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Condensed Matter > Disordered Systems and Neural Networks

arXiv:cond-mat/0511159 (cond-mat)
[Submitted on 7 Nov 2005 (v1), last revised 9 Dec 2005 (this version, v2)]

Title:Learning by message-passing in networks of discrete synapses

Authors:Alfredo Braunstein, Riccardo Zecchina
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Abstract: We show that a message-passing process allows to store in binary "material" synapses a number of random patterns which almost saturates the information theoretic bounds. We apply the learning algorithm to networks characterized by a wide range of different connection topologies and of size comparable with that of biological systems (e.g. $n\simeq10^{5}-10^{6}$). The algorithm can be turned into an on-line --fault tolerant-- learning protocol of potential interest in modeling aspects of synaptic plasticity and in building neuromorphic devices.
Comments: 4 pages, 3 figures; references updated and minor corrections; accepted in PRL
Subjects: Disordered Systems and Neural Networks (cond-mat.dis-nn); Machine Learning (cs.LG); Neurons and Cognition (q-bio.NC)
Cite as: arXiv:cond-mat/0511159 [cond-mat.dis-nn]
  (or arXiv:cond-mat/0511159v2 [cond-mat.dis-nn] for this version)
  https://doi.org/10.48550/arXiv.cond-mat/0511159
Journal reference: Phys. Rev. Lett. 96, 030201 (2006)
Related DOI: https://doi.org/10.1103/PhysRevLett.96.030201

Submission history

From: Riccardo Zecchina [view email]
[v1] Mon, 7 Nov 2005 13:48:01 UTC (54 KB)
[v2] Fri, 9 Dec 2005 14:17:08 UTC (54 KB)
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