Indirect Training Algorithms for Spiking Neural Networks Controlled Virtual Insect Navigation
暂无分享,去创建一个
[1] H. Markram,et al. Regulation of Synaptic Efficacy by Coincidence of Postsynaptic APs and EPSPs , 1997, Science.
[2] Wulfram Gerstner,et al. Hebbian learning of pulse timing in the Barn Owl auditory system , 1999 .
[3] Gregory S. Snider,et al. ‘Memristive’ switches enable ‘stateful’ logic operations via material implication , 2010, Nature.
[4] L. Abbott,et al. Model neurons: From Hodgkin-Huxley to hopfield , 1990 .
[5] Wolfgang Maass,et al. Noisy Spiking Neurons with Temporal Coding have more Computational Power than Sigmoidal Neurons , 1996, NIPS.
[6] R. Strauss. The central complex and the genetic dissection of locomotor behaviour , 2002, Current Opinion in Neurobiology.
[7] P. De Camilli,et al. The distribution of synapsin I and synaptophysin in hippocampal neurons developing in culture , 1991, The Journal of neuroscience : the official journal of the Society for Neuroscience.
[8] Nicholas J. Strausfeld,et al. Arthropod Brains: Evolution, Functional Elegance, and Historical Significance , 2012 .
[9] Razvan V. Florian,et al. Reinforcement Learning Through Modulation of Spike-Timing-Dependent Synaptic Plasticity , 2007, Neural Computation.
[10] Ioana Sporea,et al. Supervised Learning in Multilayer Spiking Neural Networks , 2012, Neural Computation.
[11] Giacomo Indiveri,et al. Analog VLSI Model of Locust DCMD Neuron Response for Computation of Object Approach , 1998 .
[12] Antonius M J VanDongen,et al. The Nonkinase Phorbol Ester Receptor α1-Chimerin Binds the NMDA Receptor NR2A Subunit and Regulates Dendritic Spine Density , 2005, The Journal of Neuroscience.
[13] E. Isacoff,et al. Light-activated ion channels for remote control of neuronal firing , 2004, Nature Neuroscience.
[14] Andreas G. Andreou,et al. Analog VLSI neuromorphic image acquisition and pre-processing systems , 1995, Neural Networks.
[15] Phill Rowcliffe,et al. Training Spiking Neuronal Networks With Applications in Engineering Tasks , 2008, IEEE Transactions on Neural Networks.
[16] U. Homberg,et al. Organization and functional roles of the central complex in the insect brain. , 2014, Annual review of entomology.
[17] Abdulrazak Yahya Saleh,et al. A Novel hybrid algorithm of Differential evolution with Evolving Spiking Neural Network for pre-synaptic neurons Optimization , 2014 .
[18] Misha Mahowald,et al. A silicon model of early visual processing , 1993, Neural Networks.
[19] D. Stewart,et al. The missing memristor found , 2008, Nature.
[20] Jean-Pascal Pfister,et al. Optimal Spike-Timing-Dependent Plasticity for Precise Action Potential Firing in Supervised Learning , 2005, Neural Computation.
[21] B. Zemelman,et al. Selective Photostimulation of Genetically ChARGed Neurons , 2002, Neuron.
[22] Anthony N. Burkitt,et al. A Review of the Integrate-and-fire Neuron Model: I. Homogeneous Synaptic Input , 2006, Biological Cybernetics.
[23] Arnaud Delorme,et al. Spike-based strategies for rapid processing , 2001, Neural Networks.
[24] T. Delbruck,et al. A 128 128 120 dB 15 s Latency Asynchronous Temporal Contrast Vision Sensor , 2006 .
[25] A. Hodgkin,et al. A quantitative description of membrane current and its application to conduction and excitation in nerve , 1952, The Journal of physiology.
[26] Chris Yakopcic,et al. Memristor-based pattern recognition for image processing: an adaptive coded aperture imaging and sensing opportunity , 2010, Optical Engineering + Applications.
