous Systems},
  year = {2006},
  volume = {54},
  pages = {631--640},
  number = {8},
  abstract = {It has been shown that sensory morphology and sensory-motor coordination
	enhance the capabilities of sensing in robotic systems. The tasks
	of categorization and category learning, for example, can be significantly
	simplified by exploiting the morphological constraints, sensory-motor
	couplings and the interaction with the environment. This paper argues
	that, in the context of sensory-motor control, it is essential to
	consider body dynamics derived from morphological properties and
	the interaction with the environment in order to gain additional
	insight into the underlying mechanisms of sensory-motor coordination,
	and more generally the nature of perception. A locomotion model of
	a four-legged robot is used for the case studies in both simulation
	and real world. The locomotion model demonstrates how attractor states
	derived from body dynamics influence the sensory information, which
	can then be used for the recognition of stable behavioral patterns
	and of physical properties in the environment. A comprehensive analysis
	of behavior and sensory information leads to a deeper understanding
	of the underlying mechanisms by which body dynamics can be exploited
	for category learning of autonomous robotic systems.},
  bdsk-url-1 = {http://linkinghub.elsevier.com/retrieve/pii/S0921889006000625},
  bdsk-url-2 = {http://dx.doi.org/10.1016/j.robot.2006.03.005},
  doi = {10.1016/j.robot.2006.03.005},
  issn = {09218890},
  keywords = {categorization,legged locomotion,object recognition,passive dynamics},
  url = {http://linkinghub.elsevier.com/retrieve/pii/S0921889006000625}
}

@ARTICLE{Ijspeert2007,
  author = {Ijspeert, A.J. and Crespi, A. and Ryczko, D. and Cabelguen, J.-M.},
  title = {From swimming to walking with a salamander robot driven by a spinal
	cord model},
  journal = {Science},
  year = {2007},
  volume = {315},
  pages = {1416--1420},
  number = {5817},
  month = {October},
  owner = {fwyffels},
  timestamp = {2009.01.05}
}

@INPROCEEDINGS{Ijspeert2002,
  author = {Ijspeert, A.J. and Nakanishi, J. and Schaal, S.},
  title = {Learning Attractor Landscapes for Learning Motor Primitives},
  booktitle = {Advances in Neural Information Processing Systems 15},
  year = {2002},
  pages = {1547--1554},
  owner = {fwyffels},
  quality = {1},
  timestamp = {2013.02.07}
}

@ARTICLE{Ijspeert2013,
  author = {Ijspeert, A.J. and Nanaishi, J. and Hoffmann, H. and Pastor, P. and
	Schaal, S.},
  title = {Dynamical Movement Primitives: Learning Attractor Models for Motor
	Behaviors},
  journal = {Neural Computation},
  year = {2013},
  volume = {25},
  pages = {328--373},
  owner = {fwyffels},
  quality = {1},
  timestamp = {2013.01.31}
}

@ARTICLE{Ijspeert2008,
  author = {Ijspeert, A. J.},
  title = {{Central pattern generators for locomotion control in animals and
	robots: a review.}},
  journal = {Neural Networks},
  year = {2008},
  volume = {21},
  pages = {642--53},
  number = {4},
  month = may,
  abstract = {The problem of controlling locomotion is an area in which neuroscience
	and robotics can fruitfully interact. In this article, I will review
	research carried out on locomotor central pattern generators (CPGs),
	i.e. neural circuits capable of producing coordinated patterns of
	high-dimensional rhythmic output signals while receiving only simple,
	low-dimensional, input signals. The review will first cover neurobiological
	observations concerning locomotor CPGs and their numerical modelling,
	with a special focus on vertebrates. It will then cover how CPG models
	implemented as neural networks or systems of coupled oscillators
	can be used in robotics for controlling the locomotion of articulated
	robots. The review also presents how robots can be used as scientific
	tools to obtain a better understanding of the functioning of biological
	CPGs. Finally, various methods for designing CPGs to control specific
	modes of locomotion will be briefly reviewed. In this process, I
	will discuss different types of CPG models, the pros and cons of
	using CPGs with robots, and the pros and cons of using robots as
	scientific tools. Open research topics both in biology and in robotics
	will also be discussed.},
  doi = {10.1016/j.neunet.2008.03.014},
  issn = {0893-6080},
  keywords = {Animals,Biological Clocks,Biological Clocks: physiology,Central Nervous
	System,Central Nervous System: physiology,Humans,Locomotion,Locomotion:
	physiology,Movement,Movement: physiology,Nerve Net,Nerve Net: physiology,Neural
	Networks (Computer),Neural Pathways,Neural Pathways: physiology,Neurosciences,Neurosciences:
	methods,Neurosciences: trends,Robotics,Robotics: methods,Robotics:
	trends},
  owner = {ken},
  pmid = {18555958},
  timestamp = {2013.08.06},
}

