Published March 2018 | Version v1
Journal article

Spike-timing-dependent plasticity optimized coherence resonance and synchronization transitions by autaptic delay in adaptive scale-free neuronal networks

  • 1. School of Physics and Optoelectronic Engineering, Ludong University, Yantai, Shandong 264025 (China)
  • 2. Library, Ludong University, Yantai, Shandong 264025 (China)

Description

Highlights: • STDP can optimize MCR and ST by autaptic delay in the adaptive scale-free neuronal networks. • Effect of network average degree on ST by autaptic delay changes as Ap is increased. • MCR and ST by autaptic delay are robust to network size in the presence of STDP. - Abstract: In this paper, we numerically study the effect of spike-timing-dependent plasticity on multiple coherence resonance and synchronization transitions induced by autaptic time delay in adaptive scale-free Hodgkin–Huxley neuron networks. As the adjusting rate Ap of spike-timing-dependent plasticity increases, multiple coherence resonance and synchronization transitions enhance and become strongest at an intermediate Ap value, indicating that there is optimal spike-timing-dependent plasticity that can most strongly enhance the multiple coherence resonance and synchronization transitions. As Ap increases, increasing network average degree has a small effect on multiple coherence resonance, but its effect on synchronization transitions changes from suppressing to enhancing it. As network size is varied, multiple coherence resonance and synchronization transitions nearly do not change. These results show that spike-timing-dependent plasticity can simultaneously optimize multiple coherence resonance and synchronization transitions by autaptic delay in the adaptive scale-free neuronal networks. These findings provide a new insight into spike-timing-dependent plasticity and autaptic delay for the information processing and transmission in neural systems.

Availability note (English)

Available from http://dx.doi.org/10.1016/j.chaos.2018.01.020

Additional details

Identifiers

DOI
10.1016/j.chaos.2018.01.020;
PII
S0960077918300195;

Publishing Information

Journal Title
Chaos, Solitons and Fractals
Journal Volume
108
Journal Page Range
p. 1-7
ISSN
0960-0779

INIS

Country of Publication
United Kingdom
Country of Input or Organization
International Atomic Energy Agency (IAEA)
INIS RN
51023611
Subject category
S97: MATHEMATICAL METHODS AND COMPUTING;
Resource subtype / Literary indicator
Numerical Data
Descriptors DEI
DATA PROCESSING; NEURAL NETWORKS; NUMERICAL ANALYSIS; NUMERICAL DATA; PLASTICITY; RESONANCE; SYNCHRONIZATION
Descriptors DEC
DATA; INFORMATION; MATHEMATICS; MECHANICAL PROPERTIES; PROCESSING

Optional Information

Notes
© 2018 Elsevier Ltd. All rights reserved.