Spike-timing-dependent plasticity optimized coherence resonance and synchronization transitions by autaptic delay in adaptive scale-free neuronal networks
Creators
- 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.2018.01.020Additional 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.