@ARTICLE{Ijspeert2007a,
  author = {Ijspeert, A. J. and Crespi, A. and Ryczko, D. and Cabelguen, J. M.},
  title = {{From swimming to walking with a salamander robot driven by a spinal
	cord model.}},
  journal = {Science (New York, N.Y.)},
  year = {2007},
  volume = {315},
  pages = {1416--1420},
  number = {5817},
  month = mar,
  abstract = {The transition from aquatic to terrestrial locomotion was a key development
	in vertebrate evolution. We present a spinal cord model and its implementation
	in an amphibious salamander robot that demonstrates how a primitive
	neural circuit for swimming can be extended by phylogenetically more
	recent limb oscillatory centers to explain the ability of salamanders
	to switch between swimming and walking. The model suggests neural
	mechanisms for modulation of velocity, direction, and type of gait
	that are relevant for all tetrapods. It predicts that limb oscillatory
	centers have lower intrinsic frequencies than body oscillatory centers,
	and we present biological data supporting this.},
  bdsk-url-1 = {http://www.ncbi.nlm.nih.gov/pubmed/17347441},
  bdsk-url-2 = {http://dx.doi.org/10.1126/science.1138353},
  doi = {10.1126/science.1138353},
  issn = {1095-9203},
  keywords = {Biological, Neurological,Animals,Biological Evolution,Biomechanics,Brain
	Stem,Brain Stem: physiology,Electric Stimulation,Extremities,Extremities:
	innervation,Extremities: physiology,Gait,Locomotion,Mathematics,Models,Motor
	Neurons,Motor Neurons: physiology,Nerve Net,Nerve Net: physiology,Pleurodeles,Pleurodeles:
	anatomy \& histology,Pleurodeles: physiology,Robotics,Spinal Cord,Spinal
	Cord: physiology,Swimming,Walking},
  owner = {ken},
  pmid = {17347441},
  timestamp = {2013.08.06},
  url = {http://www.ncbi.nlm.nih.gov/pubmed/17347441}
}

@ARTICLE{Ijspeert1999,
  author = {Ijspeert, Auke J and Kodjabachian, J Rome},
  title = {{Evolution and development of a central pattern generator for the
	swimming of a Lamprey}},
  journal = {Artificial Life},
  year = {1999},
  volume = {5},
  pages = {247--269},
  number = {3},
  bdsk-url-1 = {http://birg2.epfl.ch/publications/fulltext/ijspeert99.pdf},
  url = {http://birg2.epfl.ch/publications/fulltext/ijspeert99.pdf}
}

@ARTICLE{Ingber2003,
  author = {Ingber, D E},
  title = {{Tensegrity I. Cell structure and hierarchical systems biology}},
  journal = {Journal of Cell Science},
  year = {2003},
  volume = {116},
  pages = {1157--1173},
  number = {7},
  abstract = {In 1993, a Commentary in this journal described how a simple mechanical
	model of cell structure based on tensegrity architecture can help
	to explain how cell shape, movement and cytoskeletal mechanics are
	controlled, as well as how cells sense and respond to mechanical
	forces (J. Cell Sci. 104, 613-627). The cellular tensegrity model
	can now be revisited and placed in context of new advances in our
	understanding of cell structure, biological networks and mechanoregulation
	that have been made over the past decade. Recent work provides strong
	evidence to support the use of tensegrity by cells, and mathematical
	formulations of the model predict many aspects of cell behavior.
	In addition, development of the tensegrity theory and its translation
	into mathematical terms are beginning to allow us to define the relationship
	between mechanics and biochemistry at the molecular level and to
	attack the larger problem of biological complexity. Part I of this
	two-part article covers the evidence for cellular tensegrity at the
	molecular level and describes how this building system may provide
	a structural basis for the hierarchical organization of living systems-from
	molecule to organism. Part II, which focuses on how these structural
	networks influence information processing networks, appears in the
	next issue.},
  bdsk-url-1 = {http://jcs.biologists.org/cgi/doi/10.1242/jcs.00359},
  bdsk-url-2 = {http://dx.doi.org/10.1242/jcs.00359},
  doi = {10.1242/jcs.00359},
  isbn = {0021953300219533},
  issn = {00219533},
  keywords = {cell mechanics,cell shape,cytoskeleton,integrins,intermediate filaments,microfilaments,microtubules},
  pmid = {12615960},
  publisher = {Co Biol},
  url = {http://jcs.biologists.org/cgi/doi/10.1242/jcs.00359}
}

@ARTICLE{Ingber2003a,
  author = {Ingber, D E},
  title = {{Tensegrity II. How structural networks influence cellular information
	processing networks}},
  journal = {Journal of Cell Science},
  year = {2003},
  volume = {116},
  pages = {1397--1408},
  number = {8},
  abstract = {The major challenge in biology today is biocomplexity: the need to
	explain how cell and tissue behaviors emerge from collective interactions
	within complex molecular networks. Part I of this two-part article,
	described a mechanical model of cell structure based on tensegrity
	architecture that explains how the mechanical behavior of the cell
	emerges from physical interactions among the different molecular
	filament systems that form the cytoskeleton. Recent work shows that
	the cytoskeleton also orients much of the cell's metabolic and signal
	transduction machinery and that mechanical distortion of cells and
	the cytoskeleton through cell surface integrin receptors can profoundly
	affect cell behavior. In particular, gradual variations in this single
	physical control parameter (cell shape distortion) can switch cells
	between distinct gene programs (e.g. growth, differentiation and
	apoptosis), and this process can be viewed as a biological phase
	transition. Part II of this article covers how combined use of tensegrity
	and solid-state mechanochemistry by cells may mediate mechanotransduction
	and facilitate integration of chemical and physical signals that
	are responsible for control of cell behavior. In addition, it examines
	how cell structural networks affect gene and protein signaling networks
	to produce characteristic phenotypes and cell fate transitions during
	tissue development.},
  archiveprefix = {arXiv},
  arxivid = {0012796190},
  bdsk-url-1 = {http://jcs.biologists.org/cgi/doi/10.1242/jcs.00360},
  bdsk-url-2 = {http://dx.doi.org/10.1242/jcs.00360},
  doi = {10.1242/jcs.00360},
  eprint = {0012796190},
  file = {:home/kcaluwae/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Ingber - 2003 - Tensegrity II. How structural networks influence cellular information processing networks.pdf:pdf},
  institution = {Harvard Med Sch, Boston, MA},
  isbn = {0021953300219533},
  issn = {00219533},
  keywords = {biocomplexity,bioinformatics,integrins,mechanobiology,mechanotransduction},
  pmid = {12640025},
  publisher = {Co Biol},
  url = {http://jcs.biologists.org/cgi/doi/10.1242/jcs.00360}
}

