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SUMMARY:Intrinsic Determinants of Circuit Dynamics: A Comparative Analysis
  of Neuronal Excitability in Primate Prefrontal and Visual Cortex - Anmar 
  Khadra (McGill University)
DTSTART:20251202T093000Z
DTEND:20251202T101500Z
UID:TALK240973@talks.cam.ac.uk
DESCRIPTION:The primate visual cortex (V1) and prefrontal cortex (PFC) sup
 port distinct computational roles: neurons in V1 respond with high fidelit
 y to synaptic inputs\, enabling precise encoding of visual features\, wher
 eas PFC neurons integrate and filter information to support higher-order f
 unctions such as working memory\, cognitive flexibility\, and decision-mak
 ing. A central question is how the intrinsic (in vitro) electrical propert
 ies of neurons in these regions shape their circuit dynamics and thereby i
 nfluence the neural codes engaged during behavior. To address this\, our c
 ollaborators in the Martinez-Trujillo Lab (Western University) performed w
 hole-cell recordings from marmoset V1 and PFC. We used these datasets to c
 onduct both supervised and unsupervised clustering of neuronal electrophys
 iological signatures. The supervised approach\, based on spike width and s
 pike amplitude\, identified broad-spiking (BS) and narrow-spiking (NS) neu
 rons in both regions. Unsupervised clustering\, employing the Allen Instit
 ute feature-extraction framework\, revealed seven distinct electrophysiolo
 gical clusters. Although the cluster identities were largely conserved acr
 oss V1 and PFC\, the proportion of neurons populating each cluster differe
 d across areas\, suggesting that while intrinsic phenotypes are shared\, t
 heir distributions are region-specific. To examine how these intrinsic fea
 tures contribute to circuit-level behavior\, we developed Hodgkin&ndash\;H
 uxley (HH) models of BS and NS neurons in both regions and fit them direct
 ly to the key features used for clustering\, yielding close quantitative a
 greement with the empirical data. Bifurcation analyses of these models wer
 e then performed to characterize their excitability classes and firing reg
 imes\, after which we embedded the HH neurons into networks with different
  coupling topologies to probe circuit dynamics and synchrony. We are exten
 ding this work by incorporating neuronal heterogeneity within and across r
 egions and by developing hybrid models that integrate in vivo recordings w
 ith in vitro&ndash\;derived intrinsic dynamics. As a proof of concept for 
 this hybrid approach\, we have conducted a similar hybrid modeling approac
 h in macaques PFC to quantify how intrinsic spike-frequency adaptation con
 tributes to adaptation observed in vivo. In this talk\, I will provide an 
 overview of these findings and ongoing efforts.
LOCATION:Seminar Room 1\, Newton Institute
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