A survey of signal propagation in feedforward neuronal networks

Authors:
Daqing Guo
Affiliations:
School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, People's Republic of China
Venue:
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part I
Year:
2011

Citing 4
Cited 0

Spiking Neuron Models: An Introduction

Spiking Neuron Models: An Introduction
Duality of rate coding and temporal coding in multilayered feedforward networks

Neural Computation
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems

Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Signal propagation in feedforward neuronal networks with unreliable synapses

Journal of Computational Neuroscience

Quantified Score

Hi-index	0.00

Visualization

Abstract

Understanding how neural activities are propagated through different brain regions is a critical and fundamental problem in neuroscience. A simple model for this type of signal propagation is the feedforward neuronal network, in which each neuron in a given layer only receives synaptic signals from neurons in its previous layer. This paper introduces and reviews the basic modeling framework about the signal propagation, two neural activities propagation modes, and several recent important results about signal propagation in the feedforward neuronal networks. Furthermore, a more generalized modeling framework based on unreliable synapses is also proposed and discussed.