Single Neuron Computation: From Dynamical System to Feature Detector

DOI10.1162/NECO.2007.19.12.3133zbMath1130.92005arXivq-bio/0612025OpenAlexW1996954534WikidataQ51624823 ScholiaQ51624823MaRDI QIDQ5441729

Blaise Agüera y Arcas, Adrienne L. Fairhall, Sungho Hong

Publication date: 15 February 2008

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/q-bio/0612025

zbMATH Keywords

Hodgkin-Huxley modelthreshold crossing modelsAbbott-Kepler model

Mathematics Subject Classification ID

Dynamical systems in biology (37N25) Neural networks for/in biological studies, artificial life and related topics (92B20) Applications of stochastic analysis (to PDEs, etc.) (60H30)

Related Items (9)

Stimulus features, resetting curves, and the dependence on adaptation ⋮ Type I and Type II Neuron Models Are Selectively Driven by Differential Stimulus Features ⋮ Modulation of spike and burst rate in a minimal neuronal circuit with feed-forward inhibition ⋮ Learning Quadratic Receptive Fields from Neural Responses to Natural Stimuli ⋮ Identifying Functional Bases for Multidimensional Neural Computations ⋮ Functional Identification of Spike-Processing Neural Circuits ⋮ Two Computational Regimes of a Single-Compartment Neuron Separated by a Planar Boundary in Conductance Space ⋮ Context-dependent coding in single neurons ⋮ Feature Selection in Simple Neurons: How Coding Depends on Spiking Dynamics

Cites Work

• Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Encoding Model
• Computation in a Single Neuron: Hodgkin and Huxley Revisited
• What Causes a Neuron to Spike?

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