Statistics of spike trains in conductance-based neural networks: rigorous results

DOI10.1186/2190-8567-1-8zbMath1259.92006arXiv1104.3795OpenAlexW2963935875WikidataQ41198086 ScholiaQ41198086MaRDI QIDQ1941941

Bruno Cessac

Publication date: 22 March 2013

Published in: The Journal of Mathematical Neuroscience (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1104.3795

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Neural biology (92C20)

Related Items (5)

Spike Train Statistics from Empirical Facts to Theory: The Case of the Retina ⋮ Partially observed Markov random fields are variable neighborhood random fields ⋮ Dynamics and spike trains statistics in conductance-based integrate-and-fire neural networks with chemical and electric synapses ⋮ Spatio-temporal spike train analysis for large scale networks using the maximum entropy principle and Monte Carlo method ⋮ Linear response in neuronal networks: From neurons dynamics to collective response

Cites Work

• How Gibbs distributions may naturally arise from synaptic adaptation mechanisms. A model-based argumentation
• Smooth dynamics and new theoretical ideas in nonequilibrium statistical mechanics
• Chains with complete connections: general theory, uniqueness, loss of memory and mixing properties
• Information Theory and Statistical Mechanics
• A VIEW OF NEURAL NETWORKS AS DYNAMICAL SYSTEMS
• Criteria for $\bar {d}$-continuity
• Chaos in the Hodgkin--Huxley Model
• Analytical Integrate-and-Fire Neuron Models with Conductance-Based Dynamics for Event-Driven Simulation Strategies

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