Computing with dynamic attractors in neural networks
نویسندگان
چکیده
منابع مشابه
Computing with dynamic attractors in neural networks.
In this paper we report on some new architectures for neural computation, motivated in part by biological considerations. One of our goals is to demonstrate that it is just as easy for a neural net to compute with arbitrary attractors--oscillatory or chaotic--as with the more usual asymptotically stable fixed points. The advantages (if any) of such architectures are currently being investigated...
متن کاملAttractors in Neural Networks with Innnite Gain
We study a system of equations with discontinuous right hand side, which arise as models of gene and neural networks. Associated to the system is a graph of dynamics, which can be used to deene a Morse decomposition of the invariant set of the ow on the set of rays through the origin ((5]). We study attractors in R 4 which lie in a set of orthants in the form of gure eight. Trajectories can fol...
متن کاملStatic and Dynamic Attractors of Auto-associative Neural Networks
In this paper we study the problem of the occurrence of cycles in autoassociative neural networks. We call these cycles dynamic attractors, show when and why they occur and how they can be identi-ed. Of particular interest is the pseudo-inverse network with reduced self-connection. We prove that it has dynamic attractors, which occur with a probability proportional to the number of prototypes a...
متن کاملDynamics of Neural Networks with Continuous Attractors
We investigate the dynamics of continuous attractor neural networks (CANNs). Due to the translational invariance of their neuronal interactions, CANNs can hold a continuous family of stationary states. We systematically explore how their neutral stability facilitates the tracking performance of a CANN, which is believed to have wide applications in brain functions. We develop a perturbative app...
متن کاملAttractors in fully asymmetric neural networks
The statistical properties of the length of the cycles and of the weights of the attraction basins in fully asymmetric neural networks (i.e. with completely uncorrelated synapses) are computed in the framework of the annealed approximation which we previously introduced for the study of Kauffman networks. Our results show that this model behaves essentially as a Random Map possessing a reversal...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Biosystems
سال: 1995
ISSN: 0303-2647
DOI: 10.1016/0303-2647(94)01451-c