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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,…
Adaptive whitening with fast gain modulation and slow synaptic plasticity
Lyndon R. Duong, Eero P. Simoncelli, Dmitri B. Chklovskii +1
Neurons in early sensory areas rapidly adapt to changing sensory statistics, both by normalizing the variance of their individual responses and by reducing correlations between the…
Normative framework for deriving neural networks with multi-compartmental neurons and non-Hebbian plasticity
David Lipshutz, Yanis Bahroun, Siavash Golkar +2
An established normative approach for understanding the algorithmic basis of neural computation is to derive online algorithms from principled computational objectives and evaluate…
An Out-of-Domain Synapse Detection Challenge for Microwasp Brain Connectomes
Jingpeng Wu, Yicong Li, Nishika Gupta +6
The size of image stacks in connectomics studies now reaches the terabyte and often petabyte scales with a great diversity of appearance across brain regions and samples. However,…
Adaptive whitening in neural populations with gain-modulating interneurons
Lyndon R. Duong, David Lipshutz, David J. Heeger +2
Statistical whitening transformations play a fundamental role in many computational systems, and may also play an important role in biological sensory systems. Existing neural circ…