Neuroscience-Inspired Dynamic Architectures

Neuroscience-inspired computational elements and architectures are one of the most popular ideas for replacing the von Neumann architecture. In this work, we propose a neuroscience-inspired dynamic architecture (NIDA) and discuss a method for automatically designing NIDA networks to accomplish tasks. We discuss the reasons we chose evolutionary optimization as the main design method and propose future directions for the

[1]  Jeffrey L. Krichmar,et al.  Neuromodulation as a robot controller , 2009, IEEE Robotics & Automation Magazine.

[2]  Juyang Weng,et al.  Modeling dopamine and serotonin systems in a visual recognition network , 2011, The 2011 International Joint Conference on Neural Networks.

[3]  Ling Guan,et al.  Modularity in neural computing , 1999, Proc. IEEE.

[4]  Shiro Usui,et al.  Mutation-based genetic neural network , 2005, IEEE Transactions on Neural Networks.

[5]  Peter J. Angeline,et al.  An evolutionary algorithm that constructs recurrent neural networks , 1994, IEEE Trans. Neural Networks.

[6]  Luis A. Plana,et al.  SpiNNaker: Mapping neural networks onto a massively-parallel chip multiprocessor , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[7]  David White,et al.  GANNet: A Genetic Algorithm for Optimizing Topology and Weights in Neural Network Design , 1993, IWANN.

[8]  Jürgen Schmidhuber,et al.  Optimal Artificial Curiosity, Creativity, Music, and the Fine Arts , 2005 .

[9]  Gert Cauwenberghs,et al.  Silicon spike-based synaptic array and address-event transceiver , 2004, 2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512).

[10]  Johannes Schemmel,et al.  A wafer-scale neuromorphic hardware system for large-scale neural modeling , 2010, Proceedings of 2010 IEEE International Symposium on Circuits and Systems.

[11]  Harry L. Van Trees,et al.  Detection, Estimation, and Modulation Theory, Part I , 1968 .

[12]  Gert Cauwenberghs,et al.  A real-time spike-domain sensory information processing system [image processing applications] , 2005, 2005 IEEE International Symposium on Circuits and Systems.

[13]  Sung-Bae Cho,et al.  Evolutionary Learning of Modular Neural Networks with Genetic Programming , 1998, Applied Intelligence.

[14]  M. Pickett,et al.  Phase transitions enable computational universality in neuristor-based cellular automata. , 2013, Nanotechnology.

[15]  Yong Liu,et al.  Specifications of Nanoscale Devices and Circuits for Neuromorphic Computational Systems , 2013, IEEE Transactions on Electron Devices.

[16]  J. David Schaffer,et al.  Evolving spiking neural networks for robot control , 2011, Complex Adaptive Systems.

[17]  Stéphane Doncieux,et al.  Evolving modular neural-networks through exaptation , 2009, 2009 IEEE Congress on Evolutionary Computation.

[18]  Ingo Rechenberg,et al.  Evolutionsstrategie : Optimierung technischer Systeme nach Prinzipien der biologischen Evolution , 1973 .

[19]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[20]  Juyang Weng,et al.  Neuromorphic motivated systems , 2011, The 2011 International Joint Conference on Neural Networks.

[21]  Sung-Bae Cho,et al.  Combining modular neural networks developed by evolutionary algorithm , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[22]  John J. Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities , 1999 .

[23]  Thomas S. Brinsmead,et al.  Multiple model adaptive control with safe switching , 2001 .

[24]  Muhaini Othman,et al.  Evolving spiking neural networks for personalised modelling, classification and prediction of spatio-temporal patterns with a case study on stroke , 2014, Neurocomputing.

[25]  Jürgen Schmidhuber,et al.  Curious model-building control systems , 1991, [Proceedings] 1991 IEEE International Joint Conference on Neural Networks.

[26]  Yong Liu,et al.  A 45nm CMOS neuromorphic chip with a scalable architecture for learning in networks of spiking neurons , 2011, 2011 IEEE Custom Integrated Circuits Conference (CICC).

[27]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[28]  W. S. Reilly,et al.  Building Emotional Agents , 1992 .

[29]  Ammar Belatreche,et al.  Advances in Design and Application of Spiking Neural Networks , 2006, Soft Comput..

