RADICaL is an extension of AutoLFADS for application to 2-photon (2p) calcium imaging data. AutoLFADS is the combination of Latent Factor Analysis via Dynamical Systems (LFADS), a deep learning method to infer neural population dynamics, with Population-Based Training (PBT), an automatic hyperparameter tuning framework as described in this paper. RADICaL incorporates two major innovations over AutoLFADS (see paper link). First, RADICaL’s observation model was modified to better account for the statistics of deconvolved calcium events. Second, RADICaL integrated a novel neural network training strategy, selective backpropagation through time (SBTT), that exploits the staggered timing of 2p sampling of neuronal populations to recover network dynamics with high temporal precision.
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