2002/03/27 by Patrick N. McGraw, McGraw, Patrick N., Michael Menzinger +1
Computer Science · Neuroscience · Physics and Astronomy · #Disordered Systems and Neural Networks (cond-mat.dis-nn) #FOS: Physical sciences #Neural Networks and Applications #Neural dynamics and brain function #cond-mat.dis-nn #stochastic dynamics and bifurcation
paper · pdf · doi:10.48550/arxiv.cond-mat/0203568
RevTeX, 18 pages including 12 figures. Submitted to Phys. Rev. E
arxiv created 2002/03/27 · openalex publication_date 2002/03/27 · arxiv updated 2009/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We examine the large-network, low-loading behaviour of an attractor neural network, the so-called bistable gradient network (BGN). We use analytical and numerical methods to characterize the attractor states of the network and their basins of attraction. The energy landscape is more complex than that of the Hopfield network and depends on the strength of the coupling among units. At weak coupling, the BGN acts as a highly selective associative memory; the input must be close to the one of the stored patterns in order to be recognized. A category of spurious attractors occurs which is not present in the Hopfield network. Stronger coupling results in a transition to a more Hopfield-like regime with large basins of attraction. The basins of attraction for spurious attractors are noticeably suppressed compared to the Hopfield case, even though the Hebbian synaptic structure is the same and there is no stochastic noise.