Spiking Neural Algorithms for Markov Process Random Walk

2018/05/01 by William Severa, Severa, William, Rich Lehoucq +5 · 2 citations
Computer Science · #Emerging Technologies (cs.ET) #FOS: Computer and information sciences #Neural and Evolutionary Computing (cs.NE) #cs.ET #cs.NE

paper · pdf · doi:10.48550/arxiv.1805.00509

arxiv created 2018/05/01 · arxiv updated 2018/05/03

Abstract

The random walk is a fundamental stochastic process that underlies many numerical tasks in scientific computing applications. We consider here two neural algorithms that can be used to efficiently implement random walks on spiking neuromorphic hardware. The first method tracks the positions of individual walkers independently by using a modular code inspired by the grid cell spatial representation in the brain. The second method tracks the densities of random walkers at each spatial location directly. We analyze the scaling complexity of each of these methods and illustrate their ability to model random walkers under different probabilistic conditions.

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