@ARTICLE{Ingber1998,
  author = {Ingber, Donald E},
  title = {The Architecture of Life},
  journal = {Scientific American},
  year = {1998},
  volume = {278},
  pages = {48--57},
  number = {1},
  bdsk-url-1 = {http://www.nature.com/doifinder/10.1038/scientificamerican0198-48},
  bdsk-url-2 = {http://dx.doi.org/10.1038/scientificamerican0198-48},
  doi = {10.1038/scientificamerican0198-48},
  issn = {00368733},
  pmid = {644},
  url = {http://www.nature.com/doifinder/10.1038/scientificamerican0198-48}
}

@ARTICLE{Ingber1997,
  author = {Ingber, Donald E},
  title = {{Tensegrity: The architectural basis of cellular mechanotransduction.}},
  journal = {Annual Review of Physiology},
  year = {1997},
  volume = {59},
  pages = {575--599},
  number = {1},
  abstract = {Physical forces of gravity, hemodynamic stresses, and movement play
	a critical role in tissue development. Yet, little is known about
	how cells convert these mechanical signals into a chemical response.
	This review attempts to place the potential molecular mediators of
	mechanotransduction (e.g. stretch-sensitive ion channels, signaling
	molecules, cytoskeleton, integrins) within the context of the structural
	complexity of living cells. The model presented relies on recent
	experimental findings, which suggests that cells use tensegrity architecture
	for their organization. Tensegrity predicts that cells are hard-wired
	to respond immediately to mechanical stresses transmitted over cell
	surface receptors that physically couple the cytoskeleton to extracellular
	matrix (e.g. integrins) or to other cells (cadherins, selectins,
	CAMs). Many signal transducing molecules that are activated by cell
	binding to growth factors and extracellular matrix associate with
	cytoskeletal scaffolds within focal adhesion complexes. Mechanical
	signals, therefore, may be integrated with other environmental signals
	and transduced into a biochemical response through force-dependent
	changes in scaffold geometry or molecular mechanics. Tensegrity also
	provides a mechanism to focus mechanical energy on molecular transducers
	and to orchestrate and tune the cellular response.},
  bdsk-url-1 = {http://www.ncbi.nlm.nih.gov/pubmed/9074778},
  institution = {Department of Pathology, Children's Hospital, Boston, Massachusetts,
	USA.},
  keywords = {nasa discipline cell biology,non nasa center},
  pmid = {9074778},
  publisher = {Annual Reviews 4139 El Camino Way, P.O. Box 10139, Palo Alto, CA
	94303-0139, USA},
  url = {http://www.ncbi.nlm.nih.gov/pubmed/9074778}
}

@ARTICLE{Ingber1993,
  author = {Donald E. Ingber},
  title = {Cellular tensegrity: defining new rules of biologic design that govern
	the cytoskeleton},
  journal = {Journal of Cell Science},
  year = {1993},
  volume = {104},
  pages = {613-627},
  date-added = {2013-05-24 17:40:18 +0000},
  date-modified = {2013-05-24 17:40:18 +0000},
  owner = {ken},
  timestamp = {2013.08.06}
}

@ARTICLE{Ioannidis2007,
  author = {Ioannidis, John P A},
  title = {{Why Most Published Research Findings Are False: Author's Reply to
	Goodman and Greenland}},
  journal = {PLoS Medicine},
  year = {2007},
  volume = {4},
  pages = {2},
  number = {6},
  abstract = {There is increasing concern that most current published research findings
	are false. The probability that a research claim is true may depend
	on study power and bias, the number of other studies on the same
	question, and, importantly, the ratio of true to no relationships
	among the relationships probed in each scientific field. In this
	framework, a research finding is less likely to be true when the
	studies conducted in a field are smaller; when effect sizes are smaller;
	when there is a greater number and lesser preselection of tested
	relationships; where there is greater flexibility in designs, definitions,
	outcomes, and analytical modes; when there is greater financial and
	other interest and prejudice; and when more teams are involved in
	a scientific field in chase of statistical significance. Simulations
	show that for most study designs and settings, it is more likely
	for a research claim to be false than true. Moreover, for many current
	scientific fields, claimed research findings may often be simply
	accurate measures of the prevailing bias. In this essay, I discuss
	the implications of these problems for the conduct and interpretation
	of research.},
  bdsk-url-1 = {http://medicine.plosjournals.org/perlserv/?request=get-document%5C&doi=10.1371/journal.pmed.0020124},
  file = {:home/kcaluwae/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Ioannidis - 2007 - Why Most Published Research Findings Are False Author's Reply to Goodman and Greenland.pdf:pdf},
  institution = {TU Dresden},
  publisher = {Public Library of Science},
  url = {http://medicine.plosjournals.org/perlserv/?request=get-document\&doi=10.1371/journal.pmed.0020124}
}

