A novel density-based neural mass model for simulating neuronal network dynamics with conductance-based synapses and membrane current adaptation. (November 2021) - Huang, Chih-Hsu; Lin, Chou-Ching K.
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. Highlights: A novel neural mass model as the next-generation neural mass model. Theoretically rigorous derivation based on the colored-noise population density method. The derived model shows the property of firing rate adaptation. Spike-frequency adaptation stabilizes the asynchronous irregular network activity.