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About 10,000 papers for “Neural coding”

Neural Coding

Barry J. Richmond · Encyclopedia of Computational Neuroscience · 2015

Cited by 0

Neural coding.

Thirza Dado (18510330), Paolo Papale (6456092), Antonio Lozano (13041588) et al. · Figshare · 2024

<p>The transformation between sensory stimuli and brain responses via an intermediate feature space. Neural encoding is factorized into a nonlinear “analysis” and a linear “encoding” mapping. Neural decoding is factorized into a linear “decoding” and a nonlinear “synthesis” mapping.</p>

Cited by 0Open access

Neural Coding

Barry J. Richmond · Encyclopedia of Computational Neuroscience · 2014

Cited by 0

Neural Coding

Kenneth O. Johnson · Neuron · 2000

Cited by 43Open access

Neural Network Coding

Yonina C. Eldar, Andrea Goldsmith, Denız Gündüz et al. · Cambridge University Press eBooks · 2022

A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.

Cited by 0

Neural coding. Principles

Danylo Ulianych, Viacheslav Osaulenko · OSF Preprints (OSF Preprints) · 2018

The project aims to explore principles of information encoding in neural networks

Cited by 0

Relating neural dynamics to neural coding.

Ermentrout, G Bard, F. Galán, Roberto, Urban, Nathan · KiltHub Repository · 2018

We demonstrate that two key theoretical objects used widely in computational neuroscience, the phase-resetting curve (PRC) from dynamics and the spike triggered average (STA) from statistical analysis, are closely related when neurons fire in a nearly regular manner and the stimulus is sufficiently small. We prove that the STA due to injected noisy current is proportional to the derivative of the PRC. We compare these analytic results with numerical calculations for the Hodgkin-Huxley neuron and we apply the method to neurons in the olfactory bulb of mice. This observation allows us to relate the stimulus-response properties of a neuron to its dynamics, bridging the gap between dynamical and information theoretic approaches to understanding brain computations and facilitating the interpretation of changes in channels and other cellular properties as influencing the representation of stimuli.

Cited by 0Open access

Relating Neural Dynamics to Neural Coding

G. Bard Ermentrout, Roberto F. Galán, Nathaniel N. Urban · Physical Review Letters · 2007

We demonstrate that two key theoretical objects used widely in computational neuroscience, the phase-resetting curve (PRC) from dynamics and the spike triggered average (STA) from statistical analysis, are closely related when neurons fire in a nearly regular manner and the stimulus is sufficiently small. We prove that the STA due to injected noisy current is proportional to the derivative of the PRC. We compare these analytic results with numerical calculations for the Hodgkin-Huxley neuron and we apply the method to neurons in the olfactory bulb of mice. This observation allows us to relate the stimulus-response properties of a neuron to its dynamics, bridging the gap between dynamical and information theoretic approaches to understanding brain computations and facilitating the interpretation of changes in channels and other cellular properties as influencing the representation of stimuli.

Cited by 104Open access

Neural Coding and Entropy

Christof Koch · 1999

0.8> ffit (typically, ffit = 1 msec). This implies that we can characterize the entire response of the cell via a point process and only need to keep track of the times t i the impulses were generated. Typically, these times are written as series of delta function P i ffi(t \\Gamma t i ). In the following, we will abbreviate such a spike train as ft i g. Receptive fields and tuning curves then correspond to measuring the conditional probability P [ft i gj s(t)] : (1) The most complete description of the behavior of the neuron as a function of the stimulus is of course the joint probability distribution P [ft i g; s(t)] : (2) 1<F

Cited by 0