Recent advances in experimental neuroscience now make it possible to map complete wiring diagrams, or connectomes, of neural circuits in model organisms such as the fruit fly (Drosophila melanogaster). These datasets provide an unprecedented opportunity to understand how the brain’s structure gives rise to its function. Yet, knowing the connectivity alone does not reveal how groups of neurons process information and generate behaviour. Achieving this requires models that combine the anatomical and biophysical detail of mechanistic neuroscience with the adaptability and optimisation strengths of deep learning methods from artificial intelligence.
Currently, the two modelling traditions are largely separate. Mechanistic models faithfully incorporate anatomy and biophysics, but methods for optimising them to perform complex computations are lacking. Deep learning models can be trained to perform such computations efficiently, but they often lack mechanistic interpretability and biological plausibility. The DeepCoMechTome project addresses this gap by developing a unified machine learning framework that integrates the strengths of both approaches.
Our aim is to algorithmically identify “deep mechanistic models” that respect anatomical and biophysical constraints, reproduce experimental neural data, and perform computations relevant for behaviour. This work sits at the interface of neuroscience and AI: by constraining deep learning models with biological data, we can uncover how biological intelligence achieves highly robust and energy-efficient computation—capabilities that remain out of reach for most artificial systems.
We apply this framework to neural circuits that process visual information and use it to guide behaviour, for example in visually driven locomotion. Using detailed connectome and physiological data, we build models that can be optimised for these tasks, analysed to reveal underlying computational principles, and used to make testable predictions about neural tuning and behavioural output. This creates a virtuous cycle in which models guide experiments and experimental results refine models.
Although our initial focus is on the fruit fly, the methodology is general and can be applied to a wide range of neural circuits and species. By linking structure, dynamics, and behaviour, DeepCoMechTome will provide new insights into the fundamental principles of biological intelligence and inform the design of artificial systems that share the brain’s robustness and energy efficiency.
