Hannes Rapp, Martin Paul Nawrot. A spiking neural program for sensorimotor control during foraging in flying insects. Proceedings of the National Academy of Sciences Nov 2020, 117 (45) 28412-28421; DOI: 10.1073/pnas.2009821117

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.”

Abstract

“Foraging is a vital behavioral task for living organisms. Behavioral strategies and abstract mathematical models thereof have been described in detail for various species. To explore the link between underlying neural circuits and computational principles, we present how a biologically detailed neural circuit model of the insect mushroom body implements sensory processing, learning, and motor control. We focus on cast and surge strategies employed by flying insects when foraging within turbulent odor plumes. Using a spike-based plasticity rule, the model rapidly learns to associate individual olfactory sensory cues paired with food in a classical conditioning paradigm. We show that, without retraining, the system dynamically recalls memories to detect relevant cues in complex sensory scenes. Accumulation of this sensory evidence on short time scales generates cast-and-surge motor commands. Our generic systems approach predicts that population sparseness facilitates learning, while temporal sparseness is required for dynamic memory recall and precise behavioral control. Our work successfully combines biological computational principles with spike-based machine learning. It shows how knowledge transfer from static to arbitrary complex dynamic conditions can be achieved by foraging insects and may serve as inspiration for agent-based machine learning.”

Hannes Rapp, Martin Paul Nawrot. A spiking neural program for sensorimotor control during foraging in flying insects. Proceedings of the National Academy of Sciences Nov 2020, 117 (45) 28412-28421; DOI: 10.1073/pnas.2009821117