371 research outputs found
Numerical simulation of a binary communication channel: Comparison between a replica calculation and an exact solution
The mutual information of a single-layer perceptron with Gaussian inputs
and deterministic binary outputs is studied by numerical simulations. The
relevant parameters of the problem are the ratio between the number of output
and input units, , and those describing the two-point
correlations between inputs. The main motivation of this work refers to the
comparison between the replica computation of the mutual information and an
analytical solution valid up to . The most relevant results
are: (1) the simulation supports the validity of the analytical prediction, and
(2) it also verifies a previously proposed conjecture that the replica solution
interpolates well between large and small values of .Comment: 6 pages, 8 figures, LaTeX fil
Decentralized multi-tasks distribution in heterogeneous robot teams by means of ant colony optimization and learning automata
This paper focuses on the general problem of coordinating
multiple robots. More specifically, it addresses the self-election of heterogeneous specialized tasks by autonomous robots. In this paper we focus on a specifically distributed or decentralized approach as we are particularly
interested on decentralized solution where the robots themselves autonomously and in an individual manner, are responsible of selecting a particular task so that all the existing tasks are optimally distributed and executed. In this regard, we have established an experimental scenario to solve the corresponding multi-tasks distribution problem and we propose a solution using two different approaches by applying Ant Colony Optimization-based deterministic algorithms as well as Learning Automata-based probabilistic algorithms. We have evaluated the robustness of the algorithm, perturbing the number of pending loads to simulate the robot’s error in estimating the real number of pending tasks and also the dynamic generation of loads through time. The paper ends with a critical discussion of experimental results
Coordinated population activity underlying texture discrimination in rat barrel cortex
Rodents can robustly distinguish fine differences in texture using their whiskers, a capacity that depends on neuronal activity in primary somatosensory \u201cbarrel\u201d cortex. Here we explore how texture was collectively encoded by populations of three to seven neuronal clusters simultaneously recorded from barrel cortex while a rat performed a discrimination task. Each cluster corresponded to the single-unit or multiunit activity recorded at an individual electrode. To learn how the firing of different clusters combines to represent texture, we computed population activity vectors across moving time windows and extracted the signal available in the optimal linear combination of clusters. We quantified this signal using receiver operating characteristic analysis and compared it to that available in single clusters. Texture encoding was heterogeneous across neuronal clusters, and only a minority of clusters carried signals strong enough to support stimulus discrimination on their own. However, jointly recorded groups of clusters were always able to support texture discrimination at a statistically significant level, even in sessions where no individual cluster represented the stimulus. The discriminative capacity of neuronal activity was degraded when error trials were included in the data, compared to only correct trials, suggesting a link between the neuronal activity and the animal's performance. These analyses indicate that small groups of barrel cortex neurons can robustly represent texture identity through synergistic interactions, and suggest that neurons downstream to barrel cortex could extract texture identity on single trials through simple linear combination of barrel cortex responses
Un nuevo método para el control de calidad de los datos en series temporales
ArtÃculo de revistaEste trabajo resume la aplicacion de una derivacion del programa TRAMO denominada TERROR (TRAMO for erros), al control de calidad de los datos que el Banco de España recibe regularmente de entidades, y que sirven de base para la construccion de sus series agregadas. (ad
Neural Decision Boundaries for Maximal Information Transmission
We consider here how to separate multidimensional signals into two
categories, such that the binary decision transmits the maximum possible
information transmitted about those signals. Our motivation comes from the
nervous system, where neurons process multidimensional signals into a binary
sequence of responses (spikes). In a small noise limit, we derive a general
equation for the decision boundary that locally relates its curvature to the
probability distribution of inputs. We show that for Gaussian inputs the
optimal boundaries are planar, but for non-Gaussian inputs the curvature is
nonzero. As an example, we consider exponentially distributed inputs, which are
known to approximate a variety of signals from natural environment.Comment: 5 pages, 3 figure
Refinements to rodent head fixation and fluid/food control for neuroscience
The use of head fixation in mice is increasingly common in research, its use having initially been restricted to the field of sensory neuroscience. Head restraint has often been combined with fluid control, rather than food restriction, to motivate behaviour, but this too is now in use for both restrained and non-restrained animals. Despite this, there is little guidance on how best to employ these techniques to optimise both scientific outcomes and animal welfare. This article summarises current practices and provides recommendations to improve animal wellbeing and data quality, based on a survey of the community, literature reviews, and the expert opinion and practical experience of an international working group convened by the UK's National Centre for the Replacement, Refinement and Reduction of Animals in Research (NC3Rs). Topics covered include head fixation surgery and post-operative care, habituation to restraint, and the use of fluid/food control to motivate performance. We also discuss some recent developments that may offer alternative ways to collect data from large numbers of behavioural trials without the need for restraint. The aim is to provide support for researchers at all levels, animal care staff, and ethics committees to refine procedures and practices in line with the refinement principle of the 3Rs
Intrinsic gain modulation and adaptive neural coding
In many cases, the computation of a neural system can be reduced to a
receptive field, or a set of linear filters, and a thresholding function, or
gain curve, which determines the firing probability; this is known as a
linear/nonlinear model. In some forms of sensory adaptation, these linear
filters and gain curve adjust very rapidly to changes in the variance of a
randomly varying driving input. An apparently similar but previously unrelated
issue is the observation of gain control by background noise in cortical
neurons: the slope of the firing rate vs current (f-I) curve changes with the
variance of background random input. Here, we show a direct correspondence
between these two observations by relating variance-dependent changes in the
gain of f-I curves to characteristics of the changing empirical
linear/nonlinear model obtained by sampling. In the case that the underlying
system is fixed, we derive relationships relating the change of the gain with
respect to both mean and variance with the receptive fields derived from
reverse correlation on a white noise stimulus. Using two conductance-based
model neurons that display distinct gain modulation properties through a simple
change in parameters, we show that coding properties of both these models
quantitatively satisfy the predicted relationships. Our results describe how
both variance-dependent gain modulation and adaptive neural computation result
from intrinsic nonlinearity.Comment: 24 pages, 4 figures, 1 supporting informatio
Forecasting binary longitudinal data by a functional PC-ARIMA model
In order to forecast time evolution of a binary response variable from a related continuous time series a functional logit model is proposed. The estimation of this model from discrete time observations of the predictor is solved by using functional principal component analysis and ARIMA modelling of the associated discrete time series of principal components. The proposed model is applied to forecast the risk of drought from El Niño phenomenon.Projects MTM2007-63793 from Dirección General de Investigación, Ministerio de Educación y Ciencia, Spain and P06-FQM-01470 from ConsejerÃa de Innovación Ciencia y Empresa, Junta de AndalucÃa, Spai
- …