[27] P. J. Sjöström,et al. Rate, Timing, and Cooperativity Jointly Determine Cortical Synaptic Plasticity , 2001, Neuron.
[28] R. FitzHugh. Impulses and Physiological States in Theoretical Models of Nerve Membrane. , 1961, Biophysical journal.
[29] Florentin Wörgötter,et al. A neuromorphic depth-from-motion vision model with STDP adaptation , 2006, IEEE Transactions on Neural Networks.
[30] Nikola Kasabov,et al. Dynamic evolving spiking neural networks for on-line spatio- and spectro-temporal pattern recognition. , 2013, Neural networks : the official journal of the International Neural Network Society.
[31] Sundaram Suresh,et al. A sequential learning algorithm for a Minimal Spiking Neural Network (MSNN) classifier , 2014, 2014 International Joint Conference on Neural Networks (IJCNN).
[32] K. Deisseroth,et al. Millisecond-timescale, genetically targeted optical control of neural activity , 2005, Nature Neuroscience.
[33] Eve Marder,et al. Reduction of conductance-based neuron models , 1992, Biological Cybernetics.
[34] Simei Gomes Wysoski,et al. Adaptive Learning Procedure for a Network of Spiking Neurons and Visual Pattern Recognition , 2006, ACIVS.
[35] H Rostro-Gonzalez,et al. Parameter estimation in spiking neural networks: a reverse-engineering approach , 2012, Journal of neural engineering.
[36] Georg Schnitger,et al. On the computational power of sigmoid versus Boolean threshold circuits , 1991, [1991] Proceedings 32nd Annual Symposium of Foundations of Computer Science.
[37] Ammar Belatreche,et al. An online supervised learning method for spiking neural networks with adaptive structure , 2014, Neurocomputing.
[38] G. Bi,et al. Synaptic Modifications in Cultured Hippocampal Neurons: Dependence on Spike Timing, Synaptic Strength, and Postsynaptic Cell Type , 1998, The Journal of Neuroscience.
[39] J. Albus. A Theory of Cerebellar Function , 1971 .
[40] Peter Dayan,et al. Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems , 2001 .
[41] D Ferster,et al. Cracking the Neuronal Code , 1995, Science.
[42] Kyungmin Kim,et al. Memristor Applications for Programmable Analog ICs , 2011, IEEE Transactions on Nanotechnology.
[43] Silvia Ferrari,et al. Biologically realizable reward-modulated hebbian training for spiking neural networks , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).
[44] Andrzej J. Kasinski,et al. Supervised Learning in Spiking Neural Networks with ReSuMe: Sequence Learning, Classification, and Spike Shifting , 2010, Neural Computation.
[45] C. Pennartz. Reinforcement learning by Hebbian synapses with adaptive thresholds , 1997, Neuroscience.
[46] Steven M. LaValle,et al. Planning algorithms , 2006 .
[47] S. Yoshizawa,et al. An Active Pulse Transmission Line Simulating Nerve Axon , 1962, Proceedings of the IRE.
[48] Christof Koch,et al. An Analog VLSI Model of the Fly Elementary Motion Detector , 1997, NIPS.
[49] Xu Zhang,et al. Spike-based indirect training of a spiking neural network-controlled virtual insect , 2013, 52nd IEEE Conference on Decision and Control.
[50] Y. Dan,et al. Hebbian depression of isolated neuromuscular synapses in vitro. , 1992, Science.
[51] L. Abbott,et al. Competitive Hebbian learning through spike-timing-dependent synaptic plasticity , 2000, Nature Neuroscience.
[52] W. Levy,et al. Temporal contiguity requirements for long-term associative potentiation/depression in the hippocampus , 1983, Neuroscience.
[53] Y. Dan,et al. Receptive-Field Modification in Rat Visual Cortex Induced by Paired Visual Stimulation and Single-Cell Spiking , 2006, Neuron.
