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      Theory of neuronal perturbome in cortical networks

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          Significance

          Brains are composed of networks of neurons that are highly interconnected. A central question in neuroscience is how such neuronal networks operate in tandem to make a functioning brain. To understand this, we need to study how neurons interact with each other in action, such as when viewing a visual scene or performing a motor task. One way to approach this question is by perturbing the activity of functioning neurons and measuring the resulting influence on other neurons. By using computational models of neuronal networks, we studied how this influence in visual networks depends on connectivity. Our results help to interpret contradictory results from previous experimental studies and explain how different connectivity patterns can enhance information processing during natural vision.

          Abstract

          To unravel the functional properties of the brain, we need to untangle how neurons interact with each other and coordinate in large-scale recurrent networks. One way to address this question is to measure the functional influence of individual neurons on each other by perturbing them in vivo. Application of such single-neuron perturbations in mouse visual cortex has recently revealed feature-specific suppression between excitatory neurons, despite the presence of highly specific excitatory connectivity, which was deemed to underlie feature-specific amplification. Here, we studied which connectivity profiles are consistent with these seemingly contradictory observations, by modeling the effect of single-neuron perturbations in large-scale neuronal networks. Our numerical simulations and mathematical analysis revealed that, contrary to the prima facie assumption, neither inhibition dominance nor broad inhibition alone were sufficient to explain the experimental findings; instead, strong and functionally specific excitatory–inhibitory connectivity was necessary, consistent with recent findings in the primary visual cortex of rodents. Such networks had a higher capacity to encode and decode natural images, and this was accompanied by the emergence of response gain nonlinearities at the population level. Our study provides a general computational framework to investigate how single-neuron perturbations are linked to cortical connectivity and sensory coding and paves the road to map the perturbome of neuronal networks in future studies.

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          Most cited references42

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          Chaos in neuronal networks with balanced excitatory and inhibitory activity.

          Neurons in the cortex of behaving animals show temporally irregular spiking patterns. The origin of this irregularity and its implications for neural processing are unknown. The hypothesis that the temporal variability in the firing of a neuron results from an approximate balance between its excitatory and inhibitory inputs was investigated theoretically. Such a balance emerges naturally in large networks of excitatory and inhibitory neuronal populations that are sparsely connected by relatively strong synapses. The resulting state is characterized by strongly chaotic dynamics, even when the external inputs to the network are constant in time. Such a network exhibits a linear response, despite the highly nonlinear dynamics of single neurons, and reacts to changing external stimuli on time scales much smaller than the integration time constant of a single neuron.
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            Inhibition of Inhibition in Visual Cortex: The Logic of Connections Between Molecularly Distinct Interneurons

            Cortical inhibitory neurons contact each other to form a network of inhibitory synaptic connections. Our knowledge of the connectivity pattern underlying this inhibitory network is, however, still incomplete. Here we discover a simple and complementary interaction scheme between three large molecularly distinct interneuron populations in mouse visual cortex: Parvalbumin expressing interneurons strongly inhibit one another but, surprisingly, provide little inhibition to other populations. In contrast, somatostatin expressing interneurons avoid inhibiting one another, yet strongly inhibit all other populations. Finally, vasoactive intestinal peptide expressing interneurons preferentially inhibit somatostatin interneurons. This scheme occurs in supra- and infra-granular layers, suggesting that inhibitory networks operate similarly at the input and output of visual cortex. Thus, as the specificity of connections between excitatory neurons forms the basis for the cortical canonical circuit, the scheme described here outlines a standard connectivity pattern among cortical inhibitory neurons.
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              Optogenetics in neural systems.

              Both observational and perturbational technologies are essential for advancing the understanding of brain function and dysfunction. But while observational techniques have greatly advanced in the last century, techniques for perturbation that are matched to the speed and heterogeneity of neural systems have lagged behind. The technology of optogenetics represents a step toward addressing this disparity. Reliable and targetable single-component tools (which encompass both light sensation and effector function within a single protein) have enabled versatile new classes of investigation in the study of neural systems. Here we provide a primer on the application of optogenetics in neuroscience, focusing on the single-component tools and highlighting important problems, challenges, and technical considerations. Copyright © 2011 Elsevier Inc. All rights reserved.
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                Author and article information

                Journal
                Proc Natl Acad Sci U S A
                Proc Natl Acad Sci U S A
                pnas
                pnas
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                27 October 2020
                14 October 2020
                14 October 2020
                : 117
                : 43
                : 26966-26976
                Affiliations
                [1] aBioengineering Department, Imperial College London , London SW7 2AZ, United Kingdom
                Author notes
                1To whom correspondence may be addressed. Email: c.clopath@ 123456imperial.ac.uk .

                Edited by Michael P. Stryker, University of California San Francisco Medical Center, San Francisco, CA, and approved September 11, 2020 (received for review March 11, 2020)

                Author contributions: S.S. designed research; S.S. performed research; S.S. and C.C. analyzed data; and S.S. and C.C. wrote the paper.

                Author information
                https://orcid.org/0000-0001-8159-5461
                https://orcid.org/0000-0003-4507-8648
                Article
                202004568
                10.1073/pnas.2004568117
                7604497
                33055215
                de8f280d-5e0f-4b95-90bd-04e741cbd353
                Copyright © 2020 the Author(s). Published by PNAS.

                This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).

                History
                Page count
                Pages: 11
                Categories
                Biological Sciences
                Neuroscience

                neuronal perturbation,cortical connectivity,sensory coding,visual cortex,perturbome

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