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Anirvan Sengupta

3 accepted papers

2020

A simple normative network approximates local non-Hebbian learning in the cortex

NeurIPS 2020poster

To guide behavior, the brain extracts relevant features from high-dimensional data streamed by sensory organs. Neuroscience experiments demonstrate that the processing of sensory inputs by cortical neurons is modulated by instructive signals which provide context and task-relevant information. Here,…

Cited by 20SourcePDFScholar
2019

A Similarity-preserving Network Trained on Transformed Images Recapitulates Salient Features of the Fly Motion Detection Circuit

NeurIPS 2019poster

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 unsupervised learning of a visual motion detector from pairs of consecutive video frames. Rao and Ruderman formulated this problem…

Cited by 15SourcePDFScholar
2018

Manifold-tiling Localized Receptive Fields are Optimal in Similarity-preserving Neural Networks

NeurIPS 2018poster

Many neurons in the brain, such as place cells in the rodent hippocampus, have localized receptive fields, i.e., they respond to a small neighborhood of stimulus space. What is the functional significance of such representations and how can they arise? Here, we propose that localized receptive field…