- Biol Cybern (2008) 98:459478 DOI 10.1007/s00422-008-0233-1
- This article was originally published in the Encyclopedia of Neuroscience published by Elsevier, and the attached copy is provided by Elsevier for the
- HOMEOSTASIS AND LEARNING THROUGH SPIKE-TIMING DEPENDENT L.F. Abbott(1)
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- Supplementary Material A1: Evolution of the average synaptic update rule
- A connectionist model of spatial learning in the rat Thomas Strosslin
- Dynamic I-V Curves Are Reliable Predictors of Naturalistic Pyramidal-Neuron Voltage Traces
- Journal of Neuroscience Methods 169 (2008) 417424 A benchmark test for a quantitative assessment
- Prediction Target and Loss Function for Competition
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- Behavioral/Systems/Cognitive Triplets of Spikes in a Model of
- Statistics of subthreshold neuronal voltage fluctuations due to conductance-based synaptic shot noise
- Noise-enhanced computation in a model of a cortical column
- LETTER Communicated by Peter Konig Extracting Oscillations: Neuronal Coincidence Detection
- LETTER Communicated by Laurence Abbott Intrinsic Stabilization of Output Rates by Spike-Based
- Journal of Computational Neuroscience 12, 8395, 2002 c 2002 Kluwer Academic Publishers. Manufactured in The Netherlands.
- Neurocomputing 6566 (2005) 685690 Subthreshold cross-correlations between cortical
- To appear in Proc. of the 18th Joint International Conference on Artificial Intelligence August 9-15, 2003, Acapulco, Mexico
- Reinforcement Learning in Continuous State and Action Space
- Signal buffering in random networks of spiking neurons: Microscopic versus macroscopic phenomena
- Spike-Timing Dependent Plasticity and Mutual Information Maximization for a Spiking Neuron
- Biol Cybern (2008) 99:417426 DOI 10.1007/s00422-008-0261-x
- Modeling Spatial and Temporal Aspects of Visual Backward Masking Frouke Hermens, Gediminas Luksys,
- The Spike Response Model: A Framework to Predict Neuronal Spike Trains
- Development/Plasticity/Repair Functional Requirements for Reward-Modulated
- Frontiers in Synaptic Neuroscience www.frontiersin.org December 2010 | Volume 2 | Article 151 | 1 SYNAPTIC NEUROSCIENCE
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- Stress, genotype and norepinephrine in the prediction of mouse behavior using reinforcement learning
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- Hebbian learning and spiking neurons Richard Kempter*
- Predicting spike times of a detailed conductance-based neuron model driven by stochastic spike arrival
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- This article was published in an Elsevier journal. The attached copy is furnished to the author for non-commercial research and
- Optimality Model of Unsupervised Spike-Timing Dependent Plasticity
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- Optimal Spike-Timing Dependent Plasticity for Precise Action Potential Firing
- Synaptic shot noise and conductance fluctuations affect the membrane voltage with equal significance.
- Brain-Actuated Interaction Jos del R. Millna,b,*, Frdric Renkensb
- Transient information flow in a network of excitatory and inhibitory model neurons: Role of noise and signal autocorrelation
- Integrate-and-Fire Neurons and Networks Wulfram Gerstner
- INSTITUTE OF PHYSICS PUBLISHING NETWORK: COMPUTATION IN NEURAL SYSTEMS Network: Comput. Neural Syst. 12 (2001) 409421 PII: S0954-898X(01)24073-1
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- Memoire associative 6.1 La t^ache
- ARTICLE Communicated by Carl van Vreeswijk Population Dynamics of Spiking Neurons: Fast Transients,
- Spike-triggered averages for passive and resonant neurons receiving filtered excitatory and inhibitory synaptic drive
- Piecewise linear differential equations and integrate-and-fire neurons: Insights from two-dimensional membrane models
- Online Processing of Multiple Inputs in a Sparsely-Connected Recurrent Neural Network
- Abstract. Several formulations of correlation-based Hebbian learning are reviewed. On the presynaptic side,
- LETTER Communicated by Adi Bulsara Noise in Integrate-and-Fire Neurons: From Stochastic Input to
- 2001 Special issue Coding properties of spiking neurons: reverse and cross-correlations
- |Research Focus Coding and learning of behavioral sequences
- Biol Cybern (2008) 99:361370 DOI 10.1007/s00422-008-0259-4
- Non-Invasive Brain-Actuated Control of a Mobile Robot by Human EEG
- J Comput Neurosci (2006) 21:3549 DOI 10.1007/s10827-006-7074-5
- Optimal Hebbian Learning: A Probabilistic Point of View
- Spike-timing dependent plasticity J. Sjostrom and W. Gerstner
- A Short Introduction Hopfield Model
- Hebbian Learning and Plasticity Wulfram Gerstner
- Can We Predict Every Spike? Book chapter to appear in Spike Timing: Mechanisms and Function, Edited by Patricia M.
