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
     
 

ISBN-10: 0262511002

ISBN-13: 9780262511001

Pub. Date: 07/25/1996

Publisher: MIT Press

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

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.

Product Details

ISBN-13:
9780262511001
Publisher:
MIT Press
Publication date:
07/25/1996
Series:
Computational Neuroscience Series
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
1 Deciphering the Brain's Codes
Masakazu Konishi
2 A Neural Network for Coding of Trajectories by Time
Series of Neuronal Population Vectors
AleXander V. Lukashin and Apostolos P. Georgopoulos
3 SelfOrganization of Firing Activities in Monkey's Motor
CorteX: Trajectory Computation from Spike Signals
Siming Lin, Jennie Si, and A.B. Schwartz
4 Theoretical Considerations for the Analysis of Population
Coding in Motor CorteX
Terence D. Sanger
5 Statistically Efficient Estimation Using Population
Coding
AleXandre Pouget, Kechen Zhang, Sophie Deneve, and Peter
E. Latham

6 Parameter EXtraction from Population Codes: A Critical
Assessment
Herman P. Snippe
7 Energy Efficient Neural Codes
William B. Levy and Robert A. BaXter
8 Seeing Beyond the Nyquist Limit
Daniel L. Ruderman and William Bialek
9 A Model of Spatial Map Formation in the Hippocampus of
the Rat
Kenneth I. Blum and L.F. Abbott
10 Probabilistic Interpretation of Population Codes
Richard S. Zemel, Peter Dayan, and AleXandre Pouget
11 Cortical Cells Should Fire Regularly, But Do Not
William R. Softky and Christof Koch
12 Role of Temporal Integration and Fluctuation Detection
in the Highly Irregular Firing of a Leaky Integrator Neuron Model
with Partial Reset
Guido Bugmann, Chris Christodoulou, and John G. Taylor
13 Physiological Gain Leads to High ISI Variability in a
Simple Model of a Cortical Regular Spiking Cell
Todd W. Troyer and Kenneth D. Miller
14 Coding of TimeVarying Signals in Spike Trains on
IntegrateandFire Neuronswith Random Threshold
Fabrizio Gabbiani and Christof Koch
15 Temporal Precision of Spike Trains in EXtrastriate
CorteX of the Behaving Macaque Monkey
Wyeth Bair and Christof Koch
16 Conversion of Temporal Correlations Between Stimuli to
Spatial Correlations Between Attractors
M. Griniasty, M.V. Tsodyks, and Daniel J. Amit
17 Neural Network Model of the Cerebellum: Temporal
Discrimination and the Timing of Motor Responses
Dean V. Buonomano and Michael D. Mauk
18 Gamma Oscillation Model Predicts Intensity Coding by
Phase Rather than Frequency
Roger D. Traub, Miles A. Whittington, and John
G.R. Jefferys

19 Effects of Input Synchrony on the Firing Rate of a
ThreeConductance Cortical Neuron Model
Venkatesh N. Murthy and Eberhard E. Fetz
20 NMDABased Pattern Discrimination in a Modeled Cortical
Neuron
Bartlett W. Mel
21 The Impact of Parallel Fiber Background Activity in the
Cable Properties of Cerebellar Purkinje Cells
Moshe Rapp, Yosef Yarom, and Idan Segev
IndeX

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