[30]  Hussein A. Abbass,et al.  An evolutionary artificial neural networks approach for breast cancer diagnosis , 2002, Artif. Intell. Medicine.

[31]  Rudy Setiono,et al.  Use of a quasi-Newton method in a feedforward neural network construction algorithm , 1995, IEEE Trans. Neural Networks.

[32]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[33]  R. Raff.  Understanding Evolution: The Next Step. (Book Reviews: The Shape of Life. Genes, Development, and the Evolution of Animal Form.) , 1996 .

[34]  Yoshua Bengio,et al.  Random Search for Hyper-Parameter Optimization , 2012, J. Mach. Learn. Res..

[35]  Johannes Schemmel,et al.  Wafer-scale integration of analog neural networks , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[36]  Enrique Alba,et al.  Training Neural Networks with GA Hybrid Algorithms , 2004, GECCO.

[37]  Hak-Keung Lam,et al.  Tuning of the structure and parameters of a neural network using an improved genetic algorithm , 2003, IEEE Trans. Neural Networks.

[38]  Dario Floreano,et al.  Genetic Representation and Evolvability of Modular Neural Controllers , 2010, IEEE Computational Intelligence Magazine.

[39]  Nikil D. Dutt,et al.  GPGPU accelerated simulation and parameter tuning for neuromorphic applications , 2014, 2014 19th Asia and South Pacific Design Automation Conference (ASP-DAC).

[40]  Catherine D. Schuman,et al.  Spatiotemporal Classification Using Neuroscience-Inspired Dynamic Architectures , 2014, BICA.

[41]  H. Eichenbaum.  Hippocampus Cognitive Processes and Neural Representations that Underlie Declarative Memory , 2004, Neuron.

[42]  Gregory Hornby,et al.  Measuring, enabling and comparing modularity, regularity and hierarchy in evolutionary design , 2005, GECCO '05.

[43]  N. Tsukahara,et al.  Synaptic plasticity in the mammalian central nervous system. , 1981, Annual review of neuroscience.

[44]  Nikil Dutt,et al.  An efficient automated parameter tuning framework for spiking neural networks , 2014, Front. Neurosci..

[45]  Wulfram Gerstner,et al.  A History of Spike-Timing-Dependent Plasticity , 2011, Front. Syn. Neurosci..

[46]  G. Winocur,et al.  Memory consolidation or transformation: context manipulation and hippocampal representations of memory , 2007, Nature Neuroscience.

[47]  Dharmendra S. Modha,et al.  The cat is out of the bag: cortical simulations with 109 neurons, 1013 synapses , 2009, Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis.

[48]  Olaf Sporns,et al.  Networks analysis, complexity, and brain function , 2002 .

[49]  J. Knott.  The organization of behavior: A neuropsychological theory , 1951 .

[50]  Enrique Alba,et al.  Parallel Genetic Algorithms , 2011, Studies in Computational Intelligence.

[51]  Hieu Tat Nguyen,et al.  A gradient descent rule for spiking neurons emitting multiple spikes , 2005, Inf. Process. Lett..

[52]  Gary William Flake,et al.  The Computational Beauty of Nature: Computer Explorations of Fractals, Chaos, Complex Systems and Adaptation , 1998 .

[53]  Xin Yao,et al.  Evolving artificial neural networks , 1999, Proc. IEEE.

[54]  Richard S. J. Frackowiak,et al.  Navigation-related structural change in the hippocampi of taxi drivers. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[55]  Jon H Kaas,et al.  Topographic Maps are Fundamental to Sensory Processing , 1997, Brain Research Bulletin.

[56]  Don Monroe,et al.  Neuromorphic computing gets ready for the (really) big time , 2014, CACM.

[57]  Carlos E. Garcia,et al.  QUADRATIC PROGRAMMING SOLUTION OF DYNAMIC MATRIX CONTROL (QDMC) , 1986 .

[58]  Vittorio Maniezzo,et al.  Genetic evolution of the topology and weight distribution of neural networks , 1994, IEEE Trans. Neural Networks.

[59]  Henry J. Alitto,et al.  Corticothalamic feedback and sensory processing , 2003, Current Opinion in Neurobiology.

[60]  Steve B. Furber,et al.  Understanding the interconnection network of SpiNNaker , 2009, ICS.

