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Cognitive Neurodynamics

, Volume 12, Issue 5, pp 509–518 | Cite as

Vibrational resonance in a randomly connected neural network

  • Yingmei Qin
  • Chunxiao Han
  • Yanqiu Che
  • Jia Zhao
Research Article
  • 82 Downloads

Abstract

A randomly connected network is constructed with similar characteristics (e.g., the ratio of excitatory and inhibitory neurons, the connection probability between neurons, and the axonal conduction delays) as that in the mammalian neocortex and the effects of high-frequency electrical field on the response of the network to a subthreshold low-frequency electrical field are studied in detail. It is found that both the amplitude and frequency of the high-frequency electrical field can modulate the response of the network to the low-frequency electric field. Moreover, vibrational resonance (VR) phenomenon induced by the two types of electrical fields can also be influenced by the network parameters, such as the neuron population, the connection probability between neurons and the synaptic strength. It is interesting that VR is found to be related with the ratio of excitatory neurons that are under high-frequency electrical stimuli. In summary, it is suggested that the interaction of excitatory and inhibitory currents is also an important factor that can influence the performance of VR in neural networks.

Keywords

Vibrational resonance Neural network Izhikevich neuron model Weak electric field 

Notes

Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 61431013), the Natural Science Foundation of Tianjin (Nos. 17JCQNJC03700 and 15JCYBJC19000), the Tianjin Municipal Special Program of Talents Development for Excellent Youth Scholars, and the Fundamental Research Funds for the Central Universities (No. SWU1709620). We would also acknowledge the support of Tianjin University of Technology and Education (No. KYQD14006).

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Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Tianjin Key Laboratory of Information Sensing and Intelligent Control, School of Automation and Electrical EngineeringTianjin University of Technology and EducationTianjinChina
  2. 2.Key Laboratory of Cognition and Personality (Ministry of Education) and Faculty of PsychologySouthwest UniversityChongqingChina
  3. 3.Chongqing Collaborative Innovation Center for Brain ScienceChongqingChina

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