How Gibbs distributions may naturally arise from synaptic adaptation mechanisms
暂无分享,去创建一个
[1] 廣瀬雄一,et al. Neuroscience , 2019, Workplace Attachments.
[2] Bruno Cessac,et al. Parametric estimation of spike train statistics , 2009, BMC Neuroscience.
[3] Bruno Cessac,et al. Back-engineering of spiking neural networks parameters , 2009, BMC Neuroscience.
[4] T. Viéville,et al. How Gibbs Distributions May Naturally Arise from Synaptic Adaptation Mechanisms. A Model-Based Argumentation , 2008, 0812.3899.
[5] E. Seneta. Non-negative Matrices and Markov Chains , 2008 .
[6] Shun-ichi Amari,et al. Discrimination with Spike Times and ISI Distributions , 2008, Neural Computation.
[7] W. Bialek,et al. Combinatorial coding in neural populations , 2008, 0803.3837.
[8] Jonathan Touboul,et al. Bifurcation Analysis of a General Class of Nonlinear Integrate-and-Fire Neurons , 2008, SIAM J. Appl. Math..
[9] Yun Gao,et al. Estimating the Entropy of Binary Time Series: Methodology, Some Theory and a Simulation Study , 2008, Entropy.
[10] B. Cessac. A discrete time neural network model with spiking neurons , 2007, Journal of mathematical biology.
[11] Bruno Cessac,et al. A Mathematical Analysis of the Effects of Hebbian Learning Rules on the Dynamics and Structure of Discrete-Time Random Recurrent Neural Networks , 2007, Neural Computation.
[12] A. Georgopoulos,et al. Mapping of the preferred direction in the motor cortex , 2007, Proceedings of the National Academy of Sciences.
[13] Bruno Cessac,et al. Effects of Hebbian learning on the dynamics and structure of random networks with inhibitory and excitatory neurons , 2007, Journal of Physiology-Paris.
[14] Jean-Pascal Pfister,et al. Optimality Model of Unsupervised Spike-Timing-Dependent Plasticity: Synaptic Memory and Weight Distribution , 2007, Neural Computation.
[15] Sander M. Bohte,et al. Reducing the Variability of Neural Responses: A Computational Theory of Spike-Timing-Dependent Plasticity , 2007, Neural Computation.
[16] Carson C. Chow,et al. Stochastic Dynamics of a Finite-Size Spiking Neural Network , 2007, Neural Computation.
[17] Bruno Cessac,et al. On Dynamics of Integrate-and-Fire Neural Networks with Adaptive Conductances , 2007 .
[18] Michael J. Berry,et al. Ising models for networks of real neurons , 2006, q-bio/0611072.
[19] B. Cessac. Does the complex susceptibility of the Hénon map have a pole in the upper-half plane? A numerical investigation , 2006, nlin/0609039.
[20] Bruno Cessac,et al. From neuron to neural networks dynamics , 2006, ArXiv.
[21] M. Diamond,et al. Deciphering the Spike Train of a Sensory Neuron: Counts and Temporal Patterns in the Rat Whisker Pathway , 2006, The Journal of Neuroscience.
[22] Alain Destexhe,et al. Analytical Integrate-and-Fire Neuron Models with Conductance-Based Dynamics for Event-Driven Simulation Strategies , 2006, Neural Computation.
[23] Michael J. Berry,et al. Weak pairwise correlations imply strongly correlated network states in a neural population , 2005, Nature.
[24] Hédi Soula,et al. Spontaneous Dynamics of Asymmetric Random Recurrent Spiking Neural Networks , 2004, Neural Computation.
[25] Zou Quan,et al. Modèles computationnels de la plasticité impulsionnelle : synapses, neurones et circuits , 2006 .
[26] E. Adrian,et al. The impulses produced by sensory nerve-endings: Part II. The response of a Single End-Organ. , 2006, The Journal of physiology.
[27] Wulfram Gerstner,et al. Integrate-and-Fire models with adaptation are good enough , 2005, NIPS.
