8,203 research outputs found
Dynamics and spike trains statistics in conductance-based Integrate-and-Fire neural networks with chemical and electric synapses
We investigate the effect of electric synapses (gap junctions) on collective
neuronal dynamics and spike statistics in a conductance-based
Integrate-and-Fire neural network, driven by a Brownian noise, where
conductances depend upon spike history. We compute explicitly the time
evolution operator and show that, given the spike-history of the network and
the membrane potentials at a given time, the further dynamical evolution can be
written in a closed form. We show that spike train statistics is described by a
Gibbs distribution whose potential can be approximated with an explicit
formula, when the noise is weak. This potential form encompasses existing
models for spike trains statistics analysis such as maximum entropy models or
Generalized Linear Models (GLM). We also discuss the different types of
correlations: those induced by a shared stimulus and those induced by neurons
interactions.Comment: 42 pages, 1 figure, submitte
Exact computation of the Maximum Entropy Potential of spiking neural networks models
Understanding how stimuli and synaptic connectivity in uence the statistics
of spike patterns in neural networks is a central question in computational
neuroscience. Maximum Entropy approach has been successfully used to
characterize the statistical response of simultaneously recorded spiking
neurons responding to stimuli. But, in spite of good performance in terms of
prediction, the fitting parameters do not explain the underlying mechanistic
causes of the observed correlations. On the other hand, mathematical models of
spiking neurons (neuro-mimetic models) provide a probabilistic mapping between
stimulus, network architecture and spike patterns in terms of conditional
proba- bilities. In this paper we build an exact analytical mapping between
neuro-mimetic and Maximum Entropy models.Comment: arXiv admin note: text overlap with arXiv:1309.587
The Demographic Transition and the Sexual Division of Labor
This paper presents a theory where increases in female labor force participation and reductions in the gender wage-gap are generated as part of the same process of demographic transition that leads to reductions in fertility. There have been significant increases in the labor supply of women in the last decades, both in developed and developing countries. Traditional views explain this trend through the effects of reduced fertility and/or increased women's wages. The paper suggests that all these changes can be understood as part of a single process of demographic change, triggered by reductions in mortality. Mortality reductions affect the incentives of individuals to invest in human capital and to have children. Particularly, gains in adult longevity reduce fertility, increase investments in market human capital, increase female labor force participation, and reduce the wage differential between men and women. Child mortality reductions cannot generate this same pattern of changes. The model reconciles the increase in female labor market participation with the timing of age-specific mortality reductions observed during the demographic transition. The paper presents the first model to link the change in the role of women in society to, ultimately, the reductions in mortality that characterize the demographic transitiongender wage gap, demographic transition, female labor force participation
Linear response for spiking neuronal networks with unbounded memory
We establish a general linear response relation for spiking neuronal
networks, based on chains with unbounded memory. This relation allows us to
predict the influence of a weak amplitude time-dependent external stimuli on
spatio-temporal spike correlations, from the spontaneous statistics (without
stimulus) in a general context where the memory in spike dynamics can extend
arbitrarily far in the past. Using this approach, we show how linear response
is explicitly related to neuronal dynamics with an example, the gIF model,
introduced by M. Rudolph and A. Destexhe. This example illustrates the
collective effect of the stimuli, intrinsic neuronal dynamics, and network
connectivity on spike statistics. We illustrate our results with numerical
simulations.Comment: 60 pages, 8 figure
Mapping prior information onto LMI eigenvalue-regions for discrete-time subspace identification
In subspace identification, prior information can be used to constrain the
eigenvalues of the estimated state-space model by defining corresponding LMI
regions. In this paper, first we argue on what kind of practical information
can be extracted from historical data or step-response experiments to possibly
improve the dynamical properties of the corresponding model and, also, on how
to mitigate the effect of the uncertainty on such information. For instance,
prior knowledge regarding the overshoot, the period between damped oscillations
and settling time may be useful to constraint the possible locations of the
eigenvalues of the discrete-time model. Then, we show how to map the prior
information onto LMI regions and, when the obtaining regions are non-convex, to
obtain convex approximations.Comment: Under revie
On the Intrinsic Locality Properties of Web Reference Streams
There has been considerable work done in the study of Web reference streams: sequences of requests for Web objects. In particular, many studies have looked at the locality properties of such streams, because of the impact of locality on the design and performance of caching and prefetching systems. However, a general framework for understanding why reference streams exhibit given locality properties has not yet emerged.
In this work we take a first step in this direction, based on viewing the Web as a set of reference streams that are transformed by Web components (clients, servers, and intermediaries). We propose a graph-based framework for describing this collection of streams and components. We identify three basic stream transformations that occur at nodes of the graph: aggregation, disaggregation and filtering, and we show how these transformations can be used to abstract the effects of different Web components on their associated reference streams. This view allows a structured approach to the analysis of why reference streams show given properties at different points in the Web.
Applying this approach to the study of locality requires good metrics for locality. These metrics must meet three criteria: 1) they must accurately capture temporal locality; 2) they must be independent of trace artifacts such as trace length; and 3) they must not involve manual procedures or model-based assumptions. We describe two metrics meeting these criteria that each capture a different kind of temporal locality in reference streams. The popularity component of temporal locality is captured by entropy, while the correlation component is captured by interreference coefficient of variation. We argue that these metrics are more natural and more useful than previously proposed metrics for temporal locality.
We use this framework to analyze a diverse set of Web reference traces. We find that this framework can shed light on how and why locality properties vary across different locations in the Web topology. For example, we find that filtering and aggregation have opposing effects on the popularity component of the temporal locality, which helps to explain why multilevel caching can be effective in the Web. Furthermore, we find that all transformations tend to diminish the correlation component of temporal locality, which has implications for the utility of different cache replacement policies at different points in the Web.National Science Foundation (ANI-9986397, ANI-0095988); CNPq-Brazi
The Demographic Transition and the Sexual Division of Labor
This paper presents a theory where increases in female labor force participation and reductions in the gender wage-gap are generated as part of a single process of demographic transition, characterized by reductions in mortality and fertility. The paper suggests a link between changes in mortality and transformations in the role of women in society that has not been identified before in the literature. Mortality reductions affect the incentives of individuals to invest in human capital and to have children. Particularly, gains in adult longevity reduce fertility, increase investments in market human capital, increase female labor force participation, and reduce the wage differential between men and women. Child mortality reductions, though reducing fertility, do not generate this same pattern of changes. The model reconciles the increase in female labor market participation with the timing of age-specific mortality reductions observed during the demographic transition. It generates changes in fertility, labor market attachment, and the gender wage-gap as part of a single process of social transformation, triggered by reductions in mortality.
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