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Poster C167 in Poster Session C - Friday, August 9, 2024, 11:15 am – 1:15 pm, Johnson Ice Rink
Discrete boundaries between neural populations in recurrent neural networks
Jacob Tanner1, Sina Mansour L.2, Ludovico Coletta3, Alessandro Gozzi4, Richard Betzel1; 1Indiana University, 2National University of Singapore, 3Fondazione Bruno Kessler, 4Istituto Italiano di Tecnologia
Recent theoretical and experimental work in neuroscience has focused on the representational and dynamical character of neural manifolds [e.g. (Ebitz & Hayden, 2021; Saxena & Cunningham, 2019; Mante, Sussillo, Shenoy, & Newsome, 2013; Cunningham & Yu, 2014)]. These neural manifolds are subspaces in neural activity space wherein many neurons coactivate. Importantly, neural populations studied under this “neural manifold hypothesis” are not cleanly divided into separate neural populations. Instead, many neurons contribute to most manifolds in some way or another. Here, we leveraged RNNs as a model system to study the character of discrete neural populations. We used a community detection method from network science to produce a partition that separates neurons into distinct populations. These partitions allowed us to ask the following question: do these discrete boundaries between neural populations matter to the system? We found evidence that these boundaries do matter to the system. First, we found that these boundaries neatly divide the representational content and role of neurons. Next, we found that these boundaries can be directly inferred from features of the weight matrix and we corroborated this result with structural and functional imaging data from mice and humans. Finally, we found that the dynamics of these RNNs respected the boundaries of neurons into distinct populations.
Keywords: Neural manifolds Neural populations Recurrent neural networks Dynamical systems