Information-theoretic methods for studying population codes

Ince, Robin A. A.; Senatore, R; Arabzadeh, Ehsan; Montani, Fernando; Diamond, Mathew E.; Panzeri, Stefano

doi:10.1016/j.neunet.2010.05.008

Cited by 51 publications

(78 citation statements)

References 66 publications

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“…However, there is a long-standing debate over the role and significance of concerted activity in neural information processing (Averbeck and Lee 2004). A number of studies have revealed that concerted neuronal activity encodes extra stimulus information which cannot be extracted by the responses of single neurons, and they play important roles in animal behaviors (Dan et al 1998;Ince et al 2010;Ishikane et al 2005), whereas other work suggested that neuronal correlation conveys little information and can be largely neglected (Meytlis et al 2012;Nirenberg et al 2001;Oizumi et al 2010). In this study, we observed the changes in information representation throughout the process of adaptation.…”

Section: Discussionmentioning

confidence: 99%

Shifted encoding strategy in retinal luminance adaptation: from firing rate to neural correlation

Xiao

Zhang

Xing

et al. 2013

Journal of Neurophysiology

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Neuronal responses to prolonged stimulation attenuate over time. Here, we ask a fundamental question: is adaptation a simple process for the neural system during which sustained input is ignored, or is it actually part of a strategy for the neural system to adjust its encoding properties dynamically? After simultaneously recording the activities of a group of bullfrog's retinal ganglion cells (dimming detectors) in response to sustained dimming stimulation, we applied a combination of information analysis approaches to explore the time-dependent nature of information encoding during the adaptation. We found that at the early stage of the adaptation, the stimulus information was mainly encoded in firing rates, whereas at the late stage of the adaptation, it was more encoded in neural correlations. Such a transition in encoding properties is not a simple consequence of the attenuation of neuronal firing rates, but rather involves an active change in the neural correlation strengths, suggesting that it is a strategy adopted by the neural system for functional purposes. Our results reveal that in encoding a prolonged stimulation, the neural system may utilize concerted, but less active, firings of neurons to encode information.retinal ganglion cell; luminance adaptation; information coding; neural correlation; discrimination ADAPTATION REFERS TO A GENERAL phenomenon in which a neural system adjusts its response property according to the statistics of external inputs (Kohn 2007;Wark et al. 2007). It has been widely suggested that adaptation underlies the strategy for a neural system to utilize its resource efficiently to encode stimulus information (Fairhall et al. 2001;Gutnisky and Dragoi 2008;Lesica et al. 2007;Maravall et al. 2007). For instance, it was found that the tuning functions of sensory neurons are optimized to match the variance of inputs in a natural environment, so that high encoding accuracy for the whole range of stimuli is achieved (Laughlin 1981). In practice, adaptation can also occur very rapidly in response to sudden changes in the input statistics, so that the stimulus information is transmitted with high fidelity (Fairhall et al. 2001;Sharpee et al. 2006). A recent experimental work reported that in addition to enhancing information representation, adaptation can regulate information content transmitted in the sensory pathway (Wang et al. 2010). Thus, to fully understand how the brain processes stimulus information in a natural dynamical environment, it is critical to unveil the computational roles associated with adaptation.Luminance adaptation, in which a neural system is exposed to a sustained stimulation with constant luminance, is a fundamental visual adaptation process whose biophysical mechanisms and potential computational roles have been intensively studied (Wark et al. 2007). However, the detailed time course as to how the stimulus information is encoded dynamically in the varying neural responses during adaptation remains largely unresolved (Kastner and Baccus 2011). In luminance ad...

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Section: Discussionmentioning

confidence: 99%

Shifted encoding strategy in retinal luminance adaptation: from firing rate to neural correlation

Xiao

Zhang

Xing

et al. 2013

Journal of Neurophysiology

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“…The reason for this failure is that laboratory data sample the space of possible activity patterns rather sparsely, and this sparsity undermines our confidence in the knowledge of the underlying distribution, a knowledge that is critical for the determination of the probabilities in Equation (1). This difficulty is referred to in the literature as the small sample bias, and several ad hoc counter-measures have been proposed, although those have been limited to a small handful of neurons [3][4][5].…”

Section: Methods For Estimating Information Content In Single Spike Tmentioning

confidence: 99%

The Measurement of Information Transmitted by a Neural Population: Promises and Challenges

Crumiller

Knight

Kaplan

2013

Entropy

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All brain functions require the coordinated activity of many neurons, and therefore there is considerable interest in estimating the amount of information that the discharge of a neural population transmits to its targets. In the past, such estimates had presented a significant challenge for populations of more than a few neurons, but we have recently described a novel method for providing such estimates for populations of essentially arbitrary size. Here, we explore the influence of some important aspects of the neuronal population discharge on such estimates. In particular, we investigate the roles of mean firing rate and of the degree and nature of correlations among neurons. The results provide constraints on the applicability of our new method and should help neuroscientists determine whether such an application is appropriate for their data.

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“…Accordingly, the information loss decompositions can be connected to hierarchical decompositions of the mutual information [33,34]. Furthermore, information loss associated with the preservation of only certain marginal distributions can be formulated in terms of maximum entropy [24], which renders loss lattices suitable to extend previous work studying neural population coding with the maximum entropy framework [15]. We will use the notation L(S; α) to refer to the cumulative terms of the information loss decomposition, in comparison to the cumulative terms of information gain I(S; α).…”

Section: Decompositions Of Mutual Information Lossmentioning

confidence: 99%

“…The bivariate redundancy measure also already used in [24] corresponds to I(S; i.j). The rest of incremental terms can be obtained from the information loss lattice using Equation (15). Note that we could have proceeded in a similar way starting from a definition of the cumulative terms in the gain lattice, such as I min , and then determining the terms of the loss lattice.…”

Section: Dual Decompositions Of Information Gain and Information Lossmentioning

confidence: 99%

Synergy and Redundancy in Dual Decompositions of Mutual Information Gain and Information Loss

Chicharro

Panzeri

2017

Entropy

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Williams and Beer (2010) proposed a nonnegative mutual information decomposition, based on the construction of information gain lattices, which allows separating the information that a set of variables contains about another variable into components, interpretable as the unique information of one variable, or redundant and synergy components. In this work, we extend this framework focusing on the lattices that underpin the decomposition. We generalize the type of constructible lattices and examine the relations between different lattices, for example, relating bivariate and trivariate decompositions. We point out that, in information gain lattices, redundancy components are invariant across decompositions, but unique and synergy components are decomposition-dependent. Exploiting the connection between different lattices, we propose a procedure to construct, in the general multivariate case, information gain decompositions from measures of synergy or unique information. We then introduce an alternative type of lattices, information loss lattices, with the role and invariance properties of redundancy and synergy components reversed with respect to gain lattices, and which provide an alternative procedure to build multivariate decompositions. We finally show how information gain and information loss dual lattices lead to a self-consistent unique decomposition, which allows a deeper understanding of the origin and meaning of synergy and redundancy.

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Information-theoretic methods for studying population codes

Cited by 51 publications

References 66 publications

Shifted encoding strategy in retinal luminance adaptation: from firing rate to neural correlation

Shifted encoding strategy in retinal luminance adaptation: from firing rate to neural correlation

The Measurement of Information Transmitted by a Neural Population: Promises and Challenges

Synergy and Redundancy in Dual Decompositions of Mutual Information Gain and Information Loss

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