@INPROCEEDINGS{Iscen2013,
  author = {Iscen, A. and Agogino, A. and SunSpiral, V. and Tumer, K.},
  title = {Learning to Control Complex Tensegrity Robots},
  booktitle = {International Conference on Autonomous Agents and Multiagent Systems (AAMAS)},
  year = {2013},
  owner = {ken},
  timestamp = {2013.08.06}
}

@INPROCEEDINGS{Iscen2013a,
  author = {Iscen, Atil and Agogino, Adrian and SunSpiral, Vytas and Tumer, Kagan},
  title = {Learning to control complex tensegrity robots},
  booktitle = {International Conference on Autonomous Agents and Multiagent Systems (AAMAS)},
  year = {2013},
  pages = {1193--1194},
  acmid = {2485138},
  bdsk-url-1 = {http://dl.acm.org/citation.cfm?id=2484920.2485138},
  date-added = {2013-06-04 18:35:40 +0000},
  date-modified = {2013-06-04 18:46:49 +0000},
  keywords = {multiagent systems, robotics, tensegrity},
  numpages = {2},
  owner = {ken},
  timestamp = {2013.08.06},
  url = {http://dl.acm.org/citation.cfm?id=2484920.2485138}
}

@INPROCEEDINGS{Iscen2013b,
  author = {Iscen, Atil and Agogino, Adrian and SunSpiral, Vytas and Tumer, Kagan},
  title = {Controlling Tensegrity Robots through Evolution},
  booktitle = {GECCO},
  pages={1293--1300},
  year = {2013},
  bdsk-url-1 = {http://dx.doi.org/10.1145/2463372.2463525},
  date-added = {2013-06-04 18:37:34 +0000},
  date-modified = {2013-06-04 18:46:49 +0000},
  doi = {10.1145/2463372.2463525},
  owner = {ken},
  timestamp = {2013.08.06}
}

@INPROCEEDINGS{Iscen2013c,
  author = {Iscen, Atil and Agogino, Adrian and SunSpiral, Vytas and Tumer, Kagan},
  title = {Robust Distributed Control of Rolling Tensegrity Robot},
  booktitle = {The Autonomous Robots and Multirobot Systems ({ARMS}) workshop at
	{AAMAS} 2013},
  year = {2013},
  bdsk-url-1 = {http://dl.acm.org/citation.cfm?id=2484920.2485138},
  date-added = {2013-06-04 18:43:17 +0000},
  date-modified = {2013-06-04 18:48:37 +0000},
  owner = {ken},
  timestamp = {2013.08.06}
}

@INPROCEEDINGS{iscenaamas2013,
  author = {Iscen, Atil and Agogino, Adrian and SunSpiral, Vytas and Tumer, Kagan},
  title = {Learning to Control Complex Tensegrity Robots},
  booktitle = {AAMAS},
  year = {2013}
}

@TECHREPORT{Jaeger2010,
  author = {Jaeger, H.},
  title = {Reservoir Self-Control for Achieving Invariance Slow Input Distortions},
  institution = {Jacobs University},
  year = {2010},
  owner = {fwyffels},
  timestamp = {2012.01.27}
}

@CONFERENCE{Jaeger2003,
  author = {Jaeger, H.},
  title = {Adaptive nonlinear system identification with echo state networks},
  booktitle = {Advances in Neural Information Processing Systems},
  year = {2003},
  owner = {fwyffels},
  timestamp = {2012.12.03}
}

@TECHREPORT{Jaeger2002,
  author = {Jaeger, H.},
  title = {A tutorial on training recurrent neural networks, covering BPPT,
	RTRL, EKF and the "echo state network" approach},
  institution = {International University Bremen},
  year = {2002},
  owner = {fwyffels},
  timestamp = {2010.10.28}
}

@TECHREPORT{Jaeger2002a,
  author = {Jaeger, H.},
  title = {Short term memory in Echo State Networks},
  institution = {Fraunhofer Institute for Autonomous Intelligent Systems},
  year = {2002},
  number = {GMD Report 152},
  owner = {fwyffels},
  timestamp = {2010.11.03}
}

@TECHREPORT{rc_Jaeger2002TechRep159,
  author = {Jaeger, H},
  title = {{Tutorial on training recurrent neural networks, covering \{BPTT\},
	\{RTRL\}, \{EKF\} and the {\^O}{\o}Ωecho state network" approach}},
  institution = {German National Research Center for Information Technology},
  year = {2002},
  number = {GMD Report 159}
}

@TECHREPORT{Jaeger2001a,
  author = {Jaeger, H.},
  title = {The ``echo state'' approach to analysing and training recurrent neural
	networks},
  institution = {German National Research Center for Information Technology},
  year = {2001},
  number = {GMD Report 148},
  owner = {fwyffels},
  timestamp = {2008.04.02}
}

@TECHREPORT{rc_Jaeger2001TechRep148,
  author = {Jaeger, H},
  title = {{The {\^O}{\o}Ωecho state{\^O}{\o}Ω approach to analysing and training
	recurrent neural networks}},
  institution = {German National Research Center for Information Technology},
  year = {2001},
  number = {GMD report 148}
}