[61]  David B. Fogel,et al.  Evolving Neural Control Systems , 1995, IEEE Expert.

[62]  O. Sporns,et al.  Rich Club Organization of Macaque Cerebral Cortex and Its Role in Network Communication , 2012, PloS one.

[63]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[64]  Kenneth O. Stanley and Joseph Reisinger and Risto Miikkulainen,et al.  Exploiting Morphological Conventions for Genetic Reuse , 2004 .

[65]  Catherine D. Schuman,et al.  Variable structure dynamic artificial neural networks , 2013, BICA 2013.

[66]  Hiroshi Tsujino,et al.  Modular Neural Networks for Reinforcement Learning with Temporal Intrinsic Rewards , 2007, 2007 International Joint Conference on Neural Networks.

[67]  V.P. Plagianakos,et al.  Spiking neural network training using evolutionary algorithms , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

[68]  Wolfgang Maass,et al.  Networks of Spiking Neurons: The Third Generation of Neural Network Models , 1996, Electron. Colloquium Comput. Complex..

[69]  Randall D. Beer,et al.  The dynamics of adaptive behavior: A research program , 1997, Robotics Auton. Syst..

[70]  Michael Athans,et al.  The stochastic control of the F-8C aircraft using a multiple model adaptive control (MMAC) method--Part I: Equilibrium flight , 1977 .

[71]  C Casanova,et al.  When the auditory cortex turns visual. , 2001, Progress in brain research.

[72]  Hussein A. Abbass,et al.  C-Net: A Method for Generating Non-deterministic and Dynamic Multivariate Decision Trees , 2001, Knowledge and Information Systems.

[73]  Gerald Sommer,et al.  Evolutionary reinforcement learning of artificial neural networks , 2007, Int. J. Hybrid Intell. Syst..

[74]  Shingo Mabu,et al.  A Graph-Based Evolutionary Algorithm: Genetic Network Programming (GNP) and Its Extension Using Reinforcement Learning , 2007, Evolutionary Computation.

[75]  Deepa Subramaniam Nachimuthu,et al.  Evolutionary learning of spiking neural networks towards quantification of 3D MRI brain tumor tissues , 2015, Soft Comput..

[76]  Nikil D. Dutt,et al.  Biologically plausible models of homeostasis and STDP: Stability and learning in spiking neural networks , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

[77]  Kenji Doya,et al.  NeuroEvolution Based on Reusable and Hierarchical Modular Representation , 2008, ICONIP.

[78]  Henry Markram,et al.  Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties , 2011, PLoS Comput. Biol..

[79]  Michael Defoin-Platel,et al.  Integrated feature and parameter optimization for an evolving spiking neural network: Exploring heterogeneous probabilistic models , 2009, Neural Networks.

[80]  C.W. Anderson,et al.  Learning to control an inverted pendulum using neural networks , 1989, IEEE Control Systems Magazine.

[81]  Gert Cauwenberghs,et al.  Spatial acuity modulation of an address-event imager , 2004, Proceedings of the 2004 11th IEEE International Conference on Electronics, Circuits and Systems, 2004. ICECS 2004..

[82]  David J. Spiegelhalter,et al.  Machine Learning, Neural and Statistical Classification , 2009 .

[83]  M. Athans,et al.  The uncertainty threshold principle: Fundamental limitations of optimal decision making under dynamic uncertainty , 1976, 1976 IEEE Conference on Decision and Control including the 15th Symposium on Adaptive Processes.

[84]  Bogdan Draganski,et al.  Neuroplasticity: Changes in grey matter induced by training , 2004, Nature.

[85]  Marvin Minsky,et al.  Steps toward Artificial Intelligence , 1995, Proceedings of the IRE.

[86]  Lawrence Davis,et al.  Training Feedforward Neural Networks Using Genetic Algorithms , 1989, IJCAI.

[87]  Keigo Watanabe,et al.  Behavior generation in robots by emotional motivation , 2009, 2009 IEEE International Symposium on Industrial Electronics.

[88]  Johannes Schemmel,et al.  A VLSI Implementation of the Adaptive Exponential Integrate-and-Fire Neuron Model , 2010, NIPS.

[89]  J. Panksepp.  Affective Neuroscience: The Foundations of Human and Animal Emotions , 1998 .

