Brain-like Information Processing for Spatio-Temporal Pattern Recognition

Abstract

Information processes in the brain, such as gene and protein expression, learning, memory, perception, cognition, consciousness are all spatio- and/or spectro temporal. Modelling such processes would require sophisticated information science methods and the best ones could be the brain-inspired ones, that use the same brain information processing principles. Spatio and spectro-temporal data (SSTD) are also the most common types of data collected in many domain areas, including engineering, bioinformatics, neuroinformatics, ecology, environment, medicine, economics, etc. However, there is lack of methods for the efficient analysis of such data and for spatio-temporal pattern recognition (STPR). The brain functions as a spatio-temporal information processing machine and deals extremely well with spatio-temporal data. Its organization and functions have been the inspiration for the development of new methods for SSTD analysis and STPR. Brain-inspired spiking neural networks (SNN) are considered the third generation of neural networks and are a promising paradigm for the creation of new intelligent ICT for SSTD. This new generation of computational models and systems is potentially capable of modeling complex information processes due to the ability to represent and integrate different information dimensions, such as time, space, frequency, and phase, and to deal with large volumes of data in an adaptive and self-organizing manner. This chapter reviews methods and systems of SNN for SSTD analysis and STPR, including single neuronal models, evolving spiking neural networks (eSNN), and computational neurogenetic models (CNGM). Software and hardware implementations and some pilot applications for audio-visual pattern recognition, EEG data-analysis, cognitive robotic systems, BCI, neurodegenerative diseases, and others are discussed.

Keywords

Gene Regulatory Network Spike Activity Synaptic Weight Synaptic Efficacy Spike Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Abbreviations

AER

address event representation

AMPA

α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid

AMPAR

(amino-methylisoxazole-propionic acid) receptor

ANN

artificial neural network

BCI

brain-computer interface

CLC

chloride channel

CNGM

computational neurogenetic model

EEG

electroencephalography

FPGA

field-programmable gate array

GABAAR

GABAA receptor

GABABR

GABAB receptor

GABRA

GABAA receptor

GABRB

GABAB receptor

GDP

guanosine diphosphate

GRN

gene regulatory network

HMM

hidden Markov model

IBM

individual-based model

IFM

integrate-and-fire model

KCN

kalium (potassium) voltage-gated channel

LDA

linear discriminant analysis

LIF

leaky integrate-and-fire neuron

LIFM

leaky IFM

LSM

liquid state machine

LTD

long-term depression

LTP

long-term potentiation

MLP

multilayer perceptron

NMDA

N-methyl-d-aspartate

NMDR

(N-methyl-d-aspartate acid) NMDA receptor

PCA

principle component analysis

PSO

particle swarm optimization

PSP

post-synaptic potential

ReSuMe

remote supervised method

SCN

sodium voltage-gated channel

SDSP

spike driven synaptic plasticity

SNN

spiking neural network

SRM

spike response model

SSTD

spatio and spectro-temporal data

STDP

spike-timing dependent plasticity

STPR

spatio-temporal pattern recognition

eSNN

evolving spiking neural network

fMRI

functional magnetic resonance imaging

mRNA

messenger RNA

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Copyright information

© Springer-Verlag 2014

Authors and Affiliations

  1. 1.KEDRI – Knowledge Engineering and Discovery Research InstituteAuckland University of TechnologyAucklandNew Zealand

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