@ARTICLE{Jaeger2000a,
  author = {Jaeger, H},
  title = {{Observable operator models for discrete stochastic time series.}},
  journal = {Neural Computation},
  year = {2000},
  volume = {12},
  pages = {1371--1398},
  number = {6},
  abstract = {A widely used class of models for stochastic systems is hidden Markov
	models. Systems that can be modeled by hidden Markov models are a
	proper subclass of linearly dependent processes, a class of stochastic
	systems known from mathematical investigations carried out over the
	past four decades. This article provides a novel, simple characterization
	of linearly dependent processes, called observable operator models.
	The mathematical properties of observable operator models lead to
	a constructive learning algorithm for the identification of linearly
	dependent processes. The core of the algorithm has a time complexity
	of O(N + nm3), where N is the size of training data, n is the number
	of distinguishable outcomes of observations, and m is model state-space
	dimension.},
  bdsk-url-1 = {http://www.ncbi.nlm.nih.gov/pubmed/10935718},
  institution = {German National Research Center for Information Technology, Institute
	for Intelligent Autonomous Systems, Sankt Augustin.},
  pmid = {10935718},
  url = {http://www.ncbi.nlm.nih.gov/pubmed/10935718}
}

@ARTICLE{Jaeger2004,
  author = {Jaeger, Herbert and Haas, Harald},
  title = {{Harnessing nonlinearity: Predicting chaotic systems and saving energy
	in wireless communication.}},
  journal = {Science},
  year = {2004},
  volume = {304},
  pages = {78--80},
  number = {5667},
  abstract = {We present a method for learning nonlinear systems, echo state networks
	(ESNs). ESNs employ artificial recurrent neural networks in a way
	that has recently been proposed independently as a learning mechanism
	in biological brains. The learning method is computationally efficient
	and easy to use. On a benchmark task of predicting a chaotic time
	series, accuracy is improved by a factor of 2400 over previous techniques.
	The potential for engineering applications is illustrated by equalizing
	a communication channel, where the signal error rate is improved
	by two orders of magnitude.},
  bdsk-url-1 = {http://www.ncbi.nlm.nih.gov/pubmed/15064413},
  bdsk-url-2 = {http://dx.doi.org/10.1126/science.1091277},
  doi = {10.1126/science.1091277},
  file = {:home/kcaluwae/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Jaeger, Haas - 2004 - Harnessing nonlinearity predicting chaotic systems and saving energy in wireless communication.pdf:pdf},
  institution = {International University Bremen, Bremen D-28759, Germany. h.jaeger@iu-bremen.de},
  pmid = {15064413},
  publisher = {American Association for the Advancement of Science},
  url = {http://www.ncbi.nlm.nih.gov/pubmed/15064413}
}

@ARTICLE{Jaeger2007,
  author = {Jaeger, H. and Luko{\v s}evi{\v c}ius, M. and Popovici, D. and Siewert,
	U.},
  title = {Optimization and applications of echo state networks with leaky-integrator
	neurons},
  journal = {Neural Networks},
  year = {2007},
  volume = {20},
  pages = {335--352},
  owner = {fwyffels},
  timestamp = {2012.12.04}
}

@TECHREPORT{Jaeger2011,
  author = {Jaeger, H. and Steil, J.J. and Reinhart, R.F.},
  title = {Technical report on describing the archetype architectures},
  institution = {EU - FP7},
  year = {2011},
  owner = {fwyffels},
  quality = {1},
  timestamp = {2013.02.18}
}

@INPROCEEDINGS{Jalalvand2012,
  author = {Jalalvand, A. and Triefenbach, F. and Martens, J-P.},
  title = {Continuous Digit Recognition in Noise: Reservoirs can do an excellent
	job!},
  booktitle = {Interspeech},
  year = {2012},
  owner = {fwyffels},
  timestamp = {2012.08.07}
}

@ARTICLE{Janczewski2006,
  author = {Janczewski, W.A. and Feldman, J.L.},
  title = {Distinct rhythm generators for inspiration and expiration in the
	juvenile rat},
  journal = {The Journal of Physiology},
  year = {2006},
  volume = {570},
  pages = {407--420},
  owner = {fwyffels},
  quality = {1},
  timestamp = {2013.02.03}
}

@ARTICLE{pc_John1987PhysRevLetters,
  author = {John, S},
  title = {{Strong Localization of Photons in Certain Disordered Dielectric
	Superlattices}},
  journal = {Physical Review Letters},
  year = {1987},
  volume = {58},
  pages = {2486--2489},
  number = {23}
}

@BOOK{Jolliffe2005,
  title = {Principal Component Analysis. Encyclopedia of Statistics in Behavioral
	Science.},
  year = {2005},
  author = {Jolliffe, I.},
  owner = {fwyffels},
  quality = {1},
  timestamp = {2013.01.23}
}

