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.
Highlights
One of the central problems for neurobiology is to understand the computational effectiveness of the brains of higher animals
Called, place cells—the hippocampal cells getting activated whenever an animal enters a certain spatial location— gave rise to the idea that the hippocampus contributes to the creation of internal, neural representations of the environment
We demonstrate that spatial navigation can rapidly be solved in the hippocampus-like neural network by parallel mental exploration of alternative routes
Summary
One of the central problems for neurobiology is to understand the computational effectiveness of the brains of higher animals. We show here that a best path can rapidly be found by parallel search in the same kind of network, but by a propagating wave of spiking activity. The process of path planning and navigation, as proposed in our model, consists of the following steps: (1) expanding waves of neural activity are initiated from the place cells corresponding to selected target location(s); (2) the propagating waves alter synaptic connectivity within the network through spike-timingdependent plasticity and create a directed synaptic vector field (SVF) converging on the goal locations; (3) this vector field is used by an animal to navigate toward targets; (4) whenever a new planning process is necessary, all synapses are reset to the baseline state and waves of activity can be initiated from the new target locations
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