TY - JOUR
T1 - A novel density-based neural mass model for simulating neuronal network dynamics with conductance-based synapses and membrane current adaptation
AU - Huang, Chih Hsu
AU - Lin, Chou Ching K.
N1 - Funding Information:
This study has been supported by the Ministry of Science and Technology, Taiwan, Republic of China under Grant No. 108-2321-B-006-024-MY2.
Funding Information:
This study has been supported by the Ministry of Science and Technology, Taiwan , Republic of China under Grant No. 108-2321-B-006-024-MY2 .
Publisher Copyright:
© 2021 The Author(s)
PY - 2021/11
Y1 - 2021/11
N2 - Despite its success in understanding brain rhythms, the neural mass model, as a low-dimensional mean-field network model, is phenomenological in nature, so that it cannot replicate some of rich repertoire of responses seen in real neuronal tissues. Here, using a colored-synapse population density method, we derived a novel neural mass model, termed density-based neural mass model (dNMM), as the mean-field description of network dynamics of adaptive exponential integrate-and-fire (aEIF) neurons, in which two critical neuronal features, i.e., voltage-dependent conductance-based synaptic interactions and adaptation of firing rate responses, were included. Our results showed that the dNMM was capable of correctly estimating firing rate responses of a neuronal population of aEIF neurons receiving stationary or time-varying excitatory and inhibitory inputs. Finally, it was also able to quantitatively describe the effect of spike-frequency adaptation in the generation of asynchronous irregular activity of excitatory–inhibitory cortical networks. We conclude that in terms of its biological reality and calculation efficiency, the dNMM is a suitable candidate to build significantly large-scale network models involving multiple brain areas, where the neuronal population is the smallest dynamic unit.
AB - Despite its success in understanding brain rhythms, the neural mass model, as a low-dimensional mean-field network model, is phenomenological in nature, so that it cannot replicate some of rich repertoire of responses seen in real neuronal tissues. Here, using a colored-synapse population density method, we derived a novel neural mass model, termed density-based neural mass model (dNMM), as the mean-field description of network dynamics of adaptive exponential integrate-and-fire (aEIF) neurons, in which two critical neuronal features, i.e., voltage-dependent conductance-based synaptic interactions and adaptation of firing rate responses, were included. Our results showed that the dNMM was capable of correctly estimating firing rate responses of a neuronal population of aEIF neurons receiving stationary or time-varying excitatory and inhibitory inputs. Finally, it was also able to quantitatively describe the effect of spike-frequency adaptation in the generation of asynchronous irregular activity of excitatory–inhibitory cortical networks. We conclude that in terms of its biological reality and calculation efficiency, the dNMM is a suitable candidate to build significantly large-scale network models involving multiple brain areas, where the neuronal population is the smallest dynamic unit.
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U2 - 10.1016/j.neunet.2021.06.009
DO - 10.1016/j.neunet.2021.06.009
M3 - Article
C2 - 34157643
AN - SCOPUS:85108993646
SN - 0893-6080
VL - 143
SP - 183
EP - 197
JO - Neural Networks
JF - Neural Networks
ER -