An artificial chaotic spiking neuron inspired by spiral ganglion cell: Paralleled spike encoding, theoretical analysis, and electronic circuit implementation

An artificial chaotic spiking neuron inspired by spiral ganglion cell: Paralleled spike encoding, theoretical analysis, and electronic circuit implementation

A novel chaotic spiking neuron is presented and its nonlinear dynamics and encoding functions are analyzed. A set of paralleled N neurons accepts a common analog input and outputs a set of N chaotic spike-trains. Three theorems which guarantee that the neurons can encode the analog input into a summ...

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Bibliographic Details
Published in:Neural networks Vol. 22; no. 5; pp. 664 - 673
Main Authors: Torikai, Hiroyuki, Nishigami, Toru
Format: Journal Article Conference Proceeding
Language:English
Published: Kidlington Elsevier Ltd 01-07-2009
Elsevier
Subjects:
Action Potentials
Algorithms
Applied sciences
Artificial intelligence
Chaos
Coding, codes
Computer science; control theory; systems
Computer Simulation
Connectionism. Neural networks
Exact sciences and technology
Humans
Information, signal and communications theory
Models, Neurological
Neural Networks (Computer)
Neurons - physiology
Nonlinear Dynamics
Pulse coding
Signal and communications theory
Spiking neuron
Spiral Ganglion - physiology
Spiral ganglion cell
Telecommunications and information theory
Chaos
Spiking neuron
Pulse coding
Spiral ganglion cell
Input output
Histogram
Mimic
Theory
Electronic circuit
Iterative method
Neural network
Implementation
Time resolution
Coding
SPICE
Numerical simulation
Summation
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Summary:A novel chaotic spiking neuron is presented and its nonlinear dynamics and encoding functions are analyzed. A set of paralleled N neurons accepts a common analog input and outputs a set of N chaotic spike-trains. Three theorems which guarantee that the neurons can encode the analog input into a summation of the N chaotic spike-trains are derived: (1) a spike histogram of the summed spike-train can mimic waveforms of various inputs, (2) the spike-trains do not synchronize to each other and thus the summed spike-train can have N times higher encoding resolution than each single spike-train, and (3) firing rates of the neurons can be adjusted by internal parameters. The theorems are proven by using nonlinear iterative maps and are confirmed by numerical simulations as well. Electronic circuit implementation methods of the paralleled neurons are also presented and typical paralleled encoding functions are confirmed by both experimental measurements and SPICE simulations.
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ISSN:0893-6080
1879-2782
DOI:10.1016/j.neunet.2009.06.023