3,350 research outputs found
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
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