Asymptotic description of stochastic neural networks. I - existence of a Large Deviation Principle

We study the asymptotic law of a network of interacting neurons when the number of neurons becomes infinite. The dynamics of the neurons is described by a set of stochastic differential equations in discrete time. The neurons interact through the synaptic weights which are Gaussian correlated random variables. We describe the asymptotic law of the network when the number of neurons goes to infinity. Unlike previous works which made the biologically unrealistic assumption that the weights were i.i.d. random variables, we assume that they are correlated. We introduce the process-level empirical measure of the trajectories of the solutions to the equations of the finite network of neurons and the averaged law (with respect to the synaptic weights) of the trajectories of the solutions to the equations of the network of neurons. The result is that the image law through the empirical measure satisfies a large deviation principle with a good rate function. We provide an analytical expression of this rate function in terms of the spectral representation of certain Gaussian processes

Asymptotic description of stochastic neural networks. II - Characterization of the limit law

We continue the development, started in of the asymptotic description of certain stochastic neural networks. We use the Large Deviation Principle (LDP) and the good rate function H announced there to prove that H has a unique minimum mu_e, a stationary measure on the set of trajectories. We characterize this measure by its two marginals, at time 0, and from time 1 to T. The second marginal is a stationary Gaussian measure. With an eye on applications, we show that its mean and covariance operator can be inductively computed. Finally we use the LDP to establish various convergence results, averaged and quenched

Stochastic neural field equations: A rigorous footing

We extend the theory of neural fields which has been developed in a deterministic framework by considering the influence spatio-temporal noise. The outstanding problem that we here address is the development of a theory that gives rigorous meaning to stochastic neural field equations, and conditions ensuring that they are well-posed. Previous investigations in the field of computational and mathematical neuroscience have been numerical for the most part. Such questions have been considered for a long time in the theory of stochastic partial differential equations, where at least two different approaches have been developed, each having its advantages and disadvantages. It turns out that both approaches have also been used in computational and mathematical neuroscience, but with much less emphasis on the underlying theory. We present a review of two existing theories and show how they can be used to put the theory of stochastic neural fields on a rigorous footing. We also provide general conditions on the parameters of the stochastic neural field equations under which we guarantee that these equations are well-posed. In so doing we relate each approach to previous work in computational and mathematical neuroscience. We hope this will provide a reference that will pave the way for future studies (both theoretical and applied) of these equations, where basic questions of existence and uniqueness will no longer be a cause for concern

The meanfield limit of a network of Hopfield neurons with correlated synaptic weights

We study the asymptotic behaviour for asymmetric neuronal dynamics in a network of Hopfield neurons. The randomness in the network is modelled by random couplings which are centered Gaussian correlated random variables. We prove that the annealed law of the empirical measure satisfies a large deviation principle without any condition on time. We prove that the good rate function of this large deviation principle achieves its minimum value at a unique Gaussian measure which is not Markovian. This implies almost sure convergence of the empirical measure under the quenched law. We prove that the limit equations are expressed as an infinite countable set of linear non Markovian SDEs.Comment: 102 page

A limitation of the hydrostatic reconstruction technique for Shallow Water equations

Because of their capability to preserve steady-states, well-balanced schemes for Shallow Water equations are becoming popular. Among them, the hydrostatic reconstruction proposed in Audusse et al. (2004), coupled with a positive numerical flux, allows to verify important mathematical and physical properties like the positivity of the water height and, thus, to avoid unstabilities when dealing with dry zones. In this note, we prove that this method exhibits an abnormal behavior for some combinations of slope, mesh size and water height.Comment: 7 page

The Federal Reserve's Term Auction Facility

As liquidity conditions in the term funding markets grew increasingly strained in late 2007, the Federal Reserve began making funds available directly to banks through a new tool, the Term Auction Facility (TAF). The TAF provides term funding on a collateralized basis, at interest rates and amounts set by auction. The facility is designed to improve liquidity by making it easier for sound institutions to borrow when the markets are not operating efficiently.Federal Reserve System ; Bank liquidity ; Banks and banking

The Unemployment Trap Meets the Age-Earning Profile.

The relative costs of taking employment or receiving welfare are usually understood through comparisons of a person’s social security entitlements and their wage alternative, known as replacement rates. In some situations it appears that the additional income from working is negligible, and this is said to constitute an “unemployment trap”. However, conventional replacement rates ignore the fact that age-earnings profiles slope upward through the acquisition of labour market experience. We offer a dynamic reinterpretation and compare alternative calculations for Australia in 2000. The usual and incorrect approach exaggerates significantly the likelihood of unemployment traps, but the presence of children mitigates considerably, and can even reverse, this assessment.unemployment traps, social security, age-earnings profiles, wages