41 citations · 43 across the 6 of their papers we have counts for
6 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…
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…
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…
Metastable attractors explain the variable timing of stable behavioral action sequences
Stefano Recanatesi, Ulises Pereira, Masayoshi Murakami +2
Natural animal behavior displays rich lexical and temporal dynamics, even in a stable environment. This implies that behavioral variability arises from sources within the brain, bu…
Dimensionality compression and expansion in Deep Neural Networks
Stefano Recanatesi, Matthew Farrell, Madhu Advani +3
Datasets such as images, text, or movies are embedded in high-dimensional spaces. However, in important cases such as images of objects, the statistical structure in the data const…