[90]  Risto Miikkulainen,et al.  Evolving neural networks , 2008, GECCO '08.

[91]  Merav Parter,et al.  Facilitated Variation: How Evolution Learns from Past Environments To Generalize to New Environments , 2008, PLoS Comput. Biol..

[92]  Huaiyu Dai,et al.  Adaptive quickest change detection with unknown parameter , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[93]  Yoshua. Bengio,et al.  Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..

[94]  O. Mangasarian,et al.  Pattern Recognition Via Linear Programming: Theory and Application to Medical Diagnosis , 1989 .

[95]  Xin Yao,et al.  Evolving artificial neural network ensembles , 2008, IEEE Computational Intelligence Magazine.

[96]  C. W. Harper,et al.  Order in living organisms : a systems analysis of evolution , 1980 .

[97]  H. P. Schwefel,et al.  Numerische Optimierung von Computermodellen mittels der Evo-lutionsstrategie , 1977 .

[98]  Hojjat Adeli,et al.  Spiking Neural Networks , 2009, Int. J. Neural Syst..

[99]  Michael L. Anderson,et al.  Neural reuse in the evolution and development of the brain: evidence for developmental homology? , 2013, Developmental psychobiology.

[100]  Pierre-Yves Oudeyer,et al.  Intrinsically Motivated Machines , 2006, 50 Years of Artificial Intelligence.

[101]  Jihoon Yang,et al.  Constructive Neural-Network Learning Algorithms for Pattern Classification , 2000 .

[102]  Catherine D. Schuman,et al.  Dynamic Adaptive Neural Network Array , 2014, UCNC.

[103]  Wolfgang Maass,et al.  Emulation of Hopfield networks with spiking neurons in temporal coding , 1998 .

[104]  Steve B. Furber,et al.  Modeling Spiking Neural Networks on SpiNNaker , 2010, Computing in Science & Engineering.

[105]  Eric B. Keverne,et al.  Epigenetics, brain evolution and behaviour , 2008, Frontiers in Neuroendocrinology.

[106]  G. Gattu,et al.  Nonlinear Quadratic Dynamic Matrix Control with State Estimation , 1992 .

[107]  Geoffrey E. Hinton,et al.  A Learning Algorithm for Boltzmann Machines , 1985, Cogn. Sci..

[108]  Xin YaoComputational.  A Population-Based Learning Algorithm Which Learns BothArchitectures and Weights of Neural Networks , 1996 .

[109]  Dimitri P. Bertsekas,et al.  Dynamic Programming and Optimal Control, Two Volume Set , 1995 .

[110]  Randall D. Beer,et al.  The Dynamics of Active Categorical Perception in an Evolved Model Agent , 2003, Adapt. Behav..

[111]  Stephen P. Boyd,et al.  A New CAD Method and Associated Architectures for Linear Controllers , 1987, 1987 American Control Conference.

[112]  E. Marder,et al.  Central pattern generators and the control of rhythmic movements , 2001, Current Biology.

[113]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[114]  Nikola K. Kasabov,et al.  Evolving Spiking Neural Networks and Neurogenetic Systems for Spatio- and Spectro-Temporal Data Modelling and Pattern Recognition , 2012, WCCI.

[115]  Rodrigo Alvarez-Icaza,et al.  Neurogrid: A Mixed-Analog-Digital Multichip System for Large-Scale Neural Simulations , 2014, Proceedings of the IEEE.

[116]  L. Altenberg,et al.  PERSPECTIVE: COMPLEX ADAPTATIONS AND THE EVOLUTION OF EVOLVABILITY , 1996, Evolution; international journal of organic evolution.

[117]  D. Wolpert,et al.  Internal models in the cerebellum , 1998, Trends in Cognitive Sciences.

[118]  T. Sejnowski,et al.  Natural patterns of activity and long-term synaptic plasticity , 2000, Current Opinion in Neurobiology.

[119]  César Hervás-Martínez,et al.  Cooperative coevolution of artificial neural network ensembles for pattern classification , 2005, IEEE Transactions on Evolutionary Computation.

[120]  U. Alon,et al.  Spontaneous evolution of modularity and network motifs. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[121]  Nuttapong Chentanez,et al.  Intrinsically Motivated Reinforcement Learning , 2004, NIPS.

