Spiking Neural Algorithms for Markov Process Random Walk
This work addresses the challenge of efficient random walk simulation for scientific computing applications on neuromorphic hardware, presenting incremental algorithmic improvements.
The paper tackles the problem of implementing random walks on spiking neuromorphic hardware by proposing two neural algorithms: one tracking individual walkers using a grid cell-inspired code and another tracking densities directly, analyzing their scaling complexity and ability to model walkers under different probabilistic conditions.
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.