A spiking neural program for sensorimotor control during foraging in flying insects

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Abstract
Significance: Living organisms demonstrate remarkable abilities in mastering problems imposed by complex and dynamic environments, and they can generalize their experience in order to rapidly adapt behavior. This paper demonstrates the benefits of using biological spiking neural networks, sparse computations, and local learning rules. It highlights the functional roles of temporal- and population-sparse coding for rapid associative learning, precise memory recall, and transformation into navigational output. We show how memory formation generalizes to perform precise memory recall under dynamic, nonstationary conditions, giving rise to nontrivial foraging behavior in a complex natural environment. Results suggest how principles of biological computation could benefit agent-based machine learning to deal with nonstationary scenarios.
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Funding Information
  • Deutsche Forschungsgemeinschaft (403329959)

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