64 citations · 94 across the 12 of their papers we have counts for
4 papers · 1 filter
Duality Principle and Biologically Plausible Learning: Connecting the Representer Theorem and Hebbian Learning
Yanis Bahroun, Dmitri B. Chklovskii, Anirvan M. Sengupta
A normative approach called Similarity Matching was recently introduced for deriving and understanding the algorithmic basis of neural computation focused on unsupervised problems.…
Unlocking the Potential of Similarity Matching: Scalability, Supervision and Pre-training
Yanis Bahroun, Shagesh Sridharan, Atithi Acharya +2
While effective, the backpropagation (BP) algorithm exhibits limitations in terms of biological plausibility, computational cost, and suitability for online learning. As a result,…
A Similarity-preserving Neural Network Trained on Transformed Images Recapitulates Salient Features of the Fly Motion Detection Circuit
Yanis Bahroun, Anirvan M. Sengupta, Dmitri B. Chklovskii
Learning to detect content-independent transformations from data is one of the central problems in biological and artificial intelligence. An example of such problem is unsupervise…
A biologically plausible neural network for local supervision in cortical microcircuits
Siavash Golkar, David Lipshutz, Yanis Bahroun +2
The backpropagation algorithm is an invaluable tool for training artificial neural networks; however, because of a weight sharing requirement, it does not provide a plausible model…