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      A biophysical account of multiplication by a single neuron

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          Abstract

          Nonlinear, multiplication-like operations carried out by individual nerve cells greatly enhance the computational power of a neural system 13 , but our understanding of their biophysical implementation is scant. Here we pursue this problem in the Drosophila melanogaster ON motion vision circuit 4, 5 , in which we record the membrane potentials of direction-selective T4 neurons and of their columnar input elements 6, 7 in response to visual and pharmacological stimuli in vivo. Our electrophysiological measurements and conductance-based simulations provide evidence for a passive supralinear interaction between two distinct types of synapse on T4 dendrites. We show that this multiplication-like nonlinearity arises from the coincidence of cholinergic excitation and release from glutamatergic inhibition. The latter depends on the expression of the glutamate-gated chloride channel GluClα 8, 9 in T4 neurons, which sharpens the directional tuning of the cells and shapes the optomotor behaviour of the animals. Interacting pairs of shunting inhibitory and excitatory synapses have long been postulated as an analogue approximation of a multiplication, which is integral to theories of motion detection 10, 11 , sound localization 12 and sensorimotor control 13 .

          Abstract

          Release from shunting inhibition and coincident excitation implement a multiplication-like synaptic interaction in motion-sensing neurons of Drosophila melanogaster.

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          Most cited references57

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          Fiji: an open-source platform for biological-image analysis.

          Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.
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            Cortical interneurons that specialize in disinhibitory control

            In the mammalian cerebral cortex, the diversity of interneuronal subtypes underlies a division of labor subserving distinct modes of inhibitory control 1–7 . A unique mode of inhibitory control may be provided by inhibitory neurons that specifically suppress the firing of other inhibitory neurons. Such disinhibition could lead to the selective amplification of local processing and serve the important computational functions of gating and gain modulation 8,9 . Although several interneuron populations are known to target other interneurons to varying degrees 10–15 , little is known about interneurons specializing in disinhibition and their in vivo function. Here we show that a class of interneurons that express vasoactive intestinal polypeptide (VIP) mediates disinhibitory control in multiple areas of neocortex and is recruited by reinforcement signals. By combining optogenetic activation with single cell recordings, we examined the functional role of VIP interneurons in awake mice, and investigated the underlying circuit mechanisms in vitro in auditory and medial prefrontal cortices. We identified a basic disinhibitory circuit module in which activation of VIP interneurons transiently suppresses primarily somatostatin- and a fraction of parvalbumin-expressing inhibitory interneurons that specialize in the control of the input and output of principal cells, respectively 3,6,16,17 . During the performance of an auditory discrimination task, reinforcement signals (reward and punishment) strongly and uniformly activated VIP neurons in auditory cortex, and in turn VIP recruitment increased the gain of a functional subpopulation of principal neurons. These results reveal a specific cell-type and microcircuit underlying disinhibitory control in cortex and demonstrate that it is activated under specific behavioural conditions.
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              Dendritic computation.

              One of the central questions in neuroscience is how particular tasks, or computations, are implemented by neural networks to generate behavior. The prevailing view has been that information processing in neural networks results primarily from the properties of synapses and the connectivity of neurons within the network, with the intrinsic excitability of single neurons playing a lesser role. As a consequence, the contribution of single neurons to computation in the brain has long been underestimated. Here we review recent work showing that neuronal dendrites exhibit a range of linear and nonlinear mechanisms that allow them to implement elementary computations. We discuss why these dendritic properties may be essential for the computations performed by the neuron and the network and provide theoretical and experimental examples to support this view.
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                Author and article information

                Contributors
                groschner@neuro.mpg.de
                borst@neuro.mpg.de
                Journal
                Nature
                Nature
                Nature
                Nature Publishing Group UK (London )
                0028-0836
                1476-4687
                23 February 2022
                23 February 2022
                2022
                : 603
                : 7899
                : 119-123
                Affiliations
                GRID grid.429510.b, ISNI 0000 0004 0491 8548, Max Planck Institute of Neurobiology, ; Martinsried, Germany
                Author information
                http://orcid.org/0000-0002-4325-0232
                http://orcid.org/0000-0002-1636-6248
                http://orcid.org/0000-0002-8214-3747
                http://orcid.org/0000-0001-5537-8973
                Article
                4428
                10.1038/s41586-022-04428-3
                8891015
                35197635
                e9cb9ba9-1861-4a38-9404-9ed76d9c3c84
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 21 June 2021
                : 14 January 2022
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                © The Author(s), under exclusive licence to Springer Nature Limited 2022

                Uncategorized
                neural circuits,motion detection,biophysical models
                Uncategorized
                neural circuits, motion detection, biophysical models

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