@ARTICLE{Jontes1995,
  author = {Jontes, J D},
  title = {{Theories of muscle contraction.}},
  journal = {Journal of Structural Biology},
  year = {1995},
  volume = {115},
  pages = {119--143},
  number = {2},
  abstract = {A survey of the mainstream theories in the modern study of the mechanism
	of muscle contraction is made, with particular emphasis placed on
	the experimental results which most influenced the progression of
	ideas. Starting with early elastic and viscoelastic theories of muscle
	contraction, a chronological organization is used to present, in
	detail, the results leading up to the swinging crossbridge model.
	A brief review is made of the experimental results modifying the
	original crossbridge model such as transient-state mechanics, in
	vitro kinetics, and kinetic measurements performed on demembranated
	muscle fibers. Following a brief synopsis of three of the more prevalent
	alternative models, a summary of the more relevant structural studies
	is presented. Finally, recent results pertaining to the mechanism
	of muscle contraction are presented and their promise for the future
	is discussed.},
  institution = {Department of Cell Biology, Scripps Research Institute, La Jolla,
	California 92037, USA.},
  pmid = {7577233}
}

@ARTICLE{Joshi2005,
  author = {Joshi, Prashant and Maass, Wolfgang},
  title = {Movement generation with circuits of spiking neurons},
  journal = {Neural Computation},
  year = {2005},
  volume = {17},
  pages = {1715--1738},
  owner = {fwyffels},
  timestamp = {2009.01.15}
}

@ARTICLE{Juan2008,
  author = {Sergi Hernàndez Juan and Josep M. Mirats Tur},
  title = {Tensegrity frameworks: Static analysis review},
  journal = {Mechanism and Machine Theory },
  year = {2008},
  volume = {43},
  pages = {859 - 881},
  number = {7},
  __markedentry = {[ken:6]},
  doi = {10.1016/j.mechmachtheory.2007.06.010},
  keywords = {Tensegrity frameworks},
  owner = {ken},
  timestamp = {2013.08.16},
}

@INPROCEEDINGS{Julier1997a,
  author = {Julier, Simon J and Uhlmann, Jeffrey K},
  title = {{A new extension of the Kalman filter to nonlinear systems}},
  booktitle = {Int Symp AerospaceDefense Sensing Simul and Controls},
  year = {1997},
  volume = {3},
  pages = {182--193},
  publisher = {Spie},
  abstract = {The Kalman Filter(KF) is one of the most widely used methods for tracking
	and estimation due to its simplicity, optimality, tractability and
	robustness. However, the application of the KF to nonlinear systems
	can be diffcult. The most common approach is to use the Extended
	Kalman Filter (EKF) which simply linearises all nonlinear models
	so that the traditional linear Kalman Filter can be applied. Although
	the EKF (in its many forms) is a widely used filtering strategy,
	over thirty years of experience with it has led to a general consensus
	within the tracking and control community that it is diffcult to
	implement, diffcult to tune, and only reliable for systems which
	are almost linear on the time scale of the update intervals. In this
	paper a new linear estimator is developed and demonstrated. Using
	the principle that a set of discretely sampled points can be used
	to parameterise mean and covariance, the estimator yields performance
	equivalent to the KF for linear systems yet generalises elegantly
	to nonlinear systems without the linearisation steps required by
	the EKF. We show analytically that the expected performance of the
	new approach is superior to that of the EKF and, in fact, is directly
	comparable to that of the second order Gauss Filter. The method is
	not restricted to assuming that the distributions of noise sources
	are Gaussian. We argue that the ease of implementation and more accurate
	estimation features of the new lter recommend its use over the EKF
	in virtually all applications.},
  bdsk-url-1 = {http://link.aip.org/link/?PSI/3068/182/1%5C&Agg=doi},
  bdsk-url-2 = {http://dx.doi.org/10.1117/12.280797},
  doi = {10.1117/12.280797},
  issn = {0277786X},
  url = {http://link.aip.org/link/?PSI/3068/182/1\&Agg=doi}
}

@ARTICLE{Kaelbling1996,
  author = {L.P. Kaelbling and M.L. Littman and Andrew Moore},
  title = {Reinforcement Learning: A Survey},
  journal = {Journal of Artificial Intelligence Research},
  year = {1996},
  volume = {4},
  pages = {237-285},
  date-added = {2013-05-24 17:40:18 +0000},
  date-modified = {2013-05-24 17:40:18 +0000},
  owner = {ken},
  timestamp = {2013.08.06}
}

@BOOK{Kailath2000,
  title = {{Linear estimation}},
  publisher = {Prentice Hall},
  year = {2000},
  author = {Kailath, Thomas and Sayed, Ali H and Hassibi, Babak},
  number = {3},
  pages = {854},
  series = {Information and System Sciences},
  bdsk-url-1 = {http://cdsweb.cern.ch/record/1248418},
  booktitle = {book},
  isbn = {0130224642},
  url = {http://cdsweb.cern.ch/record/1248418}
}

@ARTICLE{Kanso2009,
  author = {Kanso, Eva and Newton, Paul K},
  title = {{Passive locomotion via normal-mode coupling in a submerged spring-mass
	system}},
  journal = {Journal of Fluid Mechanics},
  year = {2009},
  volume = {641},
  pages = {205},
  bdsk-url-1 = {http://www.journals.cambridge.org/abstract%5C_S0022112009992357},
  bdsk-url-2 = {http://dx.doi.org/10.1017/S0022112009992357},
  doi = {10.1017/S0022112009992357},
  issn = {00221120},
  keywords = {low dimensional models,nonlinear dynamical systems},
  url = {http://www.journals.cambridge.org/abstract\_S0022112009992357}
}

