The Discrete Langevin Machine: Bridging the Gap Between Thermodynamic and Neuromorphic Systems
arXiv:1901.05214 · doi:10.1103/PhysRevE.101.063304
Abstract
A formulation of Langevin dynamics for discrete systems is derived as a class of generic stochastic processes. The dynamics simplify for a two-state system and suggest a network architecture which is implemented by the Langevin machine. The Langevin machine represents a promising approach to compute successfully quantitative exact results of Boltzmann distributed systems by LIF neurons. Besides a detailed introduction of the dynamics, different simplified models of a neuromorphic hardware system are studied with respect to a control of emerging sources of errors.
25 pages, 16 figures
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