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How Cortical Circuits Implement Cortical Computations: Mouse Visual Cortex as a Model.

ABSTRACT: The mouse, as a model organism to study the brain, gives us unprecedented experimental access to the mammalian cerebral cortex. By determining the cortex's cellular composition, revealing the interaction between its different components, and systematically perturbing these components, we are obtaining mechanistic insight into some of the most basic properties of cortical function. In this review, we describe recent advances in our understanding of how circuits of cortical neurons implement computations, as revealed by the study of mouse primary visual cortex. Further, we discuss how studying the mouse has broadened our understanding of the range of computations performed by visual cortex. Finally, we address how future approaches will fulfill the promise of the mouse in elucidating fundamental operations of cortex.

SUBMITTER: Niell CM

PROVIDER: S-EPMC9925090 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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How Cortical Circuits Implement Cortical Computations: Mouse Visual Cortex as a Model.

Niell Cristopher M CM Scanziani Massimo M

Annual review of neuroscience 20210429

The mouse, as a model organism to study the brain, gives us unprecedented experimental access to the mammalian cerebral cortex. By determining the cortex's cellular composition, revealing the interaction between its different components, and systematically perturbing these components, we are obtaining mechanistic insight into some of the most basic properties of cortical function. In this review, we describe recent advances in our understanding of how circuits of cortical neurons implement compu ...[more]

PMID: 33914591

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Project description:Since environments are constantly in flux, the brain's ability to identify novel stimuli that fall outside its own internal representation of the world is crucial for an organism's survival. Within the mammalian neocortex, inhibitory microcircuits are proposed to regulate activity in an experience-dependent manner and different inhibitory neuron subtypes exhibit distinct novelty responses. Discerning the function of diverse neural circuits and their modulation by experience can be daunting unless one has a biologically plausible mechanism to detect and learn from novel experiences that is both understandable and flexible. Here we introduce a learning mechanism, familiarity modulated synapses (FMSs), through which a network response that encodes novelty emerges from unsupervised multiplicative synaptic modifications depending only on the presynaptic or both the pre- and postsynaptic activity. FMSs stand apart from other familiarity mechanisms in their simplicity: they operate under continual learning, do not require specialized architecture, and can distinguish novelty rapidly without requiring feedback. Implementing FMSs within an experimentally-constrained model of a visual cortical circuit, we demonstrate the generalizability of FMSs by reproducing three distinct novelty effects recently observed in experiments: absolute, contextual (or oddball), and omission novelty. Additionally, our model reproduces functional diversity within cell subpopulations, leading to experimentally testable predictions about connectivity and synaptic dynamics that can produce both population-level novelty responses and heterogeneous individual neuron signals. Altogether, our findings demonstrate how simple plasticity mechanisms within the cortical circuit structure can give rise to qualitatively distinct novelty responses. The flexibility of FMSs opens the door to computationally and theoretically investigating how distinct synapse modulations can lead to a variety of experience-dependent responses in a simple, understandable, and biologically plausible setup.

Dataset Information

How Cortical Circuits Implement Cortical Computations: Mouse Visual Cortex as a Model.

Publications

How Cortical Circuits Implement Cortical Computations: Mouse Visual Cortex as a Model.

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