Search Results for author:
Found 6 papers, 3 papers with code
Learning Efficient Coding of Natural Images with Maximum Manifold Capacity Representations
The resulting method is closely related to and inspired by advances in the field of self supervised learning (SSL), and we demonstrate that MMCRs are competitive with state of the art results on standard SSL benchmarks.
Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution
Neuroscience has long been an essential driver of progress in artificial intelligence (AI).
Impression learning: Online representation learning with synaptic plasticity
Understanding how the brain constructs statistical models of the sensory world remains a longstanding challenge for computational neuroscience.
Stochastic Solutions for Linear Inverse Problems using the Prior Implicit in a Denoiser
Two recent lines of work – Denoising Score Matching and Plug-and-Play – propose methodologies for drawing samples from this implicit prior and using it to solve inverse problems, respectively.
Learning efficient task-dependent representations with synaptic plasticity
Neural populations encode the sensory world imperfectly: their capacity is limited by the number of neurons, availability of metabolic and other biophysical resources, and intrinsic noise.
Flexible information routing in neural populations through stochastic comodulation
It has been observed that trial-to-trial neural activity is modulated by a shared, low-dimensional, stochastic signal that introduces task-irrelevant noise.
