Behavioral state and stimulus strength regulate the role of somatostatin interneurons in stabilizing network activity.

Abstract

Inhibition stabilization enables cortical circuits to encode sensory signals across diverse contexts. Somatostatin-expressing (SST) interneurons are well-suited for this role through their strong recurrent connectivity with excitatory pyramidal cells. We developed a cortical circuit model predicting that SST cells become increasingly important for stabilization as sensory input strengthens. We tested this prediction in mouse primary visual cortex by manipulating excitatory input to SST cells, a key parameter for inhibition stabilization, with a novel cell-type specific pharmacological method to selectively block glutamatergic receptors on SST cells. Consistent with our model predictions, we find antagonizing glutamatergic receptors drives a paradoxical facilitation of SST cells with increasing stimulus contrast. In addition, we find even stronger engagement of SST-dependent stabilization when the mice are aroused. Thus, we reveal that the role of SST cells in cortical processing gradually switches as a function of both input strength and behavioral state.

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Citation

Published Version (Please cite this version)

10.1101/2024.09.09.612138

Publication Info

Cammarata, Celine M, Yingming Pei, Brenda C Shields, Shaun SX Lim, Tammy Hawley, Jennifer Y Li, David St Amand, Nicolas Brunel, et al. (2024). Behavioral state and stimulus strength regulate the role of somatostatin interneurons in stabilizing network activity. bioRxiv. 10.1101/2024.09.09.612138 Retrieved from https://hdl.handle.net/10161/31614.

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Brunel

Nicolas Brunel

Adjunct Professor of Neurobiology

We use theoretical models of brain systems to investigate how they process and learn information from their inputs. Our current work focuses on the mechanisms of learning and memory, from the synapse to the network level, in collaboration with various experimental groups. Using methods from
statistical physics, we have shown recently that the synaptic
connectivity of a network that maximizes storage capacity reproduces
two key experimentally observed features: low connection probability
and strong overrepresentation of bidirectionnally connected pairs of
neurons. We have also inferred `synaptic plasticity rules' (a
mathematical description of how synaptic strength depends on the
activity of pre and post-synaptic neurons) from data, and shown that
networks endowed with a plasticity rule inferred from data have a
storage capacity that is close to the optimal bound.



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