A spiking neural program for sensory-motor control during foraging in flying insects

Rapp, Hannes; Nawrot, Martin Paul

doi:10.1101/2020.08.10.243881

Cited by 3 publications

References 69 publications

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Adaptive temporal processing of odor stimuli

2021

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The olfactory system translates chemical signals into neuronal signals that inform behavioral decisions of the animal. Odors are cues for source identity, but if monitored long enough, they can also be used to localize the source. Odor representations should therefore be robust to changing conditions and flexible in order to drive an appropriate behavior. In this review, we aim at discussing the main computations that allow robust and flexible encoding of odor information in the olfactory neural pathway.

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Adaptive temporal processing of odor stimuli

2021

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

Rapp

Nawrot

2020

Proc. Natl. Acad. Sci. U.S.A.

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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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A mechanistic model for reward prediction and extinction learning in the fruit fly

Springer

Nawrot

2020

Preprint

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Extinction learning, the ability to update previously learned information by integrating novel contradictory information, is a key mechanism for adapting our behavior and of high clinical relevance for therapeutic approaches to the modulation of maladaptive memories. Insect models have been instrumental in uncovering fundamental processes of memory formation and memory update. Recent experimental results in Drosophila melanogaster suggest that, after the behavioral extinction of a memory, two parallel but opposing memory traces coexist, residing at different sites within the mushroom body. Here we propose a minimalistic circuit model of the Drosophila mushroom body that supports classical appetitive and aversive conditioning and memory extinction. The model is tailored to the existing anatomical data and involves two circuit motives of central functional importance. It employs plastic synaptic connections between Kenyon cells and mushroom body output neurons (MBONs) in separate and mutually inhibiting appetitive and aversive learning pathways. Recurrent modulation of plasticity through projections from MBONs to reinforcement-mediating dopaminergic neurons implements a simple reward prediction mechanism. A distinct set of four MBONs encodes odor valence and predicts behavioral model output. Subjecting our model to learning and extinction protocols reproduced experimental results from recent behavioral and imaging studies. Simulating the experimental blocking of synaptic output of individual neurons or neuron groups in the model circuit confirmed experimental results and allowed formulation of testable predictions. In the temporal domain, our model achieves rapid learning with a step-like increase in the encoded odor value after a single pairing of the conditioned stimulus with a reward or punishment, facilitating single-trial learning.

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A spiking neural program for sensory-motor control during foraging in flying insects

Cited by 3 publications

References 69 publications

Adaptive temporal processing of odor stimuli

Adaptive temporal processing of odor stimuli

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

A mechanistic model for reward prediction and extinction learning in the fruit fly

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