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      Assessing the Impact of Single-Cell Stimulation on Local Networks in Rat Barrel Cortex—A Feasibility Study

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          Abstract

          In contrast to the long-standing notion that the role of individual neurons in population activity is vanishingly small, recent studies have shown that electrical activation of only a single cortical neuron can have measurable effects on global brain state, movement, and perception. Although highly important for understanding how neuronal activity in cortex is orchestrated, the cellular and network mechanisms underlying this phenomenon are unresolved. Here, we first briefly review the current state of knowledge regarding the phenomenon of single-cell induced network modulation and discuss possible underpinnings. Secondly, we show proof of principle for an experimental approach to elucidate the mechanisms of single-cell induced changes in cortical activity. The setup allows simultaneous recordings of the spiking activity of multiple neurons across all layers of the cortex using a multi-electrode array, while manipulating the activity of one individual neuron in close proximity to the array. We demonstrate that single cells can be recorded and stimulated reliably for hundreds of trials, conferring high statistical power even for expectedly small effects of single-neuron spiking on network activity. Preliminary results suggest that single-cell stimulation on average decreases the firing rate of local network units. We expect that characterization of the spatiotemporal spread of single-cell evoked activity across layers and columns will yield novel insights into intracortical processing.

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          Most cited references41

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          Neural correlations, population coding and computation.

          How the brain encodes information in population activity, and how it combines and manipulates that activity as it carries out computations, are questions that lie at the heart of systems neuroscience. During the past decade, with the advent of multi-electrode recording and improved theoretical models, these questions have begun to yield answers. However, a complete understanding of neuronal variability, and, in particular, how it affects population codes, is missing. This is because variability in the brain is typically correlated, and although the exact effects of these correlations are not known, it is known that they can be large. Here, we review studies that address the interaction between neuronal noise and population codes, and discuss their implications for population coding in general.
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            The variable discharge of cortical neurons: implications for connectivity, computation, and information coding.

            Cortical neurons exhibit tremendous variability in the number and temporal distribution of spikes in their discharge patterns. Furthermore, this variability appears to be conserved over large regions of the cerebral cortex, suggesting that it is neither reduced nor expanded from stage to stage within a processing pathway. To investigate the principles underlying such statistical homogeneity, we have analyzed a model of synaptic integration incorporating a highly simplified integrate and fire mechanism with decay. We analyzed a "high-input regime" in which neurons receive hundreds of excitatory synaptic inputs during each interspike interval. To produce a graded response in this regime, the neuron must balance excitation with inhibition. We find that a simple integrate and fire mechanism with balanced excitation and inhibition produces a highly variable interspike interval, consistent with experimental data. Detailed information about the temporal pattern of synaptic inputs cannot be recovered from the pattern of output spikes, and we infer that cortical neurons are unlikely to transmit information in the temporal pattern of spike discharge. Rather, we suggest that quantities are represented as rate codes in ensembles of 50-100 neurons. These column-like ensembles tolerate large fractions of common synaptic input and yet covary only weakly in their spike discharge. We find that an ensemble of 100 neurons provides a reliable estimate of rate in just one interspike interval (10-50 msec). Finally, we derived an expression for the variance of the neural spike count that leads to a stable propagation of signal and noise in networks of neurons-that is, conditions that do not impose an accumulation or diminution of noise. The solution implies that single neurons perform simple algebra resembling averaging, and that more sophisticated computations arise by virtue of the anatomical convergence of novel combinations of inputs to the cortical column from external sources.
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              The analysis of visual motion: a comparison of neuronal and psychophysical performance.

              We compared the ability of psychophysical observers and single cortical neurons to discriminate weak motion signals in a stochastic visual display. All data were obtained from rhesus monkeys trained to perform a direction discrimination task near psychophysical threshold. The conditions for such a comparison were ideal in that both psychophysical and physiological data were obtained in the same animals, on the same sets of trials, and using the same visual display. In addition, the psychophysical task was tailored in each experiment to the physiological properties of the neuron under study; the visual display was matched to each neuron's preference for size, speed, and direction of motion. Under these conditions, the sensitivity of most MT neurons was very similar to the psychophysical sensitivity of the animal observers. In fact, the responses of single neurons typically provided a satisfactory account of both absolute psychophysical threshold and the shape of the psychometric function relating performance to the strength of the motion signal. Thus, psychophysical decisions in our task are likely to be based upon a relatively small number of neural signals. These signals could be carried by a small number of neurons if the responses of the pooled neurons are statistically independent. Alternatively, the signals may be carried by a much larger pool of neurons if their responses are partially intercorrelated.
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                Author and article information

                Journal
                Int J Mol Sci
                Int J Mol Sci
                ijms
                International Journal of Molecular Sciences
                MDPI
                1422-0067
                27 May 2019
                May 2019
                : 20
                : 10
                : 2604
                Affiliations
                [1 ]University Medical Center of the Johannes Gutenberg University Mainz, Institute of Pathophysiology, 55128 Mainz, Germany
                [2 ]Focus Program Translational Neurosciences, University Medical Center of the Johannes Gutenberg University Mainz, 55128 Mainz, Germany
                Author notes
                Author information
                https://orcid.org/0000-0002-3452-2865
                https://orcid.org/0000-0002-7031-262X
                Article
                ijms-20-02604
                10.3390/ijms20102604
                6567036
                31137894
                dd98e0d7-39e1-4137-98b2-71a9d09b5ac7
                © 2019 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 29 April 2019
                : 24 May 2019
                Categories
                Article

                Molecular biology
                barrel cortex,nanostimulation,juxtacellular,in vivo
                Molecular biology
                barrel cortex, nanostimulation, juxtacellular, in vivo

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