Abstract
It has been evidenced that vision-based decision-making in Drosophila consists of both simple perceptual (linear) decision and value-based (non-linear) decision. This paper proposes a general computational spiking neural network (SNN) model to explore how different brain areas are connected contributing to Drosophila linear and nonlinear decision-making behavior. First, our SNN model could successfully describe all the experimental findings in fly visual reinforcement learning and action selection among multiple conflicting choices as well. Second, our computational modeling shows that dopaminergic neuron-GABAergic neuron-mushroom body (DA-GABA-MB) works in a recurrent loop providing a key circuit for gain and gating mechanism of nonlinear decision making. Compared with existing models, our model shows more biologically plausible on the network design and working mechanism, and could amplify the small differences between two conflicting cues more clearly. Finally, based on the proposed model, the UAV could quickly learn to make clear-cut decisions among multiple visual choices and flexible reversal learning resembling to real fly. Compared with linear and uniform decision-making methods, the DA-GABA-MB mechanism helps UAV complete the decision-making task with fewer steps.
Highlights
IntroductionIt has been evidenced that vision-based decision-making in Drosophila consists of both simple perceptual (linear) decision and value-based (non-linear) decision
It has been evidenced that vision-based decision-making in Drosophila consists of both simple perceptual decision and value-based decision
We verified our model on Drosophila visual cues learning experiments, the results showed that with the brain-inspired structures and mechanisms, the DrosDecMa model produced similar outputs compared to Drosophila behavior experiments in3,4
Summary
It has been evidenced that vision-based decision-making in Drosophila consists of both simple perceptual (linear) decision and value-based (non-linear) decision. This paper proposes a general computational spiking neural network (SNN) model to explore how different brain areas are connected contributing to Drosophila linear and nonlinear decision-making behavior. Drosophila could learn the association between visual input and punishment, and fly towards the safe pattern (upright-green T) even if the heat punishment is shut down This is the simple perceptual decision-making task. Based on the decision-making neural circuit and learning process in7, Wei et al.[8] further added conflicting cues learning during the choice phase. The difference between linear and nonlinear decision-making is based on the corresponding input color-shape selection curve, which shows little biological basis of the neural circuit. We aim to design a dynamic decision-making SNN model, named as DrosDecMa, which is mostly based on the linear and nonlinear mechanisms of the Drosophila Decision-Making circuits. The models either reproduced several observations found in neuroscience experimental studies or inspired a more intelligent decision-making algorithm
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