General Poisson Exact Breakdown of the Mutual Information to Study the Role of Correlations in Populations of Neurons

Scaglione, Alessandro; Moxon, K. A.; Foffani, Guglielmo

doi:10.1162/neco.2010.04-09-989

Cited by 7 publications

(8 citation statements)

References 21 publications

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“…This consideration is certainly not the case. In fact, the information conveyed by variability, reported here, is actually the information conveyed by sub-Poisson variability in addition to the information conveyed by the average firing rate alone in the Poisson regime (18,25). With the notable exceptions of a methodological work applying the series expansion to data from the macaque inferior temporal visual cortex (54) and our methodological work applying the general Poisson exact breakdown to data from the rat whisker cortex (25), the considerable contribution of trial-to-trial spike-count variability to rate coding has been so far largely unexplored.…”

Section: Trial-to-trial Response Variability Contributes To the Informentioning

confidence: 74%

“…Conversely, Poisson firing represents a response variability characterized by absence of autocorrelation in the spike count: If a spike occurs, the probability of observing a second spike is the same as for the first spike, because all spikes are completely independent. To gain insight into the role of variability in neural coding, in the next sections we use our general Poisson exact breakdown of the mutual information (25) to assess the possible informational advantages or disadvantages of sub-Poisson firing compared with Poisson firing for discriminating stimulus location in our VPM neurons.…”

Section: Resultsmentioning

confidence: 99%

“…39 and 40), in the present work we focused on spike count. To investigate the role of trial-to-trial spike-count variability for neural coding, we used both classical neurophysiological measures such as the Fano factor (13,16) and a rigorous information theory approach recently developed by us: the general Poisson exact breakdown of mutual information (25). This information theory approach represents an integrative generalization of several previous information theory measures, including series expansion (34), exact breakdown (41), synergy/redundancy between neurons (36), and information lost by an optimal decoder that assumes absence of correlations between neurons (35,41).…”

Section: Discussionmentioning

confidence: 99%

“…We then calculated the information carried by the observed responses of the neurons (I). The difference between the two (Î cor-auto = I − Î lin ) defines the information due to count autocorrelations in single neurons and represents the potential informational advantage of the sub-Poisson regime compared with the Poisson regime (25).…”

Section: Trial-to-trial Response Variability Contributes To the Informentioning

confidence: 99%

See 3 more Smart Citations

Trial-to-trial variability in the responses of neurons carries information about stimulus location in the rat whisker thalamus

Scaglione

Moxon

Aguilar

et al. 2011

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

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From the perspective of neural coding, the considerable trial-totrial variability in the responses of neurons to sensory stimuli is puzzling. Trial-to-trial response variability is typically interpreted in terms of "noise" (i.e., it represents either intrinsic noise of the system or information unrelated to the stimuli). However, trial-totrial response variability can be considerably different across stimuli, suggesting that it could also provide an important contribution to the information conveyed by the neural responses about the stimuli. To test this hypothesis, we addressed the problem of discriminating stimulus location from the spike-count responses of neurons recorded in the ventro-postero-medial (VPM) nucleus of the thalamus in anesthetized rats. Using a recently developed information theory approach, we verified that differences between stimuli in the trial-to-trial spike-count variability of the responses provided an important contribution to the overall information carried by the neurons. In addition, we found that the relatively reliable (sub-Poisson) firing regime of our VPM neurons was not only more informative, but also more redundant between neurons compared with a more variable (Poisson) firing regime with the same total number of spikes. The typical increase in trial-to-trial response variability from the periphery to the cortex could therefore serve as a strategy to reduce redundancy between neurons and promote efficient sparse coding distributed in large populations of neurons. Overall, our data suggest that the trial-to-trial response variability plays a critical role in establishing the tradeoff between total information and redundancy between neurons in population codes.A major challenge for system neuroscience is to understand the basic elements of the neural code. Because single neurons typically respond to different stimuli with different average firing rates, possibly the simplest hypothesis is that neurons use a ratecoding scheme to represent sensory information (1, 2). However, average firing rates do not necessarily represent a complete description of the neural responses, partly because neurons can respond with a high degree of variability to different repetitions of the same stimulus (3). Trial-to-trial variability in the neural responses can be due to synaptic noise (4, 5) or can represent information that is not directly related to the stimulus, e.g., information about the state of the system (6-8). However, the relation between trial-to-trial variability in the responses of neurons and the information conveyed by those neurons remains unclear (9).The rate-coding hypothesis was originally formulated on the basis of classic works in peripheral nerves (10,11). Indeed, at the first stages of sensory processing, the trial-to-trial variability can be so low that neural responses can be almost deterministic (12-14), i.e., neurons respond with virtually the same number of spikes to different repetitions of the same stimulus. In the ideal deterministic regime, single-trial responses ar...

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Section: Trial-to-trial Response Variability Contributes To the Informentioning

confidence: 74%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Trial-to-trial Response Variability Contributes To the Informentioning

confidence: 99%

See 2 more Smart Citations

Trial-to-trial variability in the responses of neurons carries information about stimulus location in the rat whisker thalamus

Scaglione

Moxon

Aguilar

et al. 2011

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

View full text Add to dashboard Cite

show abstract

“…The quantity I cor−dep equals ∆I 1 . Therefore the quantify ∆I k in Eq (21) can also be interpreted as the contribution of stimulus modulations of interactions of order higher than k. More recent generalizations of the information breakdown have focused on how to carefully separate the contributions of interactions between spikes emitted by different neurons from the contributions of interactions among spikes from the same neuron (Scaglione et al, 2008(Scaglione et al, , 2010.…”

Section: Other Information Theoretic Measures Of the Importance Of Nementioning

confidence: 99%

Information-theoretic methods for studying population codes

et al. 2010

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Population coding is the quantitative study of which algorithms or representations are used by the brain to combine together and evaluate the messages carried by different neurons. Here, we review an information-theoretic approach to population coding. We first discuss how to compute the information carried by simultaneously recorded neural populations, and in particular how to reduce the limited sampling bias which affects calculation of information from a limited amount of experimental data. We then discuss how to quantify the contribution of individual members of the population, or the interaction between them, to the overall information encoded by the considered group of neurons. We focus in particular on evaluating what is the contribution of interactions up to any given order to the total information. We illustrate this formalism with applications to simulated data with realistic neuronal statistics and to real simultaneous recordings of multiple spike trains.

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