Attractor networks, which map an input space to a discrete output space, are useful for pattern completion--cleaning up noisy or missing input features. However, designing a net to have a given set of attractors is notoriously tricky; training procedures are CPU intensive and often produce spurious attractors and ill-conditioned attractor basins. These difficulties occur because each connection...

A Potts associative memory network has been proposed as a simplified model of macroscopic cortical dynamics, in which each Potts unit stands for a patch of cortex, which can be activated in one of S local attractor states. The internal neuronal dynamics of the patch is not described by the model, rather it is subsumed into an effective description in terms of graded Potts units, with adaptation...

The results of numerical experiments on the structure of chaotic attrac-tors in the Khalatnikov-Kroyter model of two freedoms are presented. This model was developed for a qualitative description of the wave turbulence of the second sound in helium. The attractor dimension, size, and the maximal Lyapunov exponent in dependence on the single dimensionless parameter F of the model are found and d...

the most popular animal models of alzheimer’s disease (ad) are transgenic mice expressing human genes with known mutations which do not represent the most abundant sporadic form of the disease. an increasing number of genetic, vascular and psychosocial data strongly support that the octodon degus, a moderate-sized and diurnal precocial rodent, provides a naturalistic model for the study of the ...

We study the connection between the long-time dynamics of the 3D magnetohydrodynamicα model and the exact 3D magnetohydrodynamic system. We prove that the trajectory attractor Uα of the 3D magnetohydrodynamic-α model converges to the trajectory attractor U0 of the 3D magnetohydrodynamic system (in an appropriate topology) when α approaches zero.

Stability issues in Freeman's KIII model of the olfactory system are addressed. A possible mechanism for switching between various parts of the KIII attractor in response to external stimuli is outlined. It is shown that, in harmony with Haken's slaving principle, any particular system state (memory slot) can be described as a mixture of trajectories corresponding to a number of di erent attrac...

The attractor neural networks, originally developed by Hinton & Shallice (1991), can be applicable to variety of neuropsychological data. It can account for delays of reaction times of the brain damaged patients. When the network suffers damages, the iteration numbers between the output and the cleanup layers would increase. In addition, it can also account for performances of the categorizatio...

Recent evidence suggests that neurons in primary sensory cortex arrange into competitive groups, representing stimuli by their joint activity rather than as independent feature analysers. A possible explanation for these results is that sensory cortex implements attractor dynamics, although this proposal remains controversial. Here we report that fast attractor dynamics emerge naturally in a co...

This paper presents an inverse model using chaotic behaviour. The chaos time series inverse model, which uses coupling methods between an inverse model and chaos theory can reconstruct a deterministic and low-dimensional phase space by transforming irregular behaviours of nonlinear time-varying systems into a strange attractor (e.g., a Rossler attractor or a Lorenz attractor), and it can then p...