Rapid, parallel path planning by propagating wavefronts of spiking neural activity

TL;DR

This paper proposes a neural model where propagating wavefronts of spiking activity enable rapid, parallel path planning in the brain, demonstrating efficient navigation and synaptic adaptation for multiple environments.

Contribution

It introduces a novel neural mechanism using wavefront propagation and synaptic plasticity for fast, parallel path planning, applicable to biological and neuromorphic systems.

Findings

The model finds optimal paths in simulated environments.

It learns and navigates in multiple environments.

The approach operates with local synaptic rules during a single wavefront passage.

Abstract

Efficient path planning and navigation is critical for animals, robotics, logistics and transportation. We study a model in which spatial navigation problems can rapidly be solved in the brain by parallel mental exploration of alternative routes using propagating waves of neural activity. A wave of spiking activity propagates through a hippocampus-like network, altering the synaptic connectivity. The resulting vector field of synaptic change then guides a simulated animal to the appropriate selected target locations. We demonstrate that the navigation problem can be solved using realistic, local synaptic plasticity rules during a single passage of a wavefront. Our model can find optimal solutions for competing possible targets or learn and navigate in multiple environments. The model provides a hypothesis on the possible computational mechanisms for optimal path planning in the brain, at the same time it is useful for neuromorphic implementations, where the parallelism of information processing proposed here can fully be harnessed in hardware.