Topic: dynamical systems analysis

Real-world perception is thought to arise from the coordinated activity of neuronal populations reflecting a combination of sensory inputs, existing knowledge, and contextually modulated expectations. We hypothesize that this integration is realized in the temporal dynamics of perceptual population responses. Here, we use latent-space modeling and dynamical systems analysis to characterize activity across populations of simultaneously recorded neurons in a secondary auditory forebrain region of songbirds (European starlings), engaged in an expectation-cued song-syllable categorization task. We

One of the central goals of neuroscience is to gain a mechanistic understanding of how the dynamics of neural circuits give rise to their observed function. A popular approach towards this end is to train recurrent neural networks (RNNs) to reproduce experimental recordings of neural activity. These trained RNNs are then treated as surrogate models of biological neural circuits, whose properties can be dissected via dynamical systems analysis. How reliable are the mechanistic insights derived from this procedure? While recent advances in population-level recording technologies have allowed sim

Animals use knowledge of the environment to guide efficient foraging decisions. It is unclear how this knowledge is accumulated or maintained by recurrently connected circuits in the brain. In order to guide the study of how structures of knowledge are accumulated and maintained to support optimal foraging decisions, we reverse-engineered recurrent neural networks (RNNs) trained to perform dynamic foraging tasks. We highlight two variants of the task --- a highly structured block-switch variant and a weakly structured random walk variant --- and investigate how network dynamics are shaped by