Huang CH, Lin CCK. A novel density-based neural mass model for simulating neuronal network dynamics with conductance-based synapses and membrane current adaptation.
Neural Netw 2021;
143:183-197. [PMID:
34157643 DOI:
10.1016/j.neunet.2021.06.009]
[Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Revised: 04/01/2021] [Accepted: 06/06/2021] [Indexed: 10/21/2022]
Abstract
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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