The mechanics of correlated variability in segregated cortical excitatory subnetworks

Negrón, Alex; Getz, Matthew P.; Handy, Gregory; Doiron, Brent

doi:10.1101/2023.04.25.538323

Cited by 3 publications

(1 citation statement)

References 46 publications

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“…This pattern closely aligns with a modular structure in dVf and hVf neurons, a feature observed in cortical functional networks [ 29 , 47 – 49 ]. In a modular structure, neurons within the same module (or class) are more densely interconnected, which facilitates specialized processing within the cortical column [ 45 , 50 ]. Similar patterns have been identified in fosGFP-expressing layer 2/3 pyramidal cells in the primary somatosensory cortex, which displayed elevated spontaneous activity and were more likely to form connections with each other [ 45 , 51 ].…”

Section: Resultsmentioning

confidence: 99%

Modeling circuit mechanisms of opposing cortical responses to visual flow perturbations

Galván Fraile,

Scherr,

Ramasco

et al. 2024

PLoS Comput Biol

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In an ever-changing visual world, animals’ survival depends on their ability to perceive and respond to rapidly changing motion cues. The primary visual cortex (V1) is at the forefront of this sensory processing, orchestrating neural responses to perturbations in visual flow. However, the underlying neural mechanisms that lead to distinct cortical responses to such perturbations remain enigmatic. In this study, our objective was to uncover the neural dynamics that govern V1 neurons’ responses to visual flow perturbations using a biologically realistic computational model. By subjecting the model to sudden changes in visual input, we observed opposing cortical responses in excitatory layer 2/3 (L2/3) neurons, namely, depolarizing and hyperpolarizing responses. We found that this segregation was primarily driven by the competition between external visual input and recurrent inhibition, particularly within L2/3 and L4. This division was not observed in excitatory L5/6 neurons, suggesting a more prominent role for inhibitory mechanisms in the visual processing of the upper cortical layers. Our findings share similarities with recent experimental studies focusing on the opposing influence of top-down and bottom-up inputs in the mouse primary visual cortex during visual flow perturbations.

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Section: Resultsmentioning

confidence: 99%

Modeling circuit mechanisms of opposing cortical responses to visual flow perturbations

Galván Fraile,

Scherr,

Ramasco

et al. 2024

PLoS Comput Biol

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The mechanics of correlated variability in segregated cortical excitatory subnetworks

Negrón,

Getz,

Handy

et al. 2024

Proc. Natl. Acad. Sci. U.S.A.

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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 cross-network 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.

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Structural influences on synaptic plasticity: the role of presynaptic connectivity in the emergence of E/I co-tuning

Giannakakis

Vinogradov

Buendía

et al. 2023

Preprint

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Experimental studies have shown that in cortical neurons, excitatory and inhibitory incoming currents are strongly correlated, which is hypothesized to be essential for efficient computations. Additionally, cortical neurons exhibit strong preference to particular stimuli, which combined with the co-variability of excitatory and inhibitory inputs indicates a detailed co-tuning of the corresponding populations. Such co-tuning is hypothesized to emerge during development in a self-organized manner. Indeed, theoretical studies have demonstrated that a combination of plasticity rules could lead to the emergence of E/I co-tuning in neurons driven by low noise signals from feedforward connections. However, cortical signals are very noisy and originate in highly recurrent networks, which raises a question on the ability of known plasticity mechanisms to self-organize co-tuned connectivity. We demonstrate that high noise levels combined with random recurrence destroy co-tuning. However, we demonstrate that introducing structure in the connectivity patterns of the recurrent E/ I network, the recurrence does not hinder but enhances the formation of the co-tuned selectivity. We employ a combination of analytical methods and simulation-based inference to uncover constraints on the recurrent connectivity that allow E/I co-tuning to emerge. We find that stronger excitatory connectivity within similarly tuned neurons, combined with more homogeneous inhibitory connectivity enhances the ability of plasticity to produce co-tuning in an upstream population. Our results suggest that structured recurrent connectivity controls information propagation and can enhance the ability of synaptic plasticity to learn input-output relationships in higher brain areas.

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The mechanics of correlated variability in segregated cortical excitatory subnetworks

Cited by 3 publications

References 46 publications

Modeling circuit mechanisms of opposing cortical responses to visual flow perturbations

Modeling circuit mechanisms of opposing cortical responses to visual flow perturbations

The mechanics of correlated variability in segregated cortical excitatory subnetworks

Structural influences on synaptic plasticity: the role of presynaptic connectivity in the emergence of E/I co-tuning

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