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      Partitioning neuronal variability

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      Author(s): 1 , 1 , 1 , 2
      Publication date (Electronic):
      Journal: Nature neuroscience

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          Abstract

          Responses of sensory neurons differ across repeated measurements. This variability is usually treated as stochasticity arising within neurons or neural circuits. However, some portion of the variability arises from fluctuations in excitability due to factors that are not purely sensory, such as arousal, attention, and adaptation. To isolate these fluctuations, we developed a model in which spikes are generated by a Poisson process whose rate is the product of a drive that is sensory in origin, and a gain summarizing stimulus-independent modulatory influences on excitability. This model provides an accurate account of response distributions of visual neurons in macaque LGN, V1, V2, and MT, revealing that variability originates in large part from excitability fluctuations which are correlated over time and between neurons, and which increase in strength along the visual pathway. The model provides a parsimonious explanation for observed systematic dependencies of response variability and covariability on firing rate.

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          Spatio-temporal correlations and visual signalling in a complete neuronal population.

          Jonathan W. Pillow, Jonathon Shlens, Liam Paninski … (2008)
          Statistical dependencies in the responses of sensory neurons govern both the amount of stimulus information conveyed and the means by which downstream neurons can extract it. Although a variety of measurements indicate the existence of such dependencies, their origin and importance for neural coding are poorly understood. Here we analyse the functional significance of correlated firing in a complete population of macaque parasol retinal ganglion cells using a model of multi-neuron spike responses. The model, with parameters fit directly to physiological data, simultaneously captures both the stimulus dependence and detailed spatio-temporal correlations in population responses, and provides two insights into the structure of the neural code. First, neural encoding at the population level is less noisy than one would expect from the variability of individual neurons: spike times are more precise, and can be predicted more accurately when the spiking of neighbouring neurons is taken into account. Second, correlations provide additional sensory information: optimal, model-based decoding that exploits the response correlation structure extracts 20% more information about the visual scene than decoding under the assumption of independence, and preserves 40% more visual information than optimal linear decoding. This model-based approach reveals the role of correlated activity in the retinal coding of visual stimuli, and provides a general framework for understanding the importance of correlated activity in populations of neurons.
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            The variable discharge of cortical neurons: implications for connectivity, computation, and information coding.

            Michael N. Shadlen, William Newsome (1998)
            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.

              KH Britten, MN Shadlen, WT Newsome … (1992)
              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
                Journal ID (nlm-journal-id): 9809671
                Journal ID (pubmed-jr-id): 21092
                Journal ID (nlm-ta): Nat Neurosci
                Journal ID (iso-abbrev): Nat. Neurosci.
                Title: Nature neuroscience
                ISSN (Print): 1097-6256
                ISSN (Electronic): 1546-1726
                Publication date Nihms-submitted: 4 July 2014
                Publication date (Electronic): 28 April 2014
                Publication date (Print): June 2014
                Publication date PMC-release: 01 December 2014
                Volume: 17
                Issue: 6
                Pages: 858-865
                Affiliations
                [1 ]Center for Neural Science, New York University, 4 Washington Place, Room 809, New York, NY 10003
                [2 ]Howard Hughes Medical Institute, New York University, 4 Washington Place, Room 809, New York, NY 10003
                Author notes
                Address for correspondence: Eero P. Simoncelli, Center for Neural Science, New York University, 4 Washington Place, Room 809, New York, NY 10003, Phone: 212-998-3938, FAX: 212-995-4619, eero.simoncelli@ 123456nyu.edu
                Article
                Manuscript ID: NIHMS582311
                DOI: 10.1038/nn.3711
                PMC ID: 4135707
                PubMed ID: 24777419
                SO-VID: 9995fb40-74d8-4c39-83f3-771521f40a71
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                ScienceOpen disciplines: Neurosciences
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                ScienceOpen disciplines: Neurosciences

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