Neural Codes and Distributed Representations: Foundations of Neural Computation / Edition 1

Neural Codes and Distributed Representations: Foundations of Neural Computation / Edition 1

by Laurence F. Abbott
     
 

Since its founding in 1989 by Terrence Sejnowski, Neural
Computation
has become the leading journal in the field.
Foundations of Neural Computation collects, by topic, the most significant papers that have appeared in the journal over the past nine years.

The present volume focuses on neural codes and representations, topics of broad

See more details below

Overview

Since its founding in 1989 by Terrence Sejnowski, Neural
Computation
has become the leading journal in the field.
Foundations of Neural Computation collects, by topic, the most significant papers that have appeared in the journal over the past nine years.

The present volume focuses on neural codes and representations, topics of broad interest to neuroscientists and modelers. The topics addressed are: how neurons encode information through action potential firing patterns, how populations of neurons represent information, and how individual neurons use dendritic processing and biophysical properties of synapses to decode spike trains. The papers encompass a wide range of levels of investigation, from dendrites and neurons to networks and systems.

The MIT Press

Product Details

ISBN-13:
9780262511001
Publisher:
MIT Press
Publication date:
07/25/1996
Series:
Computational Neuroscience
Edition description:
First Edition
Pages:
369
Product dimensions:
6.00(w) x 8.90(h) x 0.90(d)
Age Range:
18 Years

Table of Contents

Introduction
1Deciphering the Brain's Codes1
2A Neural Network for Coding of Trajectories by Time Series of Neuronal Population Vectors19
3Self-Organization of Firing Activities in Monkey's Motor Cortex: Trajectory Computation from Spike Signals29
4Theoretical Considerations for the Analysis of Population Coding in Motor Cortex45
5Statistically Efficient Estimation Using Population Coding55
6Parameter Extraction from Population Codes: A Critical Assessment85
7Energy Efficient Neural Codes105
8Seeing Beyond the Nyquist Limit119
9A Model of Spatial Map Formation in the Hippocampus of the Rat129
10Probabilistic Interpretation of Population Codes139
11Cortical Cells Should Fire Regularly, But Do Not167
12Role of Temporal Integration and Fluctuation Detection in the Highly Irregular Firing of a Leaky Integrator Neuron Model with Partial Reset171
13Physiological Gain Leads to High ISI Variability in a Simple Model of a Cortical Regular Spiking Cell187
14Coding of Time-Varying Signals in Spike Trains of Integrate-and-Fire Neurons with Random Threshold201
15Temporal Precision of Spike Trains in Extrastriate Cortex of the Behaving Macaque Monkey225
16Conversion of Temporal Correlations Between Stimuli to Spatial Correlations Between Attractors243
17Neural Network Model of the Cerebellum: Temporal Discrimination and the Timing of Motor Responses261
18Gamma Oscillation Model Predicts Intensity Coding by Phase Rather than Frequency279
19Effects of Input Synchrony on the Firing Rate of a Three-Conductance Cortical Neuron Model293
20NMDA-Based Pattern Discrimination in a Modeled Cortical Neuron309
21The Impact of Parallel Fiber Background Activity on the Cable Properties of Cerebellar Purkinje Cells325
Index341

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