[122]  Mario D. Capuozzo,et al.  A compact evolutionary algorithm for integer spiking neural network robot controllers , 2011, 2011 Proceedings of IEEE Southeastcon.

[123]  Steve B. Furber,et al.  Neural Systems Engineering , 2008, Computational Intelligence: A Compendium.

[124]  Dario Floreano,et al.  Neuroevolution: from architectures to learning , 2008, Evol. Intell..

[125]  Steve Furber,et al.  Power-efficient simulation of detailed cortical microcircuits on SpiNNaker , 2012, Journal of Neuroscience Methods.

[126]  Yann LeCun,et al.  Optimal Brain Damage , 1989, NIPS.

[127]  Hojjat Adeli,et al.  Third Generation Neural Networks: Spiking Neural Networks , 2009 .

[128]  Xin Yao,et al.  A new evolutionary system for evolving artificial neural networks , 1997, IEEE Trans. Neural Networks.

[129]  David C. Geary,et al.  The Origin of Mind: Evolution of Brain, Cognition, and General Intelligence , 2004 .

[130]  Xin Yao,et al.  Towards designing artificial neural networks by evolution , 1998 .

[131]  Risto Miikkulainen,et al.  Accelerated Neural Evolution through Cooperatively Coevolved Synapses , 2008, J. Mach. Learn. Res..

[132]  Jürgen Schmidhuber,et al.  Formal Theory of Creativity, Fun, and Intrinsic Motivation (1990–2010) , 2010, IEEE Transactions on Autonomous Mental Development.

[133]  John N. Tsitsiklis,et al.  Neuro-Dynamic Programming , 1996, Encyclopedia of Machine Learning.

[134]  Gert Cauwenberghs,et al.  Dynamically Reconfigurable Silicon Array of Spiking Neurons With Conductance-Based Synapses , 2007, IEEE Transactions on Neural Networks.

[135]  Dario Floreano,et al.  Evolution of Spiking Neural Controllers for Autonomous Vision-Based Robots , 2001, EvoRobots.

[136]  César Hervás-Martínez,et al.  COVNET: a cooperative coevolutionary model for evolving artificial neural networks , 2003, IEEE Trans. Neural Networks.

[137]  Stefano Fusi,et al.  The dynamical response properties of neocortical neurons to temporally modulated noisy inputs in vitro. , 2008, Cerebral cortex.

[138]  J. Schemmel,et al.  Wafer-scale VLSI implementations of pulse coupled neural networks , 2007 .

[139]  Mitsuo Kawato,et al.  Internal models for motor control and trajectory planning , 1999, Current Opinion in Neurobiology.

[140]  PoliRiccardo,et al.  Evolving the Topology and the Weights of Neural Networks Using a Dual Representation , 1998 .

[141]  Catherine D. Schuman,et al.  Visual analytics for neuroscience-inspired dynamic architectures , 2014, 2014 IEEE Symposium on Foundations of Computational Intelligence (FOCI).

[142]  Nuttapong Chentanez,et al.  Intrinsically Motivated Learning of Hierarchical Collections of Skills , 2004 .

[143]  Risto Miikkulainen,et al.  Forming Neural Networks Through Efficient and Adaptive Coevolution , 1997, Evolutionary Computation.

[144]  Dorothea Heiss-Czedik,et al.  An Introduction to Genetic Algorithms. , 1997, Artificial Life.

[145]  G. Wagner.  HOMOLOGUES, NATURAL KINDS AND THE EVOLUTION OF MODULARITY , 1996 .

[146]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[147]  A. Selverston,et al.  Dynamical principles in neuroscience , 2006 .

[148]  Jeffrey L. Krichmar,et al.  The Neuromodulatory System: A Framework for Survival and Adaptive Behavior in a Challenging World , 2008, Adapt. Behav..

[149]  Kenneth O. Stanley,et al.  Compositional Pattern Producing Networks : A Novel Abstraction of Development , 2007 .

[150]  L. Darrell Whitley,et al.  Genetic algorithms and neural networks: optimizing connections and connectivity , 1990, Parallel Comput..

[151]  John C. Gallagher,et al.  A Modified Compact Genetic Algorithm For The Intrinsic Evolution Of Continuous Time Recurrent Neural Networks , 2002, GECCO.

[152]  Risto Miikkulainen,et al.  A Neuroevolution Approach to General Atari Game Playing , 2014, IEEE Transactions on Computational Intelligence and AI in Games.

[153]  Daniel Graupe,et al.  Principles of Artificial Neural Networks , 2018, Advanced Series in Circuits and Systems.

[154]  Linda Bushnell,et al.  Fast Modifications of the SpikeProp Algorithm , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[155]  G. Baldassarre,et al.  Evolving internal reinforcers for an intrinsically motivated reinforcement-learning robot , 2007, 2007 IEEE 6th International Conference on Development and Learning.

[156]  C. R. Cutler,et al.  Optimal Solution of Dynamic Matrix Control with Linear Programing Techniques (LDMC) , 1985, 1985 American Control Conference.

[157]  Bertrand Fontaine,et al.  Fitting Neuron Models to Spike Trains , 2011, Front. Neurosci..

[158]  A. P. Wieland,et al.  Evolving neural network controllers for unstable systems , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

[159]  Hiroshi Tsujino,et al.  Basal Ganglia Models for Autonomous Behavior Learning , 2009, Creating Brain-Like Intelligence.

[160]  Hojjat Adeli,et al.  A new supervised learning algorithm for multiple spiking neural networks with application in epilepsy and seizure detection , 2009, Neural Networks.

[161]  Kwabena Boahen,et al.  Silicon Neurons That Compute , 2012, ICANN.

[162]  Sander M. Bohte,et al.  Error-backpropagation in temporally encoded networks of spiking neurons , 2000, Neurocomputing.

[163]  Christian Lebiere,et al.  The Cascade-Correlation Learning Architecture , 1989, NIPS.

[164]  Randall D. Beer,et al.  On the Dynamics of Small Continuous-Time Recurrent Neural Networks , 1995, Adapt. Behav..

[165]  Yann LeCun,et al.  The mnist database of handwritten digits , 2005 .

[166]  María Malfaz,et al.  A Biologically Inspired Architecture for an Autonomous and Social Robot , 2011, IEEE Transactions on Autonomous Mental Development.

[167]  Jianguo Xin,et al.  Supervised learning with spiking neural networks , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[168]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[169]  M. O’Halloran,et al.  Spiking Neural Networks for Breast Cancer Classification in a Dielectrically Heterogeneous Breast , 2011 .

[170]  Neil Hernández-Gress,et al.  Pattern Recognition with Spiking Neural Networks , 2013, MICAI.

[171]  Rajarshi Das,et al.  Genetic reinforcement learning for neural networks , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

[172]  Nikil D. Dutt,et al.  Categorization and decision-making in a neurobiologically plausible spiking network using a STDP-like learning rule , 2013, Neural Networks.

[173]  Michael Schmitt,et al.  Unsupervised learning and self-organization in networks of spiking neurons , 2001 .

[174]  Jean,et al.  The Computer and the Brain , 1989, Annals of the History of Computing.

[175]  H. Witsenhausen.  Separation of estimation and control for discrete time systems , 1971 .

[176]  Kumpati S. Narendra,et al.  Adaptive control using multiple models , 1997, IEEE Trans. Autom. Control..

[177]  Gert Cauwenberghs,et al.  Saliency-Driven Image Acuity Modulation on a Reconfigurable Array of Spiking Silicon Neurons , 2004, NIPS.

[178]  Razvan V. Florian,et al.  Correct equations for the dynamics of the cart-pole system , 2005 .

[179]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[180]  Thomas K. Berger,et al.  A synaptic organizing principle for cortical neuronal groups , 2011, Proceedings of the National Academy of Sciences.

[181]  Risto Miikkulainen,et al.  Evolving Neural Networks through Augmenting Topologies , 2002, Evolutionary Computation.

[182]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[183]  Risto Miikkulainen,et al.  Efficient Reinforcement Learning through Symbiotic Evolution , 2004 .

[184]  Simei Gomes Wysoski,et al.  Fast and adaptive network of spiking neurons for multi-view visual pattern recognition , 2008, Neurocomputing.

[185]  D. George,et al.  Hierarchical Temporal Memory Concepts , Theory , and Terminology , 2006 .

[186]  Dharmendra S. Modha,et al.  Cognitive Computing , 2011, Informatik-Spektrum.

[187]  Jay H. Lee,et al.  Model predictive control: past, present and future , 1999 .

[188]  J. Byrne.  Cellular analysis of associative learning. , 1987, Physiological reviews.

[189]  Risto Miikkulainen,et al.  Efficient Non-linear Control Through Neuroevolution , 2006, ECML.

[190]  K. Schittkowski,et al.  NONLINEAR PROGRAMMING , 2022 .

[191]  Simei Gomes Wysoski,et al.  On-Line Learning with Structural Adaptation in a Network of Spiking Neurons for Visual Pattern Recognition , 2006, ICANN.

[192]  Risto Miikkulainen,et al.  Evolving adaptive neural networks with and without adaptive synapses , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[193]  Dario Floreano,et al.  Evolution of spiking neural circuits in autonomous mobile robots , 2006, Int. J. Intell. Syst..

[194]  John R. Koza,et al.  Genetic generation of both the weights and architecture for a neural network , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

[195]  Per Brodal,et al.  The Central Nervous System: Structure and Function , 2004, Journal of Neurology.

[196]  Martin Fodslette Møller,et al.  A scaled conjugate gradient algorithm for fast supervised learning , 1993, Neural Networks.

[197]  H. Markram.  The Blue Brain Project , 2006, Nature Reviews Neuroscience.

[198]  Gerald Sommer,et al.  Efficient neural network pruning during neuro-evolution , 2009, 2009 International Joint Conference on Neural Networks.

[199]  Hod Lipson,et al.  Principles of modularity, regularity, and hierarchy for scalable systems , 2007 .

[200]  François Michaud,et al.  Using Motives and Artificial Emotions for Prolonged Activity of a Group of Autonomous Robots , 2001 .

[201]  Yi Dong,et al.  Optimization Methods for Spiking Neurons and Networks , 2010, IEEE Transactions on Neural Networks.

[202]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[203]  Jeffrey L. Krichmar,et al.  Evolution of biologically plausible neural networks performing a visually guided reaching task , 2014, GECCO.

[204]  Sung-Bae Cho,et al.  Modular neural networks evolved by genetic programming , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[205]  Jeffrey L. Krichmar,et al.  A biologically inspired action selection algorithm based on principles of neuromodulation , 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

[206]  Cameron Patterson,et al.  Scalable event-driven native parallel processing: the SpiNNaker neuromimetic system , 2010, Conf. Computing Frontiers.

[207]  John C. Doyle.  Analysis of Feedback Systems with Structured Uncertainty , 1982 .

[208]  Steve B. Furber,et al.  The Leaky Integrate-and-Fire neuron: A platform for synaptic model exploration on the SpiNNaker chip , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[209]  Yuichi Nakamura,et al.  Approximation of dynamical systems by continuous time recurrent neural networks , 1993, Neural Networks.

[210]  D. Chklovskii,et al.  Maps in the brain: what can we learn from them? , 2004, Annual review of neuroscience.

[211]  Risto Miikkulainen,et al.  Evolving Reusable Neural Modules , 2004, GECCO.

[212]  Kenneth O. Stanley,et al.  From modulated Hebbian plasticity to simple behavior learning through noise and weight saturation , 2012, Neural Networks.

[213]  Wolfgang Maass,et al.  Networks of spiking neurons can emulate arbitrary Hopfield nets in temporal coding , 1997 .

[214]  Hongtao Lu,et al.  On stability of nonlinear continuous-time neural networks with delays , 2000, Neural Networks.

[215]  Christian Igel,et al.  Evolutionary tuning of multiple SVM parameters , 2005, ESANN.

[216]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[217]  Yohannes Kassahun,et al.  Towards a unified approach to learning and adaptation , 2006 .

[218]  Keigo Watanabe,et al.  Affection Based Multi-robot Team Work , 2008 .

[219]  Hani Hagras,et al.  Evolving spiking neural network controllers for autonomous robots , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[220]  Risto Miikkulainen,et al.  2-D Pole Balancing with Recurrent Evolutionary Networks , 1998 .

[221]  Johannes Schemmel,et al.  Six Networks on a Universal Neuromorphic Computing Substrate , 2012, Front. Neurosci..

[222]  Auke Jan Ijspeert,et al.  Central pattern generators for locomotion control in animals and robots: A review , 2008, Neural Networks.

[223]  Andrew G. Barto,et al.  An intrinsic reward mechanism for efficient exploration , 2006, ICML.

[224]  Y. Prigent.  [Long term depression]. , 1989, Annales medico-psychologiques.

[225]  Richard L. Lewis,et al.  Intrinsically Motivated Reinforcement Learning: An Evolutionary Perspective , 2010, IEEE Transactions on Autonomous Mental Development.

[226]  G. Schlaug,et al.  Brain Structures Differ between Musicians and Non-Musicians , 2003, The Journal of Neuroscience.

[227]  Randall D. Beer,et al.  Evolving Dynamical Neural Networks for Adaptive Behavior , 1992, Adapt. Behav..

[228]  César Hervás-Martínez,et al.  An alternative approach for neural network evolution with a genetic algorithm: Crossover by combinatorial optimization , 2006, Neural Networks.

[229]  Kenneth A. De Jong,et al.  Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents , 2000, Evolutionary Computation.

[230]  Johannes Schemmel,et al.  Implementing Synaptic Plasticity in a VLSI Spiking Neural Network Model , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[231]  Peter Dayan,et al.  Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems , 2001 .

[232]  W. Singer,et al.  Long-term depression of excitatory synaptic transmission and its relationship to long-term potentiation , 1993, Trends in Neurosciences.

[233]  Kenneth O. Stanley,et al.  A Hypercube-Based Encoding for Evolving Large-Scale Neural Networks , 2009, Artificial Life.

[234]  David W. Arathorn,et al.  Map-Seeking Circuits in Visual Cognition: A Computational Mechanism for Biological and Machine Vision , 2002 .

[235]  Tim Harford,et al.  Adapt: Why Success Always Starts with Failure , 2011 .

[236]  D. R. McGregor,et al.  Designing application-specific neural networks using the structured genetic algorithm , 1992, [Proceedings] COGANN-92: International Workshop on Combinations of Genetic Algorithms and Neural Networks.

[237]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[238]  Catherine D. Schuman,et al.  Dynamic Artificial Neural Networks with Affective Systems , 2013, PloS one.

[239]  Kwabena Boahen,et al.  Dynamical System Guided Mapping of Quantitative Neuronal Models Onto Neuromorphic Hardware , 2012, IEEE Transactions on Circuits and Systems I: Regular Papers.

[240]  Karlheinz Meier,et al.  Introducing the Human Brain Project , 2011, FET.

[241]  Jim D. Garside,et al.  Overview of the SpiNNaker System Architecture , 2013, IEEE Transactions on Computers.

[242]  Yoshua Bengio,et al.  Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.

[243]  Christian Igel,et al.  Neuroevolution for reinforcement learning using evolution strategies , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[244]  Bernhard Sendhoff,et al.  Evolutionary Multi-objective Optimization of Spiking Neural Networks , 2007, ICANN.

[245]  Rosalind W. Picard,et al.  Affective-Cognitive Learning and Decision Making: A Motivational Reward Framework for Affective Agents , 2005, ACII.

[246]  Douglas L. Rosene,et al.  The Geometric Structure of the Brain Fiber Pathways , 2012, Science.

[247]  Gert Cauwenberghs,et al.  A Multichip Neuromorphic System for Spike-Based Visual Information Processing , 2007, Neural Computation.

[248]  James A. Reggia,et al.  Evolutionary Design of Neural Network Architectures Using a Descriptive Encoding Language , 2006, IEEE Transactions on Evolutionary Computation.

[249]  Dharmendra S. Modha,et al.  A digital neurosynaptic core using embedded crossbar memory with 45pJ per spike in 45nm , 2011, 2011 IEEE Custom Integrated Circuits Conference (CICC).

[250]  D B Fogel,et al.  Evolving neural networks for detecting breast cancer. , 1995, Cancer letters.

[251]  Steve B. Furber,et al.  Concurrent heterogeneous neural model simulation on real-time neuromimetic hardware , 2011, Neural Networks.

[252]  Kenneth O. Stanley,et al.  Autonomous Evolution of Topographic Regularities in Artificial Neural Networks , 2010, Neural Computation.

[253]  Jonathan E. Fieldsend,et al.  Pareto evolutionary neural networks , 2005, IEEE Transactions on Neural Networks.