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Multiprocessing neural network simulator

Multiprocessing neural network simulator
Multiprocessing neural network simulator
Over the last few years tremendous progress has been made in neuroscience by employing simulation tools for investigating neural network behaviour. Many simulators have been created during last few decades, and their number and set of features continually grows due to persistent interest from groups of researchers and engineers.

A simulation software that is able to simulate a large-scale neural network has been developed and presented in this work. Based on a highly abstract integrate-and-fire neuron model a clock-driven sequential simulator has been developed in C++. The created program is able to associate the input patterns with the output patterns. The novel biologically plausible learning mechanism uses Long Term Potentiation and Long Term Depression to change the strength of the connections between the neurons based on a global binary feedback.

Later, the sequentially executed model has been extended to a multi-processor system, which executes the described learning algorithm using the event-driven technique on a parallel distributed framework, simulating a neural network asynchronously. This allows the simulation to manage larger scale neural networks being immune to processor failure and communication problems.

The multi-processor neural network simulator has been created, the main benefit of which is the possibility to simulate large scale neural networks using high-parallel distributed computing. For that reason the design of the simulator has been implemented considering an efficient weight-adjusting algorithm and an efficient way for asynchronous local communication between processors.
Kulakov, Anton
274cbc43-2cab-495c-910b-1a64cec81df6
Kulakov, Anton
274cbc43-2cab-495c-910b-1a64cec81df6
Zwolinski, Mark
adfcb8e7-877f-4bd7-9b55-7553b6cb3ea0

(2013) Multiprocessing neural network simulator. University of Southampton, Faculy of Physical & Applied Science, Doctoral Thesis, 160pp.

Record type: Thesis (Doctoral)

Abstract

Over the last few years tremendous progress has been made in neuroscience by employing simulation tools for investigating neural network behaviour. Many simulators have been created during last few decades, and their number and set of features continually grows due to persistent interest from groups of researchers and engineers.

A simulation software that is able to simulate a large-scale neural network has been developed and presented in this work. Based on a highly abstract integrate-and-fire neuron model a clock-driven sequential simulator has been developed in C++. The created program is able to associate the input patterns with the output patterns. The novel biologically plausible learning mechanism uses Long Term Potentiation and Long Term Depression to change the strength of the connections between the neurons based on a global binary feedback.

Later, the sequentially executed model has been extended to a multi-processor system, which executes the described learning algorithm using the event-driven technique on a parallel distributed framework, simulating a neural network asynchronously. This allows the simulation to manage larger scale neural networks being immune to processor failure and communication problems.

The multi-processor neural network simulator has been created, the main benefit of which is the possibility to simulate large scale neural networks using high-parallel distributed computing. For that reason the design of the simulator has been implemented considering an efficient weight-adjusting algorithm and an efficient way for asynchronous local communication between processors.

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More information

Published date: January 2013
Organisations: University of Southampton, EEE

Identifiers

Local EPrints ID: 348420
URI: http://eprints.soton.ac.uk/id/eprint/348420
PURE UUID: e2a59546-214c-488f-a747-9048c01747f8
ORCID for Mark Zwolinski: ORCID iD orcid.org/0000-0002-2230-625X

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Date deposited: 28 Feb 2013 15:12
Last modified: 06 Jun 2018 13:14

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