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

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          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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          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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            Dissolvable films of silk fibroin for ultrathin conformal bio-integrated electronics.

            Electronics that are capable of intimate, non-invasive integration with the soft, curvilinear surfaces of biological tissues offer important opportunities for diagnosing and treating disease and for improving brain/machine interfaces. This article describes a material strategy for a type of bio-interfaced system that relies on ultrathin electronics supported by bioresorbable substrates of silk fibroin. Mounting such devices on tissue and then allowing the silk to dissolve and resorb initiates a spontaneous, conformal wrapping process driven by capillary forces at the biotic/abiotic interface. Specialized mesh designs and ultrathin forms for the electronics ensure minimal stresses on the tissue and highly conformal coverage, even for complex curvilinear surfaces, as confirmed by experimental and theoretical studies. In vivo, neural mapping experiments on feline animal models illustrate one mode of use for this class of technology. These concepts provide new capabilities for implantable and surgical devices.
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              Response of brain tissue to chronically implanted neural electrodes.

              Chronically implanted recording electrode arrays linked to prosthetics have the potential to make positive impacts on patients suffering from full or partial paralysis. Such arrays are implanted into the patient's cortical tissue and record extracellular potentials from nearby neurons, allowing the information encoded by the neuronal discharges to control external devices. While such systems perform well during acute recordings, they often fail to function reliably in clinically relevant chronic settings. Available evidence suggests that a major failure mode of electrode arrays is the brain tissue reaction against these implants, making the biocompatibility of implanted electrodes a primary concern in device design. This review presents the biological components and time course of the acute and chronic tissue reaction in brain tissue, analyses the brain tissue response of current electrode systems, and comments on the various material science and bioactive strategies undertaken by electrode designers to enhance electrode performance.
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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
                f1669395-e1ae-403d-a827-990850b46db5
                © 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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