Search Results for author:
Found 5 papers, 1 papers with code
Towards an integration of deep learning and neuroscience
We hypothesize that (1) the brain optimizes cost functions, (2) these cost functions are diverse and differ across brain locations and over development, and (3) optimization operates within a pre-structured architecture matched to the computational problems posed by behavior.
Neurons and Cognition
Product Kanerva Machines: Factorized Bayesian Memory
An ideal cognitively-inspired memory system would compress and organize incoming items.
Gaussian Gated Linear Networks
We propose the Gaussian Gated Linear Network (G-GLN), an extension to the recently proposed GLN family of deep neural networks.
Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution
Neuroscience has long been an essential driver of progress in artificial intelligence (AI).
To reverse engineer an entire nervous system
More fundamentally, reconstructing or modeling a system is an important milestone in understanding it, and so, simulating an entire nervous system is in itself one of the goals, indeed dreams, of systems neuroscience.
