Autonomous navigational controller inspired by the hippocampus

Maizura Mokhtar; David M. Halliday; Andy M. Tyrrell

Autonomous navigational controller inspired by the hippocampus

Maizura Mokhtar, David M. Halliday, Andy M. Tyrrell

Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This study uses models of pyramidal neurons in the hippocampus to design a hardware spiking neural network neuro-controller model for the purpose of navigation. The neural network model consists of individual neurons modeled using the two-dimensional bio-inspired Izhikevich algorithm. The network is connected according to the connectivity within the hippocampus region, as this region is one of the regions in the brain that is responsible for path navigation. The information processed by the model helps provide navigation and creates memories. The neural network model is intended to be implemented onto a Field Programmable Gate Array (FPGA) device. This eliminates the need of an operating system to run the network, thus achieving autonomy.

Original language	English
Title of host publication	2007 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-6
Place of Publication	NEW YORK
Publisher	IEEE
Pages	813-818
Number of pages	6
ISBN (Print)	978-1-4244-1379-9
Publication status	Published - 2007
Event	International Joint Conference on Neural Networks - Orlando Duration: 12 Aug 2007 → 17 Aug 2007

Conference

Conference	International Joint Conference on Neural Networks
City	Orlando
Period	12/08/07 → 17/08/07

Keywords

neural network architecture
neural network hardware
MODEL
OSCILLATIONS
ROBOTS

Cite this

@inproceedings{14a1d824b9494f24b59fef88567468d9,

title = "Autonomous navigational controller inspired by the hippocampus",

abstract = "This study uses models of pyramidal neurons in the hippocampus to design a hardware spiking neural network neuro-controller model for the purpose of navigation. The neural network model consists of individual neurons modeled using the two-dimensional bio-inspired Izhikevich algorithm. The network is connected according to the connectivity within the hippocampus region, as this region is one of the regions in the brain that is responsible for path navigation. The information processed by the model helps provide navigation and creates memories. The neural network model is intended to be implemented onto a Field Programmable Gate Array (FPGA) device. This eliminates the need of an operating system to run the network, thus achieving autonomy.",

keywords = "neural network architecture, neural network hardware, MODEL, OSCILLATIONS, ROBOTS",

author = "Maizura Mokhtar and Halliday, {David M.} and Tyrrell, {Andy M.}",

year = "2007",

language = "English",

isbn = "978-1-4244-1379-9",

pages = "813--818",

booktitle = "2007 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-6",

publisher = "IEEE",

address = "United States",

note = "International Joint Conference on Neural Networks ; Conference date: 12-08-2007 Through 17-08-2007",

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TY - GEN

T1 - Autonomous navigational controller inspired by the hippocampus

AU - Mokhtar, Maizura

AU - Halliday, David M.

AU - Tyrrell, Andy M.

PY - 2007

Y1 - 2007

N2 - This study uses models of pyramidal neurons in the hippocampus to design a hardware spiking neural network neuro-controller model for the purpose of navigation. The neural network model consists of individual neurons modeled using the two-dimensional bio-inspired Izhikevich algorithm. The network is connected according to the connectivity within the hippocampus region, as this region is one of the regions in the brain that is responsible for path navigation. The information processed by the model helps provide navigation and creates memories. The neural network model is intended to be implemented onto a Field Programmable Gate Array (FPGA) device. This eliminates the need of an operating system to run the network, thus achieving autonomy.

AB - This study uses models of pyramidal neurons in the hippocampus to design a hardware spiking neural network neuro-controller model for the purpose of navigation. The neural network model consists of individual neurons modeled using the two-dimensional bio-inspired Izhikevich algorithm. The network is connected according to the connectivity within the hippocampus region, as this region is one of the regions in the brain that is responsible for path navigation. The information processed by the model helps provide navigation and creates memories. The neural network model is intended to be implemented onto a Field Programmable Gate Array (FPGA) device. This eliminates the need of an operating system to run the network, thus achieving autonomy.

KW - neural network architecture

KW - neural network hardware

KW - MODEL

KW - OSCILLATIONS

KW - ROBOTS

M3 - Conference contribution

SN - 978-1-4244-1379-9

SP - 813

EP - 818

BT - 2007 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-6

PB - IEEE

CY - NEW YORK

T2 - International Joint Conference on Neural Networks

Y2 - 12 August 2007 through 17 August 2007

ER -