title: Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines.
abstract: Recent studies have shown that synaptic unreliability is a robust and sufficient mechanism for inducing the stochasticity observed in cortex. Here, we introduce Synaptic Sampling Machines (S2Ms), a class of neural network models that uses synaptic stochasticity as a means to Monte Carlo sampling and unsupervised learning. Similar to the original formulation of Boltzmann machines, these models can be viewed as a stochastic counterpart of Hopfield networks, but where stochasticity is induced by a random mask over the connections. Synaptic stochasticity plays the dual role of an efficient mechanism for sampling, and a regularizer during learning akin to DropConnect. A local synaptic plasticity rule implementing an event-driven form of contrastive divergence enables the learning of generative models in an on-line fashion. S2Ms perform equally well using discrete-timed artificial units (as in Hopfield networks) or continuous-timed leaky integrate and fire neurons. The learned representations are remarkably sparse and robust to reductions in bit precision and synapse pruning: removal of more than 75% of the weakest connections followed by cursory re-learning causes a negligible performance loss on benchmark classification tasks. The spiking neuron-based S2Ms outperform existing spike-based unsupervised learners, while potentially offering substantial advantages in terms of power and complexity, and are thus promising models for on-line learning in brain-inspired hardware.
authors.affiliation: Department of Cognitive Sciences, University of California, Irvine Irvine, CA, USA.
authors.forename: Emre O
authors.initials: EO
authors.lastname: Neftci
authors.affiliation: Department of Bioengineering, University of California San Diego, La Jolla, CA, USA.
authors.forename: Bruno U
authors.initials: BU
authors.lastname: Pedroni
authors.affiliation: Electrical and Computer Engineering Department, University of California San Diego, La Jolla, CA, USA.
authors.forename: Siddharth
authors.initials: S
authors.lastname: Joshi
authors.affiliation: Machine Learning Department, Carnegie Mellon University Pittsburgh, PA, USA.
authors.forename: Maruan
authors.initials: M
authors.lastname: Al-Shedivat
authors.affiliation: Department of Bioengineering, University of California San Diego, La Jolla, CA, USA.
authors.forename: Gert
authors.initials: G
authors.lastname: Cauwenberghs
keywords: Hopfield networks; regularization; spiking neural networks; stochastic processes; synaptic plasticty; synaptic transmission; unsupervised learning