93 citations · 578 across the 83 of their papers we have counts for
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Computation with Sequences in a Model of the Brain
Max Dabagia, Christos H. Papadimitriou, Santosh S. Vempala
Even as machine learning exceeds human-level performance on many applications, the generality, robustness, and rapidity of the brain's learning capabilities remain unmatched. How c…
How and When Random Feedback Works: A Case Study of Low-Rank Matrix Factorization
Shivam Garg, Santosh S. Vempala
The success of gradient descent in ML and especially for learning neural networks is remarkable and robust. In the context of how the brain learns, one aspect of gradient descent t…
Assemblies of neurons learn to classify well-separated distributions
Max Dabagia, Christos H. Papadimitriou, Santosh S. Vempala
An assembly is a large population of neurons whose synchronous firing is hypothesized to represent a memory, concept, word, and other cognitive categories. Assemblies are believed…
Cortical Computation via Iterative Constructions
Christos Papadimitrou, Samantha Petti, Santosh Vempala
We study Boolean functions of an arbitrary number of input variables that can be realized by simple iterative constructions based on constant-size primitives. This restricted type…
Unsupervised Learning through Prediction in a Model of Cortex
Christos H. Papadimitriou, Santosh S. Vempala
We propose a primitive called PJOIN, for "predictive join," which combines and extends the operations JOIN and LINK, which Valiant proposed as the basis of a computational theory o…