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Efficient path planning and navigation is critical for animals, robotics,logistics and transportation. We study a model in which spatial navigationproblems can rapidly be solved in the brain by parallel mental exploration ofalternative routes using propagating waves of neural activity. A wave ofspiking activity propagates through a hippocampus-like network, altering thesynaptic connectivity. The resulting vector field of synaptic change thenguides a simulated animal to the appropriate selected target locations. Wedemonstrate that the navigation problem can be solved using realistic, localsynaptic plasticity rules during a single passage of a wavefront. Our model canfind optimal solutions for competing possible targets or learn and navigate inmultiple environments. The model provides a hypothesis on the possiblecomputational mechanisms for optimal path planning in the brain, at the sametime it is useful for neuromorphic implementations, where the parallelism ofinformation processing proposed here can fully be harnessed in hardware.

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