Computational models play a key role in explaining how brain activity emerges from the underlying neural networks, and how this activity ultimately leads to thoughts and behaviors. By summarizing the essence of the computations performed collectively by complex networks of interconnected areas, such models can help in identifying the critical differences in the function of healthy and diseased brains.
Here we exploit recent advances in artificial intelligence to build more powerful and better interpretable models of large-scale, multi-area neural dynamics and computations. We have developed a novel machine learning approach for fitting artificial, recurrent neural networks to large-scale neural activity. We extensively validated this approach on synthetic, simulated neural activity, and applied it to explain brain-wide neural activity measured with calcium imaging in mice engaged in a decision-making task. Unlike past modeling approaches, the fitted networks capture not just the average activity over many task trials, but also the prominent variability apparent in individual trials.
The framework developed in this project will easily generalize to other tasks and recordings and could be applied in mice and in humans, potentially simplifying comparisons between species.
Principal investigators: Valerio Mante, Fritjof Helmchen
PhD student: Lucas Pompe