ArticleProceedings of the National Academy of Sciences of the United States of America2020
A spiking neural program for sensorimotor control during foraging in flying insects.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed.
- A voltage-dependent switch underlies efficient yet specific learning and memory.Current biology : CB · 2026Article
- dCA1 cells encode and update contextual place preference.Frontiers in systems neuroscience · 2026Article
- Prediction error drives associative learning and conditioned behavior in a spiking model of Drosophila larva.iScience · 2024Article
- Investigating visual navigation using spiking neural network models of the insect mushroom bodies.Frontiers in physiology · 2024Article
- Reinforcement learning as a robotics-inspired framework for insect navigation: from spatial representations to neural implementation.Frontiers in computational neuroscience · 2024Article
- Olfactory receptor neurons generate multiple response motifs, increasing coding space dimensionality.eLife · 2023Article
- Emergent behaviour and neural dynamics in artificial agents tracking odour plumes.Nature machine intelligence · 2023Article
- Efficient parameter calibration and real-time simulation of large-scale spiking neural networks with GeNN and NEST.Frontiers in neuroinformatics · 2023Article
- Minimal circuit motifs for second-order conditioning in the insect mushroom body.Frontiers in physiology · 2023Article
- Memory-inspired spiking hyperdimensional network for robust online learning.Scientific reports · 2022Article
- Olfactory Sensing and Navigation in Turbulent Environments.Annual review of condensed matter physics · 2022Article
- Sensing complementary temporal features of odor signals enhances navigation of diverse turbulent plumes.eLife · 2022Article
- Insect-Inspired Robots: Bridging Biological and Artificial Systems.Sensors (Basel, Switzerland) · 2021Review
- Plume Dynamics Structure the Spatiotemporal Activity of Mitral/Tufted Cell Networks in the Mouse Olfactory Bulb.Frontiers in cellular neuroscience · 2021Article
- Adaptive temporal processing of odor stimuli.Cell and tissue research · 2021Review
- A spiking neural program for sensorimotor control during foraging in flying insects.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
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2 authors.
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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.
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