4 papers
From Local Learning to Global Prediction Through Layered Surprise Cascades
Andrew L. Smith, Linxing Preston Jiang, Jason K. Eshraghian +2
Hierarchical predictive coding proposes a compelling hypothesis of brain computation, suggesting that the cortex builds layered predictions to minimize surprise. Yet most models re…
A simple connection from loss flatness to compressed neural representations
Shirui Chen, Stefano Recanatesi, Eric Shea-Brown
Despite extensive study, the significance of sharpness -- the trace of the loss Hessian at local minima -- remains unclear. We investigate an alternative perspective: how sharpness…
Identifying the impact of local connectivity patterns on dynamics in excitatory-inhibitory networks
Yuxiu Shao, David Dahmen, Stefano Recanatesi +2
Networks of excitatory and inhibitory (EI) neurons form a canonical circuit in the brain. Seminal theoretical results on dynamics of such networks are based on the assumption that…
Learning to Embed Distributions via Maximum Kernel Entropy
Oleksii Kachaiev, Stefano Recanatesi
Empirical data can often be considered as samples from a set of probability distributions. Kernel methods have emerged as a natural approach for learning to classify these distribu…