computational model

Editor’s Choice Paper 2023. Hardware prototype of the model using a ring of UV-sensitive photodiode pairs, and test it under cloudy and occluded skies.

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In this talk I present how our anatomically accurate incentive circuit predicts the responses of mushroom body neurons from the fruit fly brain and how they can be used to create similar behaviour to the one observed in the animals.

We suggest a mechanism that integrates the allocentric velocity of the animal (estimated by the fan-shaped body) and the scene familiarity (estimated by the mushroom body) to a target velocity that drives the behaviour of the animal.

We test the performance of the incentive circuit in the visual place recognition task and suggest that the familiarity must increase along a familiar route. Additionally, PCA whitenning and combinatorial encoding techniques enhanced the separability of visual scenes. The familiarity could be used as input to the central complex for visual homing.

The goal of the project is to develop nanophotonic on-chip devices for integrated sensing and neural computation, inspired by the insect brain.

We present how our novel dopaminergic learning rule and the incentive circuit predict the responses of mushroom body neurons from the fruit fly brain and create similar behaviour to the one observed in the animals.

Modelling differential roles for identified dopaminergic and output neurons of the fruit-fly mushroom bodies combined with a novel dopaminergic plasticity rule explains neural and behavioural phenomena in olfactory learning tasks.

In this poster we show how our anatomically accurate incentive circuit predicts the responses of mushroom body neurons from the fruit fly brain and how they can be used to create similar behaviour to the one observed in the animals.

In this talk we present our recent work building an anatomically accurate neural circuit that allow memory dynamics in the fruit-fly brain.