@INCOLLECTION{schurbook,
  author = {Kaszkurewicz, E. and Bhaya, A.},
  title = {{M}atrix {D}iagonal and {D-Stability}},
  booktitle = {Matrix Diagonal Stability in Systems and Computation},
  publisher = {Birkh√{\S}user Boston},
  year = {2000},
  pages = {25-89},
  isbn = {978-1-4612-7105-5},
  language = {English}
}

@ARTICLE{Kim2004,
  author = {Kim, K I and Franz, Matthias and Sch\"{o}lkopf, Bernhard},
  title = {{Kernel Hebbian Algorithm for single-frame super-resolution}},
  journal = {Computer},
  year = {2004},
  pages = {135--149},
  number = {May},
  abstract = {This paper presents a method for single-frame image super-resolution
	using an unsupervised learning technique. The required prior knowledge
	about the high-resolution images is obtained from Kernel Principal
	Component Analysis (KPCA). The original form of KPCA, however, can
	be only applied to strongly restricted image classes due to the limited
	number of training examples that can be processed. We therefore propose
	a new iterative method for performing KPCA, the em Kernel Hebbian
	Algorithm. By kernelizing the Generalized Hebbian Algorithm, one
	can iteratively estimate the Kernel Principal Components with only
	linear order memory complexity. The resulting super-resolution algorithm
	shows a comparable performance to the existing supervised methods
	on images containing faces and natural scenes.},
  bdsk-url-1 = {http://eprints.pascal-network.org/archive/00000376/},
  file = {:home/kcaluwae/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Kim, Franz, Sch\"{o}lkopf - 2004 - Kernel Hebbian Algorithm for single-frame super-resolution.pdf:pdf},
  keywords = {computational,information theoretic learning with statistics,machine
	vision},
  publisher = {Citeseer},
  url = {http://eprints.pascal-network.org/archive/00000376/}
}

@ARTICLE{Kimura2007,
  author = {Kimura, H. and Fukuoka, Y. and Cohen, A.},
  title = {Adaptive Dynamic Walking of a Quadruped Robot on Natural Ground Based
	on Biological Concepts},
  journal = {Journal of Robotics Research},
  year = {2007},
  volume = {26},
  pages = {475--490},
  owner = {fwyffels},
  quality = {1},
  timestamp = {2013.02.07}
}

@INPROCEEDINGS{Klimke2004,
  author = {H. Klimke and S. Stephan},
  title = {The making of a tensegrity tower},
  booktitle = {IASS Symposium},
  year = {2004},
  address = {Montpellier},
  date-added = {2013-05-24 17:40:18 +0000},
  date-modified = {2013-05-24 17:40:18 +0000},
  owner = {ken},
  timestamp = {2013.08.06}
}

@ARTICLE{Knudson2011,
  author = {M. Knudson and K. Tumer},
  title = {Adaptive navigation for autonomous robots},
  journal = {Robotics and Autonomous Systems},
  year = {2011},
  volume = {In Press},
  date-added = {2013-05-24 17:40:18 +0000},
  date-modified = {2013-05-24 17:40:18 +0000},
  owner = {ken},
  timestamp = {2013.08.06}
}

@CONFERENCE{Knudson2010,
  author = {M. Knudson and K. Tumer},
  title = {Coevolution of heterogeneous multi-robot teams},
  booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference
	(GECCO)},
  year = {2010},
  date-added = {2013-05-24 17:40:18 +0000},
  date-modified = {2013-05-24 17:40:18 +0000},
  owner = {ken},
  timestamp = {2010.10.22}
}

@ARTICLE{Kober2009,
  author = {Kober, Jens and Peters, Jan},
  title = {{Learning motor primitives for robotics}},
  journal = {Proceedings of the IEEE International Conference on Robotics and
	Automation (2009)},
  year = {2009},
  pages = {2112--2118},
  abstract = {The acquisition and self-improvement of novel motor skills is among
	the most important problems in robotics. Motor primitives offer one
	of the most promising frameworks for the application of machine learning
	techniques in this context. Employing an improved form of the dynamic
	systems motor primitives originally introduced by Ijspeert et al.
	2, we show how both discrete and rhythmic tasks can be learned using
	a concerted approach of both imitation and reinforcement learning.
	For doing so, we present both learning algorithms and representations
	targeted for the practical application in robotics. Furthermore,
	we show that it is possible to include a start-up phase in rhythmic
	primitives. We show that two new motor skills, i.e., Ball-in-a-Cup
	and Ball-Paddling, can be learned on a real Barrett WAM robot arm
	at a pace similar to human learning while achieving a significantly
	more reliable final performance.},
  bdsk-url-1 = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5152577},
  bdsk-url-2 = {http://dx.doi.org/10.1109/ROBOT.2009.5152577},
  doi = {10.1109/ROBOT.2009.5152577},
  isbn = {9781424427888},
  issn = {10504729},
  publisher = {Ieee},
  url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5152577}
}

@INPROCEEDINGS{Koizumi2012b,
  author = {Koizumi, Y. and Shibata, M. and Hirai, S.},
  title = {Rolling tensegrity driven by pneumatic soft actuators},
  booktitle = {International Conference on Robotics and Automoation (ICRA)},
  year = {2012},
  pages = {1988--1993},
  bdsk-url-1 = {http://dx.doi.org/10.1109/ICRA.2012.6224834},
  date-added = {2013-05-24 17:40:18 +0000},
  date-modified = {2013-05-24 17:40:18 +0000},
  doi = {10.1109/ICRA.2012.6224834},
  owner = {ken},
  timestamp = {2013.08.06}
}

