Fast neural network simulations with population density methods

Duane Q. Nykamp; Daniel Tranchina

doi:10.1016/S0925-2312(00)00203-4

Fast neural network simulations with population density methods

Duane Q. Nykamp, Daniel Tranchina

Research output: Contribution to journal › Conference article › peer-review

Abstract

The complexity of neural networks of the brain makes studying these networks through computer simulation challenging. Conventional methods, where one models thousands of individual neurons, can take enormous amounts of computer time even for models of small cortical areas. An alternative is the population density method in which neurons are grouped into large populations and one tracks the distribution of neurons over state space for each population. We discuss the method in general and illustrate the technique for integrate-and-fire neurons. (C) 2000 Elsevier Science B.V. All rights reserved.

Original language	English (US)
Pages (from-to)	487-492
Number of pages	6
Journal	Neurocomputing
Volume	32-33
DOIs	https://doi.org/10.1016/S0925-2312(00)00203-4
State	Published - Jun 2000
Event	The 8th Annual Computational Neuroscience Meeting (CNS'99) - Pittsburgh, PA, USA Duration: Jul 18 1999 → Jul 22 1999

Keywords

Computer simulation
Network modeling
Populations
Probability density function

ASJC Scopus subject areas

Computer Science Applications
Cognitive Neuroscience
Artificial Intelligence

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10.1016/S0925-2312(00)00203-4

Cite this

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title = "Fast neural network simulations with population density methods",

abstract = "The complexity of neural networks of the brain makes studying these networks through computer simulation challenging. Conventional methods, where one models thousands of individual neurons, can take enormous amounts of computer time even for models of small cortical areas. An alternative is the population density method in which neurons are grouped into large populations and one tracks the distribution of neurons over state space for each population. We discuss the method in general and illustrate the technique for integrate-and-fire neurons. (C) 2000 Elsevier Science B.V. All rights reserved.",

keywords = "Computer simulation, Network modeling, Populations, Probability density function",

author = "Nykamp, {Duane Q.} and Daniel Tranchina",

year = "2000",

month = jun,

doi = "10.1016/S0925-2312(00)00203-4",

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volume = "32-33",

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journal = "Neurocomputing",

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AU - Nykamp, Duane Q.

AU - Tranchina, Daniel

PY - 2000/6

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N2 - The complexity of neural networks of the brain makes studying these networks through computer simulation challenging. Conventional methods, where one models thousands of individual neurons, can take enormous amounts of computer time even for models of small cortical areas. An alternative is the population density method in which neurons are grouped into large populations and one tracks the distribution of neurons over state space for each population. We discuss the method in general and illustrate the technique for integrate-and-fire neurons. (C) 2000 Elsevier Science B.V. All rights reserved.

AB - The complexity of neural networks of the brain makes studying these networks through computer simulation challenging. Conventional methods, where one models thousands of individual neurons, can take enormous amounts of computer time even for models of small cortical areas. An alternative is the population density method in which neurons are grouped into large populations and one tracks the distribution of neurons over state space for each population. We discuss the method in general and illustrate the technique for integrate-and-fire neurons. (C) 2000 Elsevier Science B.V. All rights reserved.

KW - Computer simulation

KW - Network modeling

KW - Populations

KW - Probability density function

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DO - 10.1016/S0925-2312(00)00203-4

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AN - SCOPUS:0033912659

SN - 0925-2312

VL - 32-33

SP - 487

EP - 492

JO - Neurocomputing

JF - Neurocomputing

T2 - The 8th Annual Computational Neuroscience Meeting (CNS'99)

Y2 - 18 July 1999 through 22 July 1999

ER -

Fast neural network simulations with population density methods

Abstract

Keywords

ASJC Scopus subject areas

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