Gibbs distribution analysis of temporal correlations structure in retina ganglion cells

Vasquez, Juan C.; Marre, Olivier; Palacios, Adrián; Berry, Michael; Cessac, Bruno

doi:10.1016/j.jphysparis.2011.11.001

Cited by 47 publications

(91 citation statements)

References 40 publications

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“…Spatiotemporal extensions of the maximum entropy model to include correlations between different times have also been pursued in the past [94][95][96]. However, there are still plenty of interesting questions to study using these models and many valuable opportunities for improving their reliability, uncertainty estimation, and computational efficiency.…”

Section: Discussionmentioning

confidence: 99%

Ensemble inhibition and excitation in the human cortex: An Ising-model analysis with uncertainties

2019

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The pairwise maximum entropy model, also known as the Ising model, has been widely used to analyze the collective activity of neurons. However, controversy persists in the literature about seemingly inconsistent findings, whose significance is unclear due to lack of reliable error estimates. We therefore develop a method for accurately estimating parameter uncertainty based on random walks in parameter space using adaptive Markov Chain Monte Carlo after the convergence of the main optimization algorithm. We apply our method to the spiking patterns of excitatory and inhibitory neurons recorded with multielectrode arrays in the human temporal cortex during the wake-sleep cycle. Our analysis shows that the Ising model captures neuronal collective behavior much better than the independent model during wakefulness, light sleep, and deep sleep when both excitatory (E) and inhibitory (I) neurons are modeled; ignoring the inhibitory effects of I-neurons dramatically overestimates synchrony among E-neurons. Furthermore, information-theoretic measures reveal that the Ising model explains about 80% − 95% of the correlations, depending on sleep state and neuron type. Thermodynamic measures show signatures of criticality, although we take this with a grain of salt as it may be merely a reflection of long-range neural correlations.

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Section: Discussionmentioning

confidence: 99%

Ensemble inhibition and excitation in the human cortex: An Ising-model analysis with uncertainties

2019

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“…In essence, the method extracts the degree to which different rates or events within the system are coupled, or co-occur beyond random chance. This method has previously been used to sucessfully predict the dynamics of neuronal spiking, the flocking behavior of birds and gene regulatory networks [24][25][26] .…”

Section: Resultsmentioning

confidence: 99%

Screening by changes in stereotypical behavior during cell motility

Tweedy

Witzel

Heinrich

et al. 2019

Sci Rep

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Stereotyped behaviors are series of postures that show very little variability between repeats. They have been used to classify the dynamics of individuals, groups and species without reference to the lower-level mechanisms that drive them. Stereotypes are easily identified in animals due to strong constraints on the number, shape, and relative positions of anatomical features, such as limbs, that may be used as landmarks for posture identification. In contrast, the identification of stereotypes in single cells poses a significant challenge as the cell lacks these landmark features, and finding constraints on cell shape is a non-trivial task. Here, we use the maximum caliber variational method to build a minimal model of cell behavior during migration. Without reference to biochemical details, we are able to make behavioral predictions over timescales of minutes using only changes in cell shape over timescales of seconds. We use drug treatment and genetics to demonstrate that maximum caliber descriptors can discriminate between healthy and aberrant migration, thereby showing potential applications for maximum caliber methods in automated disease screening, for example in the identification of behaviors associated with cancer metastasis.

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“…Other models for spatio-temporal interactions are specifically designed for stationary distributions. Vasquez et al (2012) presented the general form of ME models with temporal interactions for stationary distributions:…”

Section: Adding Time To Models Of Synchronous Activitymentioning

confidence: 99%

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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Gibbs distribution analysis of temporal correlations structure in retina ganglion cells

Cited by 47 publications

References 40 publications

Ensemble inhibition and excitation in the human cortex: An Ising-model analysis with uncertainties

Ensemble inhibition and excitation in the human cortex: An Ising-model analysis with uncertainties

Screening by changes in stereotypical behavior during cell motility

Modeling the Correlated Activity of Neural Populations: A Review

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