Learning by message-passing in networks of discrete synapses
<|reference_start|>Learning by message-passing in networks of discrete synapses: 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.<|reference_end|>
@article{braunstein2005learning,
    title={Learning by message-passing in networks of discrete synapses},
    author={Alfredo Braunstein and Riccardo Zecchina},
    journal={Phys. Rev. Lett. 96, 030201 (2006)},
    year={2005},
    doi={10.1103/PhysRevLett.96.030201},
    archivePrefix={arXiv},
    eprint={cond-mat/0511159},
    primaryClass={cond-mat.dis-nn cs.LG q-bio.NC}
}