An analysis of variability in somatosensory cortical neuron discharge

Whitsel, B. L.; Schreiner, Ralph; Essick, Gregory K

doi:10.1152/jn.1977.40.3.589

Cited by 28 publications

(8 citation statements)

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“…On the other hand, more-slowly firing cells had ISI densities with long tails, which was as predicted by simple leaky integrateand-fire (Lapicque) models with considerable amounts of inhibition (Tuckwell 1978). In another extensive early study (Whitsel et al 1977), the variability in the response of macaque monkey somatosensory cortical neurons to tactile stimuli was found to be heterogeneously distributed across the neuronal population, and depended on mean firing rate. The implication of these and many similar findings is that mammalian CNS neurons rarely if ever fire in regular or predictable temporal patterns.…”

Section: Introductionmentioning

confidence: 69%

A spatial stochastic neuronal model with Ornstein-Uhlenbeck input current

Tuckwell

Wan

Rospars

2002

Biological Cybernetics

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We consider a spatial neuron model in which the membrane potential satisfies a linear cable equation with an input current which is a dynamical random process of the Ornstein-Uhlenbeck (OU) type. This form of current may represent an approximation to that resulting from the random opening and closing of ion channels on a neuron's surface or to randomly occurring synaptic input currents with exponential decay. We compare the results for the case of an OU input with those for a purely white-noise-driven cable model. The statistical properties, including mean, variance and covariance of the voltage response to an OU process input in the absence of a threshold are determined analytically. The mean and the variance are calculated as a function of time for various synaptic input locations and for values of the ratio of the time constant of decay of the input current to the time constant of decay of the membrane voltage in the physiological range for real neurons. The limiting case of a white-noise input current is obtained as the correlation time of the OU process approaches zero. The results obtained with an OU input current can be substantially different from those in the white-noise case. Using simulation of the terms in the series representation for the solution, we estimate the interspike interval distribution for various parameter values, and determine the effects of the introduction of correlation in the synaptic input stochastic process.

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Section: Introductionmentioning

confidence: 69%

A spatial stochastic neuronal model with Ornstein-Uhlenbeck input current

Tuckwell

Wan

Rospars

2002

Biological Cybernetics

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“…In the second analysis, the ensemble activity was averaged across the time in which the monkey drew each segment, rather than in 25 ms bins. In both bin-and segment-based analyses, firing rates were first converted to fractional interspike intervals (31) and then log-transformed, because neuronal responses are commonly proportional to the baseline activity level (32)(33)(34); the log-transformation normalizes distributions of the measurements and stabilizes their variance. Transformed activity patterns were then classified to the segments as above.…”

Section: Methodsmentioning

confidence: 99%

Parallel processing of serial movements in prefrontal cortex

Averbeck

Chafee

Crowe

et al. 2002

Proc. Natl. Acad. Sci. U.S.A.

247

192

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A key idea in Lashley's formulation of the problem of serial order in behavior is the postulated neural representation of all serial elements before the action begins. We studied this question by recording the activity of individual neurons simultaneously in small ensembles in prefrontal cortex while monkeys copied geometrical shapes shown on a screen. Monkeys drew the shapes as sequences of movement segments, and these segments were associated with distinct patterns of neuronal ensemble activity. Here we show that these patterns were present during the time preceding the actual drawing. The rank of the strength of representation of a segment in the neuronal population during this time, as assessed by discriminant analysis, predicted the serial position of the segment in the motor sequence. An analysis of errors in copying and their neural correlates supplied additional evidence for this code and provided a neural basis for Lashley's hypothesis that errors in motor sequences would be most likely to occur when executing elements that had prior representations of nearly equal strength.

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“…However, intrinsic differences in sensor filter characteristics and coordinate reference frames give rise to discrepancies in the information provided to the brain by the different senses, leading to biases and errors in the estimation of limb configuration. Discrepancies may also arise due to variability in sensory transduction and neural encoding processes (i.e., "sensor noise") (Schreiner et al 1978; van Beers et al 2002;Whitsel et al 1977; see also Cordo et al 1994) or as a result of neural approximations to the complex nonlinear computations required to map joint angles to fingertip position or vice versa (cf. Flanders et al 1992;Ghez et al 1999).…”

Section: Introductionmentioning

confidence: 99%