# Scale-Free Navigational Planning by Neuronal Traveling Waves

## TL;DR

This paper proposes a neuronal mechanism for spatial navigation using traveling waves that provide directional information and estimate waiting times for reaching a goal.

## Contribution

A novel noise-robust neuronal path-finding algorithm using phase differences in traveling waves for spatial navigation.

## Key findings

- Traveling waves from the goal provide directional information through local phase differences.
- Waiting time estimates help determine the minimal number of neurons needed for the cognitive map.
- The model offers functional interpretations of oscillatory waves and phase precession in navigation.

## Abstract

Spatial navigation and planning is assumed to involve a cognitive map for evaluating trajectories towards a goal. How such a map is realized in neuronal terms, however, remains elusive. Here we describe a simple and noise-robust neuronal implementation of a path finding algorithm in complex environments. We consider a neuronal map of the environment that supports a traveling wave spreading out from the goal location opposite to direction of the physical movement. At each position of the map, the smallest firing phase between adjacent neurons indicate the shortest direction towards the goal. In contrast to diffusion or single-wave-fronts, local phase differences build up in time at arbitrary distances from the goal, providing a minimal and robust directional information throughout the map. The time needed to reach the steady state represents an estimate of an agent’s waiting time before it heads off to the goal. Given typical waiting times we estimate the minimal number of neurons involved in the cognitive map. In the context of the planning model, forward and backward spread of neuronal activity, oscillatory waves, and phase precession get a functional interpretation, allowing for speculations about the biological counterpart.