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Anirvan M. Sengupta

6 accepted papers

2026

A Network of Biologically Inspired Rectified Spectral Units (ReSUs) Learns Hierarchical Features Without Error Backpropagation

Shanshan Qin, Joshua L. Pughe-Sanford, Alexander Genkin, Pembe Gizem Ozdil, Philip Greengard, Anirvan M. Sengupta +1
AAAI 2026technical

We introduce a biologically inspired, multilayer neural architecture composed of Rectified Spectral Units (ReSUs). Each ReSU projects a recent window of its input history onto a canonical direction obtained via canonical correlation analysis (CCA) of previously observed past–future input pairs, and

Cited by 0Source ↗PDF ↗Scholar ↗
2025

In-Context Denoising with One-Layer Transformers: Connections between Attention and Associative Memory Retrieval

Matthew Smart, Alberto Bietti, Anirvan M. Sengupta
ICML 2025oral

We introduce in-context denoising, a task that refines the connection between attention-based architectures and dense associative memory (DAM) networks, also known as modern Hopfield networks. Using a Bayesian framework, we show theoretically and empirically that certain restricted denoising problem…

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2025

Neurons as Detectors of Coherent Sets in Sensory Dynamics

Joshua L. Pughe-Sanford, Xuehao Ding, Jason J Moore, Anirvan M. Sengupta, Charles Epstein, Philip Greengard +1
NeurIPS 2025poster

We model sensory streams as observations from high-dimensional stochastic dynamical systems and conceptualize sensory neurons as self-supervised learners of compact representations of such dynamics. From prior experience, neurons learn {\it coherent sets}—regions of stimulus state space whose trajec…

Cited by 1Source ↗Scholar ↗
2022

Constrained Predictive Coding as a Biologically Plausible Model of the Cortical Hierarchy

Siavash Golkar, Tiberiu Tesileanu, Yanis Bahroun, Anirvan M. Sengupta, Dmitri Chklovskii
NeurIPS 2022accept

Predictive coding (PC) has emerged as an influential normative model of neural computation with numerous extensions and applications. As such, much effort has been put into mapping PC faithfully onto the cortex, but there are issues that remain unresolved or controversial. In particular, current imp…

Cited by 16Source ↗PDF ↗code ↗Scholar ↗
2021

A Normative and Biologically Plausible Algorithm for Independent Component Analysis

Yanis Bahroun, Dmitri Chklovskii, Anirvan M. Sengupta
NeurIPS 2021spotlight

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 Independent Component Analysis (ICA). To serve as a model of a biological circuit, the ICA neural network (NN) must satisfy a…

Cited by 11Source ↗PDF ↗Scholar ↗
2021

Neural optimal feedback control with local learning rules

Johannes Friedrich, Siavash Golkar, Shiva Farashahi, Alexander Genkin, Anirvan M. