Neuroscience-Inspired Dynamic Architectures
暂无分享,去创建一个
[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.