The mechanics of correlated variability in segregated cortical excitatory subnetworks

• 成果类型: Article
• 署名作者: Negron, Alex; Getz, Matthew P.; Handy, Gregory; Doiron, Brent
• 署名单位: Illinois Institute of Technology; University of Chicago; University of Chicago; University of Chicago; Technical University of Munich; University of Minnesota System; University of Minnesota Twin Cities
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-12810
• DOI: 10.1073/pnas.2306800121
• 发表日期: 2024-07-09
• 关键词: network; connectivity; DYNAMICS; interneurons; modulation; neurons; state

摘要

Understanding the genesis of shared trial-to-trial variability in neuronal population activity within the sensory cortex is critical to uncovering the biological basis of information processing in the brain. Shared variability is often a reflection of the structure of cortical connectivity since it likely arises, in part, from local circuit inputs. A series of experiments from segregated networks of (excitatory) pyramidal neurons in the mouse primary visual cortex challenge this view. Specifically, the across-network correlations were found to be larger than predicted given the known weak crossnetwork connectivity. We aim to uncover the circuit mechanisms responsible for these enhanced correlations through biologically motivated cortical circuit models. Our central finding is that coupling each excitatory subpopulation with a specific inhibitory subpopulation provides the most robust network-intrinsic solution in shaping these enhanced correlations. This result argues for the existence of excitatory- inhibitory functional assemblies in early sensory areas which mirror not just response properties but also connectivity between pyramidal cells. Furthermore, our findings provide theoretical support for recent experimental observations showing that cortical inhibition forms structural and functional subnetworks with excitatory cells, in contrast to the classical view that inhibition is a nonspecific blanket suppression of local excitation. Significance The structure of recurrent connectivity within cortical networks has important implications for their activity. Previous work has found neurons preferentially interconnect to form clustered assemblies. Traditionally, such assemblies of neurons showed strong, positive within-population correlations and strong, negative cross-population correlations. Our work is motivated by recent experimental results that stand in stark contrast to these observations. Specifically, it was found that neurons in the mouse visual cortex exhibited highly correlated activity but with a small probability of connection. Using theoretical analyses, we find that the most robust solution involves inhibitory neurons being equally segregated in their interactions with excitatory neurons. That is, inhibition should strongly cocluster with excitation, a result that aligns with recent experimental observations.