How much do they tell us to move?

Prete, Valeria Del; Steinberg, O.; Treves, Alessandro; Vaadia, Eilon

doi:10.1016/s0925-2312(01)00558-6

Cited by 4 publications

(10 citation statements)

References 4 publications

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“…Similar trends in the results were also observed here for most of the targets. The range of values of AMI reported is in agreement with previously reported values of MI in the literature in the context of neuronal interactions [4]. …”

Section: Resultssupporting

confidence: 91%

Analysis of neuronal interactions during adaptation and learning in motor control of primates

Narayanan¹,

Weber²,

He³

et al. 2002

Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society] [Engi

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In this study, we investigate how neurons from different cortices of the brain interact with each other during learning of a task, and how they adapt after perturbation in rhesus monkeys. The level of interaction between neurons over time was estimated through a nonparametric approach by calculating the mutual information between their neural spike trains. It was observed that the average mutual information between neurons was increased significantly within the motor cortex and between the motor and sensory cortices of the brain during the last days of the experiment. Moreover, the increased interactions within the motor cortex were observed before the onset of expected perturbations of arm movements, indicating that the neurons interact well in advance in anticipation of a perturbation, and coordinate their activity to adapt to the task faster and more successfully.

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Section: Resultssupporting

confidence: 91%

Analysis of neuronal interactions during adaptation and learning in motor control of primates

Narayanan¹,

Weber²,

He³

et al. 2002

Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society] [Engi

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“…Since the analytical expression of the mutual information is the same in both cases the fit does not change between the two models. 6 shows the comparison between the information estimated from a sample of cells recorded in the right primary motor cortex [10] and the prediction given by either of the two theoretical distributions. In the limit of large σ the gaussian model fails to provide a good fit, but we can conclude that this failure is not due to the inclusion of negative rates in the distribution.…”

Section: Evaluation Of the Mutual Informationmentioning

confidence: 99%

“…In fact the analysis of real data has proved that the coding of the direction is not unique, but it is specific to the complex correlate which is being considered [10] (here, which arm moves).…”

Section: Evaluation Of the Mutual Informationmentioning

confidence: 99%

“…In trying to characterize the neural coding of these movements, we were particularly interested in whether, as it is reasonable to expect, different motor areas differ, at least quantitatively, in their coding properties. The results, obtained from records of activity in areas M1 and SMA, will be reported elsewhere [10]; but they suggest the importance of developing theoretical models of how populations of neurons might code simultaneously discrete and continuous correlates. For example, one clear conclusion has been that type and direction are not independent dimensions of the movement, in the sense for example that the information about direction, extracted from the activity recorded with all movement types, is much lower (roughly half) of the average information about direction, obtained with a single movement type.…”

Section: Introductionmentioning

confidence: 96%

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Theoretical model of neuronal population coding of stimuli with both continuous and discrete dimensions

Prete

Treves

2001

Phys. Rev. E

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In a recent study the initial rise of the mutual information between the firing rates of N neurons and a set of p discrete stimuli has been analytically evaluated, under the assumption that neurons fire independently of one another to each stimulus and that each conditional distribution of firing rates is gaussian. Yet real stimuli or behavioural correlates are high-dimensional, with both discrete and continuously varying features. Moreover, the gaussian approximation implies negative firing rates, which is biologically implausible. Here, we generalize the analysis to the case where the stimulus or behavioural correlate has both a discrete and a continuous dimension, like orientation and shape could be in a visual stimulus, or type and direction in a motor action. The functional relationship between the firing patterns and the continuous correlate is expressed through the tuning curve of the neuron, using two different parameters to modulate its width and its flatness. In the case of large noise we evaluate the mutual information up to the quadratic approximation as a function of population size. We also show that in the limit of large N and assuming that neurons can discriminate between continuous values with a resolution ∆ϑ, the mutual information grows to infinity like log(1/∆ϑ) when ∆ϑ goes to zero. Then we consider a more realistic distribution of firing rates, truncated at zero, and we prove that the resulting correction, with respect to the gaussian firing rates, can be expressed simply as a renormalization of the noise parameter. Finally, we demonstrate the effect of averaging the distribution across the discrete dimension, evaluating the mutual information only with respect to the continuously varying correlate.

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“…In [7,8,16] the coding of movements categorized according to their direction (continuous dimension) and their type (discrete dimension) has been studied via direct information estimates from real data and pure theoretical modelling. In particular in [7] the information between the neuronal firing rates and the movements has been evaluated in the limit of large noise and finite population size, in presence of quenched disorder and resorting to the replica trick.…”

Section: Population Information In the Initial Linear Regimementioning

confidence: 99%

Replica-free evaluation of the neuronal population information with mixed continuous and discrete stimuli: From the linear to the asymptotic regime

Prete

2003

Phys. Rev. E

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Recent studies have explored theoretically the ability of populations of neurons to carry information about a set of stimuli, both in the case of purely discrete or purely continuous stimuli, and in the case of multidimensional continuous angular and discrete correlates, in the presence of additional quenched disorder in the distribution. An analytical expression for the mutual information has been obtained in the limit of large noise by means of the replica trick. Here, we show that the same results can actually be obtained in most cases without the use of replicas, by means of a much simpler expansion of the logarithm. Fitting the theoretical model to real neuronal data, we show that the introduction of correlations in the quenched disorder improves the fit, suggesting a possible role of signal correlations-actually detected in real data-in a redundant code. We show that even in the more difficult analysis of the asymptotic regime, an explicit expression for the mutual information can be obtained without resorting to the replica trick despite the presence of quenched disorder, both with a Gaussian and with a more realistic thresholded-Gaussian model. When the stimuli are mixed continuous and discrete, we find that with both models the information seem to grow logarithmically to infinity with the number of neurons and with the inverse of the noise, even though the exact general dependence cannot be derived explicitly for the thresholded-Gaussian model. In the large noise limit, lower values of information were obtained with the thresholded-Gaussian model, for a fixed value of the noise and of the population size. On the contrary, in the asymptotic regime, with very low values of the noise, a lower information value is obtained with the Gaussian model.

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How much do they tell us to move?

Cited by 4 publications

References 4 publications

Analysis of neuronal interactions during adaptation and learning in motor control of primates

Analysis of neuronal interactions during adaptation and learning in motor control of primates

Theoretical model of neuronal population coding of stimuli with both continuous and discrete dimensions

Replica-free evaluation of the neuronal population information with mixed continuous and discrete stimuli: From the linear to the asymptotic regime

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