4,099 research outputs found
Existence and Stability of Standing Pulses in Neural Networks: II Stability
We analyze the stability of standing pulse solutions of a neural network
integro-differential equation. The network consists of a coarse-grained layer
of neurons synaptically connected by lateral inhibition with a non-saturating
nonlinear gain function. When two standing single-pulse solutions coexist, the
small pulse is unstable, and the large pulse is stable. The large single-pulse
is bistable with the ``all-off'' state. This bistable localized activity may
have strong implications for the mechanism underlying working memory. We show
that dimple pulses have similar stability properties to large pulses but double
pulses are unstable.Comment: 31 pages, 16 figures, submitted to SIAM Journal on Applied Dynamical
System
Existence and Stability of Standing Pulses in Neural Networks : I Existence
We consider the existence of standing pulse solutions of a neural network
integro-differential equation. These pulses are bistable with the zero state
and may be an analogue for short term memory in the brain. The network consists
of a single-layer of neurons synaptically connected by lateral inhibition. Our
work extends the classic Amari result by considering a non-saturating gain
function. We consider a specific connectivity function where the existence
conditions for single-pulses can be reduced to the solution of an algebraic
system. In addition to the two localized pulse solutions found by Amari, we
find that three or more pulses can coexist. We also show the existence of
nonconvex ``dimpled'' pulses and double pulses. We map out the pulse shapes and
maximum firing rates for different connection weights and gain functions.Comment: 31 pages, 29 figures, submitted to SIAM Journal on Applied Dynamical
System
A machine learning framework for data driven acceleration of computations of differential equations
We propose a machine learning framework to accelerate numerical computations
of time-dependent ODEs and PDEs. Our method is based on recasting
(generalizations of) existing numerical methods as artificial neural networks,
with a set of trainable parameters. These parameters are determined in an
offline training process by (approximately) minimizing suitable (possibly
non-convex) loss functions by (stochastic) gradient descent methods. The
proposed algorithm is designed to be always consistent with the underlying
differential equation. Numerical experiments involving both linear and
non-linear ODE and PDE model problems demonstrate a significant gain in
computational efficiency over standard numerical methods
Local/global analysis of the stationary solutions of some neural field equations
Neural or cortical fields are continuous assemblies of mesoscopic models,
also called neural masses, of neural populations that are fundamental in the
modeling of macroscopic parts of the brain. Neural fields are described by
nonlinear integro-differential equations. The solutions of these equations
represent the state of activity of these populations when submitted to inputs
from neighbouring brain areas. Understanding the properties of these solutions
is essential in advancing our understanding of the brain. In this paper we
study the dependency of the stationary solutions of the neural fields equations
with respect to the stiffness of the nonlinearity and the contrast of the
external inputs. This is done by using degree theory and bifurcation theory in
the context of functional, in particular infinite dimensional, spaces. The
joint use of these two theories allows us to make new detailed predictions
about the global and local behaviours of the solutions. We also provide a
generic finite dimensional approximation of these equations which allows us to
study in great details two models. The first model is a neural mass model of a
cortical hypercolumn of orientation sensitive neurons, the ring model. The
second model is a general neural field model where the spatial connectivity
isdescribed by heterogeneous Gaussian-like functions.Comment: 38 pages, 9 figure
Differentiable Genetic Programming
We introduce the use of high order automatic differentiation, implemented via
the algebra of truncated Taylor polynomials, in genetic programming. Using the
Cartesian Genetic Programming encoding we obtain a high-order Taylor
representation of the program output that is then used to back-propagate errors
during learning. The resulting machine learning framework is called
differentiable Cartesian Genetic Programming (dCGP). In the context of symbolic
regression, dCGP offers a new approach to the long unsolved problem of constant
representation in GP expressions. On several problems of increasing complexity
we find that dCGP is able to find the exact form of the symbolic expression as
well as the constants values. We also demonstrate the use of dCGP to solve a
large class of differential equations and to find prime integrals of dynamical
systems, presenting, in both cases, results that confirm the efficacy of our
approach
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