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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 Normative and Biologically Plausible Algorithm for Independent Component Analysis
Yanis Bahroun, Dmitri B Chklovskii, Anirvan M Sengupta
The brain effortlessly solves blind source separation (BSS) problems, but the algorithm it uses remains elusive. In signal processing, linear BSS problems are often solved by Indep…
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 Neural Network with Local Learning Rules for Minor Subspace Analysis
Yanis Bahroun, Dmitri B. Chklovskii
The development of neuromorphic hardware and modeling of biological neural networks requires algorithms with local learning rules. Artificial neural networks using local learning r…
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…