Neural Comp. Sign up for ETOCS
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
 QUICK SEARCH:   [advanced]


     


This Article
Right arrow Full Text
Right arrow Full Text (PDF)
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Knight, B. W.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Knight, B. W.
(Neural Computation. 2000;12:473-518.)
© 2000 The MIT Press

Dynamics of Encoding in Neuron Populations: Some General Mathematical Features

Bruce W. Knight

Laboratory of Biophysics, Rockefeller University, and Laboratory of Applied Mathematics, Mount Sinai Medical School, New York University, New York, NY 10021, U.S.A.

The use of a population dynamics approach promises efficient simulation of large assemblages of neurons. Depending on the issues addressed and the degree of realism incorporated in the simulated neurons, a wide range of different population dynamics formulations can be appropriate. Here we present a common mathematical structure that these various formulations share and that implies dynamical behaviors that they have in common. This underlying structure serves as a guide toward efficient means of simulation. As an example, we derive the general population firing-rate frequency-response and show how it may be used effectively to address a broad range of interacting-population response and stability problems. A few specific cases will be worked out. A summary of this work appears at the end, before the appendix.




This article has been cited by other articles:


Home page
Neural Comput.Home page
L. Sirovich
Populations of Tightly Coupled Neurons: The RGC/LGN System
Neural Comput., May 1, 2008; 20(5): 1179 - 1210.
[Abstract] [Full Text] [PDF]


Home page
Neural Comput.Home page
E. Muller, L. Buesing, J. Schemmel, and K. Meier
Spike-Frequency Adapting Neural Ensembles: Beyond Mean Adaptation and Renewal Theories
Neural Comput., November 1, 2007; 19(11): 2958 - 3010.
[Abstract] [Full Text] [PDF]


Home page
Neural Comput.Home page
C. Ly and D. Tranchina
Critical analysis of dimension reduction by a moment closure method in a population density approach to neural network modeling.
Neural Comput., August 1, 2007; 19(8): 2032 - 2092.
[Abstract] [Full Text] [PDF]


Home page
Neural Comput.Home page
N. Masuda, B. Doiron, A. Longtin, and K. Aihara
Coding of Temporally Varying Signals in Networks of Spiking Neurons with Global Delayed Feedback
Neural Comput., October 1, 2005; 17(10): 2139 - 2175.
[Abstract] [Full Text] [PDF]


Home page
Proc. Natl. Acad. Sci. USAHome page
D. Cai, L. Tao, M. Shelley, and D. W. McLaughlin
An effective kinetic representation of fluctuation-driven neuronal networks with application to simple and complex cells in visual cortex
PNAS, May 18, 2004; 101(20): 7757 - 7762.
[Abstract] [Full Text] [PDF]


Home page
Neural Comput.Home page
C. R. Laing and A. Longtin
Dynamics of Deterministic and Stochastic Paired Excitatory-Inhibitory Delayed Feedback
Neural Comput., December 1, 2003; 15(12): 2779 - 2822.
[Abstract] [Full Text] [PDF]


Home page
Neural Comput.Home page
M. de Kamps
A Simple and Stable Numerical Solution for the Population Density Equation
Neural Comput., September 1, 2003; 15(9): 2129 - 2146.
[Abstract] [Full Text]


Home page
Neural Comput.Home page
A.R.R. Casti, A. Omurtag, A. Sornborger, E. Kaplan, B. Knight, J. Victor, and L. Sirovich
A Population Study of Integrate-and-Fire-or-Burst Neurons
Neural Comput., May 1, 2002; 14(5): 957 - 986.
[Abstract] [Full Text]


Home page
Neural Comput.Home page
J. Eggert and J. L. van Hemmen
Modeling Neuronal Assemblies: Theory and Implementation
Neural Comput., September 1, 2001; 13(9): 1923 - 1974.
[Abstract] [Full Text] [PDF]


Home page
Neural Comput.Home page
D. Q. Nykamp and D. Tranchina
A Population Density Approach That Facilitates Large-Scale Modeling of Neural Networks: Extension to Slow Inhibitory Synapses
Neural Comput., March 1, 2001; 13(3): 511 - 546.
[Abstract] [Full Text]




HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
J COGNITIVE NEUROSCIENCE NEURAL COMPUTATION MIT PRESS JOURNALS
Copyright © 2000 by The MIT Press.