Maximum entropy models reveal the correlation structure in cortical neural activity during wakefulness and sleep

Nghiem, Trang-Anh; Teleńczuk, Bartosz; Marre, Olivier; Destexhe, Alain

doi:10.1101/243857

Cited by 9 publications

(7 citation statements)

References 26 publications

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“…A major challenge is also to account for the diversity of stimuli or even of neural states present in the recordings, and recapitulate this diversity in a single model. It is still challenging for a model to account for both awake animals and slowwave sleep, as neurons behave differently in these regimes (Nghiem et al, 2018). Coarse-grained, modular models such as the hierarchical model (Santos et al, 2010), with different types of interactions at different spatial scales, offer promising avenues for this feat.…”

Section: Discussionmentioning

confidence: 99%

“…In the salamander retina (Gardella et al, 2016), the population activity explained 50% of pairwise correlations, which was similar to what was reported in mouse V1 . Recent application of the population coupling model to the human and monkey cortices showed that the model explained the collective activity well during sleep, but that detailed interactions between specific pairs of neurons mattered during wakefulness (Nghiem et al, 2018). SImilarly, in mouse V1, population rate predicted better pairwise correlations during synchronized states, and very little during desynchronize states .…”

Section: Models Of Population Couplingmentioning

confidence: 99%

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Modeling the Correlated Activity of Neural Populations: A Review

Gardella

Marre

Mora³

2019

Neural Computation

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The principles of neural encoding and computations are inherently collective and usually involve large populations of interacting neurons with highly correlated activities. While theories of neural function have long recognized the importance of collective effects in populations of neurons, only in the past two decades has it become possible to record from many cells simulatenously using advanced experimental techniques with singlespike resolution, and to relate these correlations to function and behaviour. This review focuses on the modeling and inference approaches that have been recently developed to describe the correlated spiking activity of populations of neurons. We cover a variety of models describing correlations between pairs of neurons as well as between larger groups, synchronous or delayed in time, with or without the explicit influence of the stimulus, and including or not latent variables. We discuss the advantages and drawbacks or each method, as well as the computational challenges related to their application to recordings of ever larger populations.

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Section: Discussionmentioning

confidence: 99%

Section: Models Of Population Couplingmentioning

confidence: 99%

Modeling the Correlated Activity of Neural Populations: A Review

Gardella

Marre

Mora³

2019

Neural Computation

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“…In recent years, the field of statistical inference applied to neuronal dynamics has mainly focused on devising models and procedures that could reliably recover the real or effective synaptic structure of a network of neurons, under different dynamical and stimulation conditions 3,30,31 , even though, more often than not, an experimental ground truth was not readily available.…”

Section: /13mentioning

confidence: 99%

Spontaneous activity emerging from an inferred network model captures complex spatio-temporal dynamics of spike data

Capone¹,

Gigante²,

Giudice³

2018

Preprint

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Inference methods are widely used to recover effective models from observed data. However, few studies attempted to investigate the dynamics of inferred models in neuroscience, and none, to our knowledge, at the network level. We introduce a principled modification of a widely used generalized linear model (GLM), and learn its structural and dynamic parameters from in-vitro spike data. The spontaneous activity of the new model captures prominent features of the non-stationary and non-linear dynamics displayed by the biological network, where the reference GLM largely fails, and also reflects fine-grained spatio-temporal dynamical features. Two ingredients were key for success. The first is a saturating transfer function: beyond its biological plausibility, it limits the neuron's information transfer, improving robustness against endogenous and external noise. The second is a super-Poisson spikes generative mechanism; it accounts for the undersampling of the network, and allows the model neuron to flexibly incorporate the observed activity fluctuations. 2/13

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“…Entropy maximization or related concepts has been frequently utilized in the past ten years to analyze large biological datasets in various fields. These fields range from determining macromolecular interactions and structures [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ] to inferring signaling [ 21 , 22 , 23 , 24 , 25 ] and regulatory networks [ 26 , 27 , 28 ] and the coding organization in neural populations [ 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 ] based on DNA sequence analyzes (the detection of specific binding sites, for instance) [ 42 , 43 , 44 , 45 , 46 ]. MEM is a powerful vehicle to reconstruct images based on various datasets.…”

Section: Introductionmentioning

confidence: 99%

Modified Maximum Entropy Method and Estimating the AIF via DCE-MRI Data Analysis

Farsani

Schmid

2022

Entropy

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Background: For the kinetic models used in contrast-based medical imaging, the assignment of the arterial input function named AIF is essential for the estimation of the physiological parameters of the tissue via solving an optimization problem. Objective: In the current study, we estimate the AIF relayed on the modified maximum entropy method. The effectiveness of several numerical methods to determine kinetic parameters and the AIF is evaluated—in situations where enough information about the AIF is not available. The purpose of this study is to identify an appropriate method for estimating this function. Materials and Methods: The modified algorithm is a mixture of the maximum entropy approach with an optimization method, named the teaching-learning method. In here, we applied this algorithm in a Bayesian framework to estimate the kinetic parameters when specifying the unique form of the AIF by the maximum entropy method. We assessed the proficiency of the proposed method for assigning the kinetic parameters in the dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), when determining AIF with some other parameter-estimation methods and a standard fixed AIF method. A previously analyzed dataset consisting of contrast agent concentrations in tissue and plasma was used. Results and Conclusions: We compared the accuracy of the results for the estimated parameters obtained from the MMEM with those of the empirical method, maximum likelihood method, moment matching (“method of moments”), the least-square method, the modified maximum likelihood approach, and our previous work. Since the current algorithm does not have the problem of starting point in the parameter estimation phase, it could find the best and nearest model to the empirical model of data, and therefore, the results indicated the Weibull distribution as an appropriate and robust AIF and also illustrated the power and effectiveness of the proposed method to estimate the kinetic parameters.

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Maximum entropy models reveal the correlation structure in cortical neural activity during wakefulness and sleep

Cited by 9 publications

References 26 publications

Modeling the Correlated Activity of Neural Populations: A Review

Modeling the Correlated Activity of Neural Populations: A Review

Spontaneous activity emerging from an inferred network model captures complex spatio-temporal dynamics of spike data

Modified Maximum Entropy Method and Estimating the AIF via DCE-MRI Data Analysis

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