@ARTICLE{KorkmazJune2011,
  author = {Sinan Korkmaz and et al.},
  title = {Determining Control Strategies for Damage Tolerance of an Active
	Tensegrity Structure},
  journal = {Engineering Structures},
  year = {June 2011},
  volume = {33},
  pages = {1930-1939},
  date-added = {2013-05-24 17:40:18 +0000},
  date-modified = {2013-05-24 17:40:18 +0000},
  issue = {6},
  owner = {ken},
  timestamp = {2013.08.06}
}

@ARTICLE{Krasny2004,
  author = {Krasny, D. and Orin, D.},
  title = {Generating High-Speed Dynamic Running Gaits in a Quadruped Robot
	Using an Evolutionary Search},
  journal = {IEEE Transactions on Systems, Man, and Cybernetics Part B},
  year = {2004},
  volume = {34},
  pages = {1685--1696},
  owner = {fwyffels},
  timestamp = {2011.02.01}
}

@INPROCEEDINGS{Krause2010,
  author = {Krause, A.F. and Durr, V. and Blasing, B. and Schack, T.},
  title = {Evolutionary Optimization of Echo State Networks: multiple motor
	pattern learning},
  booktitle = {6th International Workshop on Artificial Neural Networks and Intelligent
	Information Processing},
  year = {2010},
  owner = {fwyffels},
  timestamp = {2012.07.19}
}

@ARTICLE{pc_Kuramochi2005PhysRevB,
  author = {Kuramochi, E and Notomi, M and Hughes, S and Shinya, A and Watanabe,
	T and Ramunno, L},
  title = {{Disorder-induced scattering loss of line-defect waveguides in photonic
	crystal slabs}},
  journal = {Physical Review B},
  year = {2005},
  volume = {72},
  pages = {--},
  number = {16},
  abstract = {Detailed propagation loss spectrum measurements for line-defect waveguides
	in silicon photonic crystal slabs are presented, which show record
	low loss values (5 dB/cm) and complicated frequency dependence. We
	quantitatively analyze the origin of the loss spectrum shape using
	a photon Green function theory and obtain a very good agreement,
	thus providing an explanation of the complex physical mechanisms
	responsible for the observed propagation loss. In particular, we
	demonstrate the influence of out-plane, backward, intermode, and
	in-plane scattering processes on the observed loss spectra, induced
	by the structural disorder that occurs during fabrication, and highlight
	the importance of backward and intermode scattering in these waveguides.}
}

@ARTICLE{pc_Kuramochi2006ApplPhysLett,
  author = {Kuramochi, E and Notomi, M and Mitsugi, S and Shinya, A and Tanabe,
	T and Watanabe, T},
  title = {{Ultrahigh-Q photonic crystal nanocavities realized by the local
	width modulation of a line defect}},
  journal = {Applied Physics Letters},
  year = {2006},
  volume = {88},
  pages = {--},
  number = {4},
  abstract = {We propose an ultrahigh quality factor (Q) photonic crystal slab nanocavity
	created by the local width modulation of a line defect. We show numerically
	that this nanocavity has an intrinsic Q value of up to 7x10(7). Transmission
	measurements for fabricated Si photonic-crystal-slab nanocavities
	directly coupled to input/output waveguides have exhibited a loaded
	Q value of similar to 800 000. These theoretical and experimental
	Q values are very high for photonic crystal nanocavities. In addition,
	we demonstrate that simply shifting two holes away from a line defect
	is sufficient to achieve an ultrahigh Q value both theoretically
	and experimentally. (c) 2006 American Institute of Physics.},
  keywords = {wave-guides slabs resonators mode}
}

@INPROCEEDINGS{Laub2003,
  author = {B. Laub and E. Venkatapathy},
  title = {Thermal Protection System Technology and Facility Needs for Demanding
	Future Planetary Missions},
  booktitle = {International Workshop on Planetary Probe Atmospheric Entry and Descent
	Trajectory Analysis and Science},
  year = {2003},
  pages = {268-278},
  month = {Oct},
  location = {Lisbon, Portugal},
  owner = {ken},
  timestamp = {2013.08.06}
}

@ARTICLE{Legenstein2010,
  author = {Legenstein, Robert and Chase, Steven M and Schwartz, Andrew B and
	Maass, Wolfgang},
  title = {{A reward-modulated Hebbian learning rule can explain experimentally
	observed network reorganization in a brain control task.}},
  journal = {Journal of Neuroscience},
  year = {2010},
  volume = {30},
  pages = {8400--8410},
  number = {25},
  abstract = {It has recently been shown in a brain-computer interface experiment
	that motor cortical neurons change their tuning properties selectively
	to compensate for errors induced by displaced decoding parameters.
	In particular, it was shown that the 3D tuning curves of neurons
	whose decoding parameters were re-assigned changed more than those
	of neurons whose decoding parameters had not been re-assigned. In
	this article, we propose a simple learning rule that can reproduce
	this effect. Our learning rule uses Hebbian weight updates driven
	by a global reward signal and neuronal noise. In contrast to most
	previously proposed learning rules, this approach does not require
	extrinsic information to separate noise from signal. The learning
	rule is able to optimize the performance of a

license_type: permissive

branch_name: refs/heads/master

extension: bib