Bursting in a next generation neural mass model with synaptic dynamics: a slow-fast approach

Taher, H., Avitabile, D., Desroches, M.


We report a detailed analysis on the emergence of bursting in a recently developed neural mass model that takes short-term synaptic plasticity into account. The one being used here is particularly important, as it represents an exact meanfield limit of synaptically coupled quadratic integrate & fire neurons, a canonical model for type I excitability. In absence of synaptic dynamics, a periodic external current with a slow frequency {\epsilon} can lead to burst-like dynamics. The firing patterns can be understood using techniques of singular perturbation theory, specifically slow-fast dissection. In the model with synaptic dynamics the separation of timescales leads to a variety of slow-fast phenomena and their role for bursting is rendered inordinately more intricate. Canards are one of the main slow-fast elements on the route to bursting. They describe trajectories evolving nearby otherwise repelling locally invariant sets of the system and are found in the transition region from subthreshold dynamics to bursting. For values of the timescale separation nearby the singular limit {\epsilon} = 0, we report peculiar jump-on canards, which block a continuous transition to bursting. In the biologically more plausible regime of {\epsilon} this transition becomes continuous and bursts emerge via consecutive spike-adding transitions. The onset of bursting is of complex nature and involves mixed-type like torus canards, which form the very first spikes of the burst and revolve nearby fast-subsystem repelling limit cycles. We provide numerical evidence for the same mechanisms to be responsible for the emergence of bursting in the quadratic integrate & fire network with plastic synapses. The main conclusions apply for the network, owing to the exactness of the meanfield limit.




  	title = {Bursting in a next generation neural mass model with synaptic dynamics: a slow–fast approach},
	url = {https://doi.org/10.1007/s11071-022-07406-6},
	doi = {10.1007/s11071-022-07406-6},
	journal = {Nonlinear Dynamics},
	month = apr,
	year = {2022},