TY - JOUR
T1 - Vibrational resonance in a randomly connected neural network
AU - Qin, Yingmei
AU - Han, Chunxiao
AU - Che, Yanqiu
AU - Zhao, Jia
N1 - Funding Information:
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).
Funding Information:
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).
Publisher Copyright:
© 2018, Springer Nature B.V.
PY - 2018/10/1
Y1 - 2018/10/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85048745630&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85048745630&partnerID=8YFLogxK
U2 - 10.1007/s11571-018-9492-2
DO - 10.1007/s11571-018-9492-2
M3 - Article
C2 - 30250629
AN - SCOPUS:85048745630
SN - 1871-4080
VL - 12
SP - 509
EP - 518
JO - Cognitive Neurodynamics
JF - Cognitive Neurodynamics
IS - 5
ER -