How are insects able to perform complex probabilistic tasks by leveraging only small small neural networks, whereas machine-learning tasks often require large-scale architectures and extensive training on massive datasets? Evolution is able to shape decision-making in small neural circuits while maintaining high performance. By joining physical modelling, supervised and unsupervised structured inferences, Bayesian induction and numerical simulations, we can probe how evolution programmed robust decision making in the “brain“ of insects. In turn, we can extract key neural circuits from these insects and test their performance in real-world tasks.





