Departmental Papers (BE)

Document Type

Journal Article

Date of this Version



We introduce a novel type of neural network, termed the parallelHopfield network, that can simultaneously effect the dynamics of many different, independent Hopfield networks in parallel in the same piece of neural hardware. Numerically we find that under certain conditions, each Hopfield subnetwork has a finite memory capacity approaching that of the equivalent isolated attractor network, while a simple signal-to-noise analysis sheds qualitative, and some quantitative, insight into the workings (and failures) of the system.


Suggested Citation:
Wilson, R.C. (2009). "Parallel Hopfield Networks." Neural Computation. Vol. 21, 831-850.

© 2008 Massachusetts Institute of Technology



Date Posted: 09 November 2010

This document has been peer reviewed.