[54] David J. Frank,et al. Nanoscale CMOS , 1999, Proc. IEEE.
[55] T. Bliss,et al. Long‐lasting potentiation of synaptic transmission in the dentate area of the unanaesthetized rabbit following stimulation of the perforant path , 1973, The Journal of physiology.
[56] Eugene M. Izhikevich,et al. Simple model of spiking neurons , 2003, IEEE Trans. Neural Networks.
[57] D. Hubel,et al. Receptive fields of single neurones in the cat's striate cortex , 1959, The Journal of physiology.
[58] Feng Zhang,et al. Channelrhodopsin-2 and optical control of excitable cells , 2006, Nature Methods.
[59] Wolfgang Maass,et al. Networks of Spiking Neurons: The Third Generation of Neural Network Models , 1996, Electron. Colloquium Comput. Complex..
[60] N. Strausfeld,et al. Deep Homology of Arthropod Central Complex and Vertebrate Basal Ganglia , 2013, Science.
[61] C. Morris,et al. Voltage oscillations in the barnacle giant muscle fiber. , 1981, Biophysical journal.
[62] Harald Burgsteiner,et al. Imitation learning with spiking neural networks and real-world devices , 2006, Eng. Appl. Artif. Intell..
[63] Robert A. Legenstein,et al. What Can a Neuron Learn with Spike-Timing-Dependent Plasticity? , 2005, Neural Computation.
[64] Kelin J. Kuhn,et al. Moore's Law Past 32nm: Future Challenges in Device Scaling , 2009, 2009 13th International Workshop on Computational Electronics.
[65] T. Bliss,et al. Long‐lasting potentiation of synaptic transmission in the dentate area of the anaesthetized rabbit following stimulation of the perforant path , 1973, The Journal of physiology.
[66] Wei Yang Lu,et al. Nanoscale memristor device as synapse in neuromorphic systems. , 2010, Nano letters.
[67] Robert F. Stengel,et al. Optimal Control and Estimation , 1994 .
[68] Erik De Schutter,et al. Computational Modeling Methods for Neuroscientists , 2009 .
[69] V. Mountcastle. Modality and topographic properties of single neurons of cat's somatic sensory cortex. , 1957, Journal of neurophysiology.
[70] Andrew S. Cassidy,et al. Cognitive computing systems: Algorithms and applications for networks of neurosynaptic cores , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).
[71] Sander M. Bohte,et al. Error-backpropagation in temporally encoded networks of spiking neurons , 2000, Neurocomputing.
[72] Silvia Ferrari,et al. Indirect training of a spiking neural network for flight control via spike-timing-dependent synaptic plasticity , 2010, 49th IEEE Conference on Decision and Control (CDC).
[73] Ila R Fiete,et al. Gradient learning in spiking neural networks by dynamic perturbation of conductances. , 2006, Physical review letters.
[74] Wofgang Maas,et al. Networks of spiking neurons: the third generation of neural network models , 1997 .
[75] Jean-Pascal Pfister,et al. Optimal Hebbian Learning: A Probabilistic Point of View , 2003, ICANN.
[76] José Carlos Príncipe,et al. Approximate reconstruction of bandlimited functions for the integrate and fire sampler , 2009, Advances in Computational Mathematics.
[77] Leon O. Chua,et al. Neural Synaptic Weighting With a Pulse-Based Memristor Circuit , 2012, IEEE Transactions on Circuits and Systems I: Regular Papers.
[78] Xu Zhang,et al. A Radial Basis Function Spike Model for Indirect Learning via Integrate-and-Fire Sampling and Reconstruction Techniques , 2012, Adv. Artif. Neural Syst..
[79] Wolfgang Maass,et al. A Reward-Modulated Hebbian Learning Rule Can Explain Experimentally Observed Network Reorganization in a Brain Control Task , 2010, The Journal of Neuroscience.