ePoster · Computational Neuroscience
Dynamical representations between biologically plausible and implausible task-trained neural networks
Matthew Getz, Julijana Gjorgjieva · Bernstein Conference 2024
Recurrent neural networks (RNNs) are proving increasingly useful in systems neuroscience. Training RNNs on cognitive-like tasks has demonstrated their potential to infer dynamical processes and computations in the brain [1, 2]. They have additionally been shown to provide an interpretable link between connectivity structure and activity dynamics in low-rank networks [3]. However, a potentially serious shortcoming is that many (if not most) of these networks disobey biological constraints in terms of their connectivity structure and response properties, which raises questions as to their interp