scholarly journals Optimal Self-Induced Stochastic Resonance in Multiplex Neural Networks: Electrical vs. Chemical Synapses

2020 ◽  
Vol 14 ◽  
Author(s):  
Marius E. Yamakou ◽  
Poul G. Hjorth ◽  
Erik A. Martens
Keyword(s):  
Neural Networks ◽  
Stochastic Resonance ◽  
Chemical Synapses

scholarly journals Influence of Autapses on Synchronization in Neural Networks With Chemical Synapses

2020 ◽  
Vol 14 ◽  
Author(s):  
Paulo R. Protachevicz ◽  
Kelly C. Iarosz ◽  
Iberê L. Caldas ◽  
Chris G. Antonopoulos ◽  
Antonio M. Batista ◽  
...  
Keyword(s):  
Neural Networks ◽  
Random Network ◽  
Inhibitory Neurons ◽  
Neural Dynamics ◽  
Excitatory Synapses ◽  
Closed Loops ◽  
Integrate And Fire ◽  
Network Topologies ◽  
Chemical Synapses ◽  
Synchronous Behavior

A great deal of research has been devoted on the investigation of neural dynamics in various network topologies. However, only a few studies have focused on the influence of autapses, synapses from a neuron onto itself via closed loops, on neural synchronization. Here, we build a random network with adaptive exponential integrate-and-fire neurons coupled with chemical synapses, equipped with autapses, to study the effect of the latter on synchronous behavior. We consider time delay in the conductance of the pre-synaptic neuron for excitatory and inhibitory connections. Interestingly, in neural networks consisting of both excitatory and inhibitory neurons, we uncover that synchronous behavior depends on their synapse type. Our results provide evidence on the synchronous and desynchronous activities that emerge in random neural networks with chemical, inhibitory and excitatory synapses where neurons are equipped with autapses.

scholarly journals Control of noise-induced coherent oscillations in three-neuron motifs

2021 ◽  
Author(s):  
Florian Bönsel ◽  
Patrick Krauss ◽  
Claus Metzner ◽  
Marius E. Yamakou
Keyword(s):  
Stochastic Resonance ◽  
Scaling Limit ◽  
Single Neurons ◽  
Excitable System ◽  
Degree Of Coherence ◽  
Chemical Synapses

AbstractThe phenomenon of self-induced stochastic resonance (SISR) requires a nontrivial scaling limit between the deterministic and the stochastic timescales of an excitable system, leading to the emergence of coherent oscillations which are absent without noise. In this paper, we numerically investigate SISR and its control in single neurons and three-neuron motifs made up of the Morris–Lecar model. In single neurons, we compare the effects of electrical and chemical autapses on the degree of coherence of the oscillations due to SISR. In the motifs, we compare the effects of altering the synaptic time-delayed couplings and the topologies on the degree of SISR. Finally, we provide two enhancement strategies for a particularly poor degree of SISR in motifs with chemical synapses: (1) we show that a poor SISR can be significantly enhanced by attaching an electrical or an excitatory chemical autapse on one of the neurons, and (2) we show that by multiplexing the motif with a poor SISR to another motif (with a high SISR in isolation), the degree of SISR in the former motif can be significantly enhanced. We show that the efficiency of these enhancement strategies depends on the topology of the motifs and the nature of synaptic time-delayed couplings mediating the multiplexing connections.

Stochastic resonance as an emergent property of neural networks

2013 ◽  
Author(s):  
M. A. Lopes ◽  
A. V. Goltsev ◽  
K.-E. Lee ◽  
J. F. F. Mendes
Keyword(s):  
Neural Networks ◽  
Stochastic Resonance ◽  
Emergent Property

Stochastic resonance in small-world neuronal networks with hybrid electrical–chemical synapses

2014 ◽  
Vol 60 ◽  
pp. 40-48 ◽  
Author(s):  
Jiang Wang ◽  
Xinmeng Guo ◽  
Haitao Yu ◽  
Chen Liu ◽  
Bin Deng ◽  
...  
Keyword(s):  
Stochastic Resonance ◽  
Neuronal Networks ◽  
Small World ◽  
Chemical Synapses
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