Spatio-temporal spike trains analysis for large scale networks using maximum entropy principle and Monte-Carlo method

Understanding the dynamics of neural networks is a major challenge in experimental neuroscience. For that purpose, a modelling of the recorded activity that reproduces the main statistics of the data is required. In a first part, we present a review on recent results dealing with spike train statistics analysis using maximum entropy models (MaxEnt). Most of these studies have been focusing on modelling synchronous spike patterns, leaving aside the temporal dynamics of the neural activity. However, the maximum entropy principle can be generalized to the temporal case, leading to Markovian models where memory effects and time correlations in the dynamics are properly taken into account. In a second part, we present a new method based on Monte-Carlo sampling which is suited for the fitting of large-scale spatio-temporal MaxEnt models. The formalism and the tools presented here will be essential to fit MaxEnt spatio-temporal models to large neural ensembles.Comment: 41 pages, 10 figure

Dynamical criticality in the collective activity of a population of retinal neurons

Recent experimental results based on multi-electrode and imaging techniques have reinvigorated the idea that large neural networks operate near a critical point, between order and disorder. However, evidence for criticality has relied on the definition of arbitrary order parameters, or on models that do not address the dynamical nature of network activity. Here we introduce a novel approach to assess criticality that overcomes these limitations, while encompassing and generalizing previous criteria. We find a simple model to describe the global activity of large populations of ganglion cells in the rat retina, and show that their statistics are poised near a critical point. Taking into account the temporal dynamics of the activity greatly enhances the evidence for criticality, revealing it where previous methods would not. The approach is general and could be used in other biological networks

A tractable method for describing complex couplings between neurons and population rate

Neurons within a population are strongly correlated, but how to simply capture these correlations is still a matter of debate. Recent studies have shown that the activity of each cell is influenced by the population rate, defined as the summed activity of all neurons in the population. However, an explicit, tractable model for these interactions is still lacking. Here we build a probabilistic model of population activity that reproduces the firing rate of each cell, the distribution of the population rate, and the linear coupling between them. This model is tractable, meaning that its parameters can be learned in a few seconds on a standard computer even for large population recordings. We inferred our model for a population of 160 neurons in the salamander retina. In this population, single-cell firing rates depended in unexpected ways on the population rate. In particular, some cells had a preferred population rate at which they were most likely to fire. These complex dependencies could not be explained by a linear coupling between the cell and the population rate. We designed a more general, still tractable model that could fully account for these non-linear dependencies. We thus provide a simple and computationally tractable way to learn models that reproduce the dependence of each neuron on the population rate

Blindfold learning of an accurate neural metric

The brain has no direct access to physical stimuli, but only to the spiking activity evoked in sensory organs. It is unclear how the brain can structure its representation of the world based on differences between those noisy, correlated responses alone. Here we show how to build a distance map of responses from the structure of the population activity of retinal ganglion cells, allowing for the accurate discrimination of distinct visual stimuli from the retinal response. We introduce the Temporal Restricted Boltzmann Machine to learn the spatiotemporal structure of the population activity, and use this model to define a distance between spike trains. We show that this metric outperforms existing neural distances at discriminating pairs of stimuli that are barely distinguishable. The proposed method provides a generic and biologically plausible way to learn to associate similar stimuli based on their spiking responses, without any other knowledge of these stimuli

Closed-loop estimation of retinal network sensitivity reveals signature of efficient coding

According to the theory of efficient coding, sensory systems are adapted to represent natural scenes with high fidelity and at minimal metabolic cost. Testing this hypothesis for sensory structures performing non-linear computations on high dimensional stimuli is still an open challenge. Here we develop a method to characterize the sensitivity of the retinal network to perturbations of a stimulus. Using closed-loop experiments, we explore selectively the space of possible perturbations around a given stimulus. We then show that the response of the retinal population to these small perturbations can be described by a local linear model. Using this model, we computed the sensitivity of the neural response to arbitrary temporal perturbations of the stimulus, and found a peak in the sensitivity as a function of the frequency of the perturbations. Based on a minimal theory of sensory processing, we argue that this peak is set to maximize information transmission. Our approach is relevant to testing the efficient coding hypothesis locally in any context where no reliable encoding model is known

A simple model for low variability in neural spike trains

Neural noise sets a limit to information transmission in sensory systems. In several areas, the spiking response (to a repeated stimulus) has shown a higher degree of regularity than predicted by a Poisson process. However, a simple model to explain this low variability is still lacking. Here we introduce a new model, with a correction to Poisson statistics, which can accurately predict the regularity of neural spike trains in response to a repeated stimulus. The model has only two parameters, but can reproduce the observed variability in retinal recordings in various conditions. We show analytically why this approximation can work. In a model of the spike emitting process where a refractory period is assumed, we derive that our simple correction can well approximate the spike train statistics over a broad range of firing rates. Our model can be easily plugged to stimulus processing models, like Linear-nonlinear model or its generalizations, to replace the Poisson spike train hypothesis that is commonly assumed. It estimates the amount of information transmitted much more accurately than Poisson models in retinal recordings. Thanks to its simplicity this model has the potential to explain low variability in other areas

La adopción internacional y las asociaciones de familias adoptantes: un ejemplo de sociedad civil virtual global.

global iniciado al final de la Segunda Guerra Mundial y la guerra de Corea al que España se incorporó en la década de 1990, la magnitud adquirida en pocos años requirió de un singular esfuerzo legislativo a la vez que estimuló movimientos sociales y formas de organización de la sociedad civil sobre nuevos temas. Las asociaciones de padres adoptivos constituyen un buen ejemplo de ello. Se trata, en general, de relaciones esencialmente virtuales, complementadas con uno o dos grandes encuentros anuales que hacen que, mientras que las sedes físicas de las asociaciones -cuando existenpermanecen casi vacías, las sedes virtuales, listas de distribución y chats mantienen una actividad casi frenética

La cerilla de Darwin (o cómo los juicios sobre la ausencia de información pueden inducir la desvalorización social).

El día después del inicio del año 2000, el mundo, especialmente el mundo rico, respiró aliviado: el tan temido 'efecto 2000' sobre los ordenadores se había superado sin nada que lamentar. No sólo no se habían registrado problemas en cuestiones tan delicadas como las vinculadas a la carrera espacial o armamentista sino que tampoco el ciudadano medio había registrado inconvenientes adicionales en su cotidianeidad: el agua corriente seguía saliendo de los grifos, la energía eléctrica no se había interrumpido, los cajeros automáticos continuaban dando dinero, los hospitales cubrían sus servicios de urgencias y todos y cada uno de los aspectos de la vida cotidiana seguían su curso y también aquellos a los que habitualmente no accede el ciudadano medio pero que, a juzgar por el buen funcionamiento de los más inmediatos, pudo imaginar que estaban bajo control y por lo tanto no constituían una amenaza. El primer mundo, el mundo 'civilizado', tenía buenas razones para respirar tranquilo: la varias veces millonaria cifra de dinero que se había invertido en prever las posibles consecuencias del 'efecto 2000' había merecido la pena por lo que los contribuyentes no cuestionarían tal inversión y los políticos y gestores habrían cumplido con su deber de prevenir antes que curar