State modulation in spatial networks with three interneuron subtypes
Neuroscience ePoster by Chengcheng Huang, Madeline Edwards and Jonathan Rubin
Part of COSYNE 2025 posters
Audio
Abstract
Neuronal responses to sensory stimuli are strongly modulated by an animal’s brain state. Three distinct
subtypes of inhibitory interneurons, parvalbumin (PV), somatostatin (SOM), and vasoactive intestinal
peptide (VIP) expressing cells, have been identified as key players in flexibly modulating sensory
responses [1]. The three interneuron populations have specialized local microcircuit motifs and are targeted
differentially by neuromodulators and top-down inputs from higher-order cortical areas. However,
the distinct contribution of each interneuron subtype to modulating a cortical network’s dynamical state
remains unclear. In this work, we systematically study the impacts of cell-type specific modulatory
inputs in spatially ordered spiking neuron networks comprising excitatory (E) neurons and the three
interneuron populations. The application of external input models slow processes like the variation
of arousal state. We find that the network transitions across the same three states, characterized by
distinct population rates and coherence properties, as input strength to any one population is varied.
The details of this transition, however, depend on the modulation target. Strikingly, the firing rates of
the SOM cells align closely with the level of network synchrony irrespective of the target of modulatory
input. Our model results are consistent with recent photostimulation experiments in mouse visual
cortex which suggest that SOM cells are responsible for inducing low gamma/beta rhythms (20-30 Hz)
[2, 3, 4]. Further analysis reveals that inhibition from SOM to PV cells must be limited to allow gradual
transitions from asynchrony to synchrony and that the strength of recurrent excitation onto SOM cells
determines the level of synchrony achievable in the network. Overall, our results highlight common
dynamic regimes achieved across modulations of different cell populations and identify SOM cells as
the main driver of network synchrony.
Topics
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