Nonlinear Dynamics, Quasi-Periodic Summation, Self-Oscillating Processes, and Information Coding in Selective Spiking Neural Networks

Nonlinear dynamics, physical processes, and information processing in selective spiking neurons are investigated. Summation of pulse inputs are considered on the basis of the theory of quasi-periodic functions and nonlinear transformation via relaxation of the self-oscillating system of a neuron. A...

Full description

Saved in:
Bibliographic Details
Published in:Bulletin of the Russian Academy of Sciences. Physics 2018-11, Vol.82 (11), p.1425-1430
Main Author: Mazurov, M. E.
Format: Article
Language:English
Subjects:
Coding
Data processing
Dynamical systems
Hadrons
Heavy Ions
Neural networks
Nonlinear dynamics
Nuclear Physics
Periodic functions
Physics
Physics and Astronomy
Spiking
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Nonlinear dynamics, physical processes, and information processing in selective spiking neurons are investigated. Summation of pulse inputs are considered on the basis of the theory of quasi-periodic functions and nonlinear transformation via relaxation of the self-oscillating system of a neuron. A way of encoding input information is also considered in which the information unit is a pulse sequence, and the intensity of the input signal is encoded by a synchronous change in the frequency of the pulse sequences.
ISSN:1062-8738
1934-9432
DOI:10.3103/S1062873818110163