EnaS: a new software for neural population analysis in large scale spiking networks

doi:10.1186/1471-2202-14-S1-P57International audienceWith the advent of new Multi-Electrode Arrays techniques (MEA), the simultaneous recording of the activity up to hundreds of neurons over a dense configuration supplies today a critical database to unravel the role of specific neural assemblies. Thus, the analysis of spike trains obtained from in vivo or in vitro experimental data requires suitable statistical models and computational tools. The EnaS software, developed by our team, offers new computational methods of spike train statistics, based on Gibbs distributions (in its more general sense, including, but not limited, to the Maximal Entropy - MaxEnt) and taking into account time constraints in neural networks (such as memory effects). It also offers several statistical model choices, some of these models already used in the community (such as the conditional intensity models [5]), and some others developed by us ([1] and [2]), and allows a quantitative comparison between these models. It also offers a control of finite-size sampling effects inherent to empirical statistics

EnaS: a new software for neural population analysis in large scale spiking networks

doi:10.1186/1471-2202-14-S1-P57International audienceWith the advent of new Multi-Electrode Arrays techniques (MEA), the simultaneous recording of the activity up to hundreds of neurons over a dense configuration supplies today a critical database to unravel the role of specific neural assemblies. Thus, the analysis of spike trains obtained from in vivo or in vitro experimental data requires suitable statistical models and computational tools. The EnaS software, developed by our team, offers new computational methods of spike train statistics, based on Gibbs distributions (in its more general sense, including, but not limited, to the Maximal Entropy - MaxEnt) and taking into account time constraints in neural networks (such as memory effects). It also offers several statistical model choices, some of these models already used in the community (such as the conditional intensity models [5]), and some others developed by us ([1] and [2]), and allows a quantitative comparison between these models. It also offers a control of finite-size sampling effects inherent to empirical statistics

Analyzing large-scale spike trains data with spatio-temporal constraints

National audienceRecent experimental advances have made it possible to record several hundred neurons simultaneously in the retina as well as in the cortex. Analyzing such a huge amount of data requires to elaborate statistical, mathematical and numer- ical methods, to describe both the spatio-temporal structure of the population activity and its relevance to sensory coding. Among these methods, the maxi- mum entropy principle has been used to describe the statistics of spike trains. Recall that the maximum entropy principle consists of xing a set of constraints, determined with the empirical average of quantities ("observables") measured on the raster: for example average ring rate of neurons, or pairwise corre- lations. Maximising the statistical entropy given those constraints provides a probability distribution, called a Gibbs distribution, that provides a statistical model to t the data and extrapolate phenomenological laws. Most approaches were restricted to instantaneous observables i.e. quantities corresponding to spikes occurring at the same time (singlets, pairs, triplets and so on).Les récents progrès expérimentaux ont permis d'enregistrer plusieurs centaines de simultanément neurones de la rétine ainsi que dans le cortex. Analyser un tel énorme quantité de données nécessite d'élaborer des statistiques, mathématiques et de nom- méthodes iCal, pour décrire à la fois la structure spatio-temporelle de la population activité et sa pertinence pour codage sensoriel. Parmi ces méthodes, le maxi- principe de l'entropie maman a été utilisé pour décrire les statistiques de trains de potentiels. Rappelons que le principe de l'entropie maximale se compose de xing un ensemble de contraintes, déterminée par la moyenne empirique des quantités ("observables") mesurée sur la trame: pour le taux d'anneau exemple moyenne de neurones, ou par paires corres- tions. Maximiser l'entropie statistique compte tenu de ces contraintes constitue une distribution de probabilité, appelée distribution de Gibbs, qui donne une statistique modèle de t les données et extrapoler les lois phénoménologiques. La plupart des approches ont été limitées aux quantités observables instantanées dire correspondant à pointes se produisant en même temps (maillots, paires, triplets et ainsi de suite)

Analyzing large-scale spike trains data with spatio-temporal constraints

National audienceRecent experimental advances have made it possible to record several hundred neurons simultaneously in the retina as well as in the cortex. Analyzing such a huge amount of data requires to elaborate statistical, mathematical and numer- ical methods, to describe both the spatio-temporal structure of the population activity and its relevance to sensory coding. Among these methods, the maxi- mum entropy principle has been used to describe the statistics of spike trains. Recall that the maximum entropy principle consists of xing a set of constraints, determined with the empirical average of quantities ("observables") measured on the raster: for example average ring rate of neurons, or pairwise corre- lations. Maximising the statistical entropy given those constraints provides a probability distribution, called a Gibbs distribution, that provides a statistical model to t the data and extrapolate phenomenological laws. Most approaches were restricted to instantaneous observables i.e. quantities corresponding to spikes occurring at the same time (singlets, pairs, triplets and so on).Les récents progrès expérimentaux ont permis d'enregistrer plusieurs centaines de simultanément neurones de la rétine ainsi que dans le cortex. Analyser un tel énorme quantité de données nécessite d'élaborer des statistiques, mathématiques et de nom- méthodes iCal, pour décrire à la fois la structure spatio-temporelle de la population activité et sa pertinence pour codage sensoriel. Parmi ces méthodes, le maxi- principe de l'entropie maman a été utilisé pour décrire les statistiques de trains de potentiels. Rappelons que le principe de l'entropie maximale se compose de xing un ensemble de contraintes, déterminée par la moyenne empirique des quantités ("observables") mesurée sur la trame: pour le taux d'anneau exemple moyenne de neurones, ou par paires corres- tions. Maximiser l'entropie statistique compte tenu de ces contraintes constitue une distribution de probabilité, appelée distribution de Gibbs, qui donne une statistique modèle de t les données et extrapoler les lois phénoménologiques. La plupart des approches ont été limitées aux quantités observables instantanées dire correspondant à pointes se produisant en même temps (maillots, paires, triplets et ainsi de suite)

PRANAS: A New Platform for Retinal Analysis and Simulation

International audienceThe retina encodes visual scenes by trains of action potentials that are sent to the brain via the optic nerve. In this paper, we describe a new free access user-end software allowing to better understand this coding. It is called PRANAS (https://pranas.inria.fr), standing for Platform for Retinal ANalysis And Simulation. PRANAS targets neuroscientists and modelers by providing a unique set of retina-related tools. PRANAS integrates a retina simulator allowing large scale simulations while keeping a strong biological plausibility and a toolbox for the analysis of spike train population statistics. The statistical method (entropy maximization under constraints) takes into account both spatial and temporal correlations as constraints, allowing to analyze the effects of memory on statistics. PRANAS also integrates a tool computing and representing in 3D (time-space) receptive fields. All these tools are accessible through a friendly graphical user interface. The most CPU-costly of them have been implemented to run in parallel