Response Functions Improving Performance in Analog Attractor Neural Networks

Details Response Functions Improving Performance in Analog Attractor Neural Networks Response Functions Improving Performance in Analog Attractor Neural Networks

1994-02-22

arxiv.org/abs/cond-mat/9402096v1

Physics

Condensed Matter

13 pages (with figures), LaTex (RevTex), to appear on Phys.Rev.E (RC)

Scientific paper

10.1103/PhysRevE.49.R1823

In the context of attractor neural networks, we study how the equilibrium analog neural activities, reached by the network dynamics during memory retrieval, may improve storage performance by reducing the interferences between the recalled pattern and the other stored ones. We determine a simple dynamics that stabilizes network states which are highly correlated with the retrieved pattern, for a number of stored memories that does not exceed $\alpha_{\star} N$, where $\alpha_{\star}\in[0,0.41]$ depends on the global activity level in the network and $N$ is the number of neurons.

Affiliated with

Also associated with

No associations

Rating

Response Functions Improving Performance in Analog Attractor Neural Networks does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Response Functions Improving Performance in Analog Attractor Neural Networks, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Response Functions Improving Performance in Analog Attractor Neural Networks will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-124084