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Poster B4 in Poster Session B - Thursday, August 8, 2024, 1:30 – 3:30 pm, Johnson Ice Rink
Estimating flexible across-area communication with neurally-constrained RNN
Joao Barbosa1 (), Adrian Valente1, Scott Brincat2, Earl Miller2, Srdjan Ostojic1; 1Group for Neural Theory, Ecole Normale Superieure, Paris, France, 2The Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
Neural computations supporting complex behaviors involve multiple brain regions, and large-scale recordings from animals engaged in complex tasks are increasingly common. A current challenge in analysing these data is to identify which part of the information contained within a brain region is shared with others. Here, to address this limitation, we trained multi-region recurrent neural networks (RNN) models to reproduce the dynamics of large-scale single-unit recordings (more than 6000 neurons across 7 cortical areas) from monkeys engaged in a two-dimensional (color and motion direction) context-dependent decision-making task. Decoding analyses show that all areas encode both stimuli (color and direction). However, using our approach we uncovered feed-forward and feedback interactions within a network of 7 interacting regions. Constraining interactions during training or testing recovered the canonical brain hierarchy that differentiate sensory and frontal regions. Inspecting across-region interactions, we also found that frontal regions compress the irrelevant stimulus in a context-dependent manner, while sensory regions always compress the same stimulus.
Keywords: neural networks context-dependent decision-making RNN large-scale recordings