Learning by message passing in networks of discrete synapses.
We show that a message-passing process allows us to store in binary "material" synapses a number of random patterns which almost saturate 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., [EQUATION: SEE TEXT]). The algorithm can be turned into an online-fault tolerant-learning protocol of potential interest in modeling aspects of synaptic plasticity and in building neuromorphic devices.
ICTP, Strada Costiera 11, I-34100 Trieste, Italy.
Alfredo
A
Braunstein
Riccardo
R
Zecchina
D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D001683:Biological Clocks / Q000502:physiology; D032701:Biomimetics / Q000379:methods; D003198:Computer Simulation; D006801:Humans; D007858:Learning / Q000502:physiology*; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D013569:Synapses / Q000502:physiology*; D009435:Synaptic Transmission / Q000502:physiology*