« The Human Connectome Project | Main | Manfred Warmuth »

The thermodynamic temperature of a rhythmic spiking network

Paul Merolla, Tristan Ursell, John Arthur published today on arXiv.org a beautiful and unconventional paper that applies concepts from statistical mechanics to spiking neurons:

ABSTRACT: Artificial neural networks built from two-state neurons are powerful computational substrates, whose computational ability is well understood by analogy with statistical mechanics. In this work, we introduce similar analogies in the context of spiking neurons in a fixed time window, where excitatory and inhibitory inputs drawn from a Poisson distribution play the role of temperature. For single neurons with a "bandgap" between their inputs and the spike threshold, this temperature allows for stochastic spiking. By imposing a global inhibitory rhythm over the fixed time windows, we connect neurons into a network that exhibits synchronous, clock-like updating akin to neural networks. We implement a single-layer Boltzmann machine without learning to demonstrate our model.

TrackBack

TrackBack URL for this entry:
http://p9.hostingprod.com/@modha.org/blog-mt/mt-tb.fcgi/163

Post a comment

(If you haven't left a comment here before, you may need to be approved by the site owner before your comment will appear. Until then, it won't appear on the entry. Thanks for waiting.)