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      Tools for probing local circuits: high-density silicon probes combined with optogenetics

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          To understand how function arises from the interactions between neurons, it is necessary to use methods that allow the monitoring of brain activity at the single-neuron, single-spike level and the targeted manipulation of the diverse neuron types selectively in a closed-loop manner. Large-scale recordings of neuronal spiking combined with optogenetic perturbation of identified individual neurons has emerged as a suitable method for such tasks in behaving animals. To fully exploit the potential power of these methods, multiple steps of technical innovation are needed. We highlight the current state-of-the-art in electrophysiological recording methods, combined with optogenetics, and discuss directions for progress. In addition, we point to areas where rapid development is in progress and discuss topics where near-term improvements are possible and needed.

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          Most cited references 171

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          Driving fast-spiking cells induces gamma rhythm and controls sensory responses.

          Cortical gamma oscillations (20-80 Hz) predict increases in focused attention, and failure in gamma regulation is a hallmark of neurological and psychiatric disease. Current theory predicts that gamma oscillations are generated by synchronous activity of fast-spiking inhibitory interneurons, with the resulting rhythmic inhibition producing neural ensemble synchrony by generating a narrow window for effective excitation. We causally tested these hypotheses in barrel cortex in vivo by targeting optogenetic manipulation selectively to fast-spiking interneurons. Here we show that light-driven activation of fast-spiking interneurons at varied frequencies (8-200 Hz) selectively amplifies gamma oscillations. In contrast, pyramidal neuron activation amplifies only lower frequency oscillations, a cell-type-specific double dissociation. We found that the timing of a sensory input relative to a gamma cycle determined the amplitude and precision of evoked responses. Our data directly support the fast-spiking-gamma hypothesis and provide the first causal evidence that distinct network activity states can be induced in vivo by cell-type-specific activation.
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            Parvalbumin neurons and gamma rhythms enhance cortical circuit performance.

            Synchronized oscillations and inhibitory interneurons have important and interconnected roles within cortical microcircuits. In particular, interneurons defined by the fast-spiking phenotype and expression of the calcium-binding protein parvalbumin have been suggested to be involved in gamma (30-80 Hz) oscillations, which are hypothesized to enhance information processing. However, because parvalbumin interneurons cannot be selectively controlled, definitive tests of their functional significance in gamma oscillations, and quantitative assessment of the impact of parvalbumin interneurons and gamma oscillations on cortical circuits, have been lacking despite potentially enormous significance (for example, abnormalities in parvalbumin interneurons may underlie altered gamma-frequency synchronization and cognition in schizophrenia and autism). Here we use a panel of optogenetic technologies in mice to selectively modulate multiple distinct circuit elements in neocortex, alone or in combination. We find that inhibiting parvalbumin interneurons suppresses gamma oscillations in vivo, whereas driving these interneurons (even by means of non-rhythmic principal cell activity) is sufficient to generate emergent gamma-frequency rhythmicity. Moreover, gamma-frequency modulation of excitatory input in turn was found to enhance signal transmission in neocortex by reducing circuit noise and amplifying circuit signals, including inputs to parvalbumin interneurons. As demonstrated here, optogenetics opens the door to a new kind of informational analysis of brain function, permitting quantitative delineation of the functional significance of individual elements in the emergent operation and function of intact neural circuitry.
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              A resource of Cre driver lines for genetic targeting of GABAergic neurons in cerebral cortex.

              A key obstacle to understanding neural circuits in the cerebral cortex is that of unraveling the diversity of GABAergic interneurons. This diversity poses general questions for neural circuit analysis: how are these interneuron cell types generated and assembled into stereotyped local circuits and how do they differentially contribute to circuit operations that underlie cortical functions ranging from perception to cognition? Using genetic engineering in mice, we have generated and characterized approximately 20 Cre and inducible CreER knockin driver lines that reliably target major classes and lineages of GABAergic neurons. More select populations are captured by intersection of Cre and Flp drivers. Genetic targeting allows reliable identification, monitoring, and manipulation of cortical GABAergic neurons, thereby enabling a systematic and comprehensive analysis from cell fate specification, migration, and connectivity, to their functions in network dynamics and behavior. As such, this approach will accelerate the study of GABAergic circuits throughout the mammalian brain. Copyright © 2011 Elsevier Inc. All rights reserved.
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                Author and article information

                Journal
                8809320
                1600
                Neuron
                Neuron
                Neuron
                0896-6273
                1097-4199
                10 February 2015
                8 April 2015
                08 April 2016
                : 86
                : 1
                : 92-105
                Affiliations
                [1 ]The Neuroscience Institute, New York University, School of Medicine, New York, NY 10016, USA
                [2 ]Center for Neural Science, New York University, School of Medicine, New York, NY 10016, USA
                [3 ]MTA-SZTE ‘Lendület’ Oscillatory Neural Networks Research Group, University of Szeged, Department of Physiology, Szeged, H-6720, Hungary
                [4 ]NeuroNexus Technologies, Inc., Ann Arbor, Michigan, 48108, USA
                [5 ]Center for Wireless Integrated Microsensing and Systems, The University of Michigan, Ann Arbor, Michigan, 48109-2122, USA
                Author notes
                Correspondence: gyorgy.buzsáki@ 123456nyumc.org , NYU Neuroscience Institute, New York University, Langone Medical Center, East River Science Park, 450 East 29th Street, 9th Floor, New York, NY 10016
                Article
                NIHMS660824
                10.1016/j.neuron.2015.01.028
                4392339
                25856489
                © 2015 Published by Elsevier Inc.

                This manuscript version is made available under the CC BY-NC-ND 4.0 license.

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                Neurosciences

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