[28] Michael J. Black,et al. Modeling Neural Population Spiking Activity with Gibbs Distributions , 2005, NIPS.
[29] Wulfram Gerstner,et al. Adaptive exponential integrate-and-fire model as an effective description of neuronal activity. , 2005, Journal of neurophysiology.
[30] W. Gerstner,et al. Generalized Bienenstock-Cooper-Munro rule for spiking neurons that maximizes information transmission. , 2005, Proceedings of the National Academy of Sciences of the United States of America.
[31] Hédi Soula,et al. Dynamique et plasticité dans les réseaux de neurones à impulsions : étude du couplage temporel réseau / agent / environnement , 2005 .
[32] Eugene M. Izhikevich,et al. Which model to use for cortical spiking neurons? , 2004, IEEE Transactions on Neural Networks.
[33] Nathan Intrator,et al. Theory of Cortical Plasticity , 2004 .
[34] Matthew A. Wilson,et al. Dynamic Analyses of Information Encoding in Neural Ensembles , 2004, Neural Computation.
[35] B. Cessac,et al. Self-Organized Criticality and Thermodynamic Formalism , 2002, nlin/0209038.
[36] John P. Miller,et al. Temporal encoding in nervous systems: A rigorous definition , 1995, Journal of Computational Neuroscience.
[37] C. Malsburg. Self-organization of orientation sensitive cells in the striate cortex , 2004, Kybernetik.
[38] Don H. Johnson,et al. Neural Population Structures and Consequences for Neural Coding , 2004, Journal of Computational Neuroscience.
[39] Peter Dayan,et al. Plasticity Kernels and Temporal Statistics , 2003, NIPS.
[40] Eugene M. Izhikevich,et al. Simple model of spiking neurons , 2003, IEEE Trans. Neural Networks.
[41] Eugene M. Izhikevich,et al. Relating STDP to BCM , 2003, Neural Computation.
[42] Gal Chechik,et al. Spike-Timing-Dependent Plasticity and Relevant Mutual Information Maximization , 2003, Neural Computation.
[43] Sheila Nirenberg,et al. Decoding neuronal spike trains: How important are correlations? , 2003, Proceedings of the National Academy of Sciences of the United States of America.
[44] Alexa Riehle,et al. Spike synchronization and firing rate in a population of motor cortical neurons in relation to movement direction and reaction time , 2003, Biological Cybernetics.
[45] Wulfram Gerstner,et al. Mathematical formulations of Hebbian learning , 2002, Biological Cybernetics.
[46] Wulfram Gerstner,et al. Spiking Neuron Models , 2002 .
[47] Rajesh P. N. Rao,et al. Spike-Timing-Dependent Hebbian Plasticity as Temporal Difference Learning , 2001, Neural Computation.
[48] Arnaud Delorme,et al. Networks of integrate-and-fire neuron using rank order coding A: How to implement spike time dependent Hebbian plasticity , 2001, Neurocomputing.
[49] Arnaud Delorme,et al. Networks of integrate-and-fire neurons using Rank Order Coding B: Spike timing dependent plasticity and emergence of orientation selectivity , 2001, Neurocomputing.
[50] G. Bi,et al. Synaptic modification by correlated activity: Hebb's postulate revisited. , 2001, Annual review of neuroscience.
[51] Peter Dayan,et al. Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems , 2001 .
[52] Don H. Johnson,et al. Toward a theory of information processing , 2000, 2000 IEEE International Symposium on Information Theory (Cat. No.00CH37060).
[53] B. Cessac,et al. What Can One Learn About Self-Organized Criticality from Dynamical Systems Theory? , 1999, cond-mat/9912081.
[54] Rajesh P. N. Rao,et al. Predictive Sequence Learning in Recurrent Neocortical Circuits , 1999, NIPS.
[55] R. Nicoll,et al. Long-term potentiation--a decade of progress? , 1999, Science.
[56] A. Riehle,et al. Precise spike synchronization in monkey motor cortex involved in preparation for movement , 1999, Experimental Brain Research.
[57] D. Ruelle. Smooth Dynamics and New Theoretical Ideas in Nonequilibrium Statistical Mechanics , 1998, chao-dyn/9812032.
[58] Bruno Cessac,et al. Self-organization and dynamics reduction in recurrent networks: stimulus presentation and learning , 1998, Neural Networks.
[59] G. Keller. Equilibrium States in Ergodic Theory , 1998 .
[60] R. Lima,et al. Relative Entropy and Identification of Gibbs Measures in Dynamical Systems , 1998 .
[61] J. Zukas. Introduction to the Modern Theory of Dynamical Systems , 1998 .
[62] Francis Comets,et al. Detecting phase transition for Gibbs measures , 1997 .
[63] D. Johnston,et al. Regulation of Synaptic Efficacy by Coincidence of Postsynaptic APs and EPSPs , 1997 .
[64] William Bialek,et al. Spikes: Exploring the Neural Code , 1996 .
[65] P. Collet,et al. Maximum Likelihood and Minimum Entropy Identification of Grammars , 1995, ArXiv.
[66] Y. Klimontovich. Thermodynamics of Chaotic Systems — An introduction , 1994 .
[67] J. Guckenheimer,et al. Bifurcation of the Hodgkin and Huxley equations: A new twist , 1993 .
[68] SM Dudek,et al. Bidirectional long-term modification of synaptic effectiveness in the adult and immature hippocampus , 1993, The Journal of neuroscience : the official journal of the Society for Neuroscience.
[69] W. Singer,et al. Different voltage-dependent thresholds for inducing long-term depression and long-term potentiation in slices of rat visual cortex , 1990, Nature.
[70] W. Parry,et al. Zeta functions and the periodic orbit structure of hyperbolic dynamics , 1990 .
[71] F. Gantmakher,et al. Théorie des matrices , 1990 .
[72] A. Hodgkin,et al. A quantitative description of membrane current and its application to conduction and excitation in nerve , 1990, Bulletin of mathematical biology.
[73] K. Miller,et al. Ocular dominance column development: analysis and simulation. , 1989, Science.
[74] D. Amit. Modelling Brain Function: The World of Attractor Neural Networks , 1989 .
[75] Morris W. Hirsch,et al. Convergent activation dynamics in continuous time networks , 1989, Neural Networks.
[76] Chuanshu Ji,et al. Estimating functionals of one-dimensional Gibbs states , 1989 .
[77] W. Levy,et al. Temporal contiguity requirements for long-term associative potentiation/depression in the hippocampus , 1983, Neuroscience.
[78] A P Georgopoulos,et al. On the relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex , 1982, The Journal of neuroscience : the official journal of the Society for Neuroscience.
[79] E. Bienenstock,et al. Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex , 1982, The Journal of neuroscience : the official journal of the Society for Neuroscience.
[80] D. Mayer,et al. The Ruelle-Araki Transfer Operator in Classical Statistical Mechanics , 1980 .
[81] J. Ko. Sensory discrimination: neural processes preceding discrimination decision. , 1980 .
[82] K. O. Johnson,et al. Sensory discrimination: neural processes preceding discrimination decision. , 1980, Journal of neurophysiology.
[83] R. Bowen. Equilibrium States and the Ergodic Theory of Anosov Diffeomorphisms , 1975 .
[84] 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.
[85] 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.
[86] S. Yoshizawa,et al. An Active Pulse Transmission Line Simulating Nerve Axon , 1962, Proceedings of the IRE.
[87] E. Jaynes. Information Theory and Statistical Mechanics , 1957 .
[88] R. FitzHugh. Mathematical models of threshold phenomena in the nerve membrane , 1955 .
[89] J. Knott. The organization of behavior: A neuropsychological theory , 1951 .
[90] F. Attneave,et al. The Organization of Behavior: A Neuropsychological Theory , 1949 .
[91] E. Adrian,et al. The impulses produced by sensory nerve‐endings , 1926 .