Heterogeneous Connectivity in E/I Neural Network Allows Entrainment to a Wide Frequency Range

Note: https://bostonu.zoom.us/j/99089473786?pwd=anliRUdkdGZSYVpGandneWlSRFc5QT09
Speaker: Jingjin Wei, Boston University, Physics Department

When: December 4, 2020 (Fri), 01:00PM to 02:00PM (add to my calendar)

This event is part of the Departmental Seminars.

Oscillations and rhythms are measured in the brain through large-scale measures like EEG (electroencephalogram) and LFP (Local Field Potential). In particular, cortical gamma rhythms (30-90 Hz) found in different regions of the brain are correlated with different cognitive states. Despite a wide range of frequencies in the gamma frequency band, the regions communicate to complete high-level tasks. One way in which communication takes place is through entrainment: a region receives rhythmic input from another region and phase-locks to it. This raises the question of how the properties of network rhythms foster entrainment.

Standard neuronal network models of gamma frequency generation use homogeneous network models because these models have robust natural (intrinsic) gamma frequencies. However, homogeneous network models can only phase-lock to input frequencies that are close enough to their natural frequencies. Here we investigate what modifications can be imposed upon the homogeneous network models to allow the network to phase-lock to weak rhythmic input over a large portion of the gamma frequency range. We show that heterogeneity in the synaptic conductance from excitatory neurons to inhibitory neurons greatly increases the frequency range over which the network can entrain. We find that subsets of entrained inhibitory cells form due to the added heterogeneity to support the entrainment of the whole network through feed-back inhibition. This improvement is shown to be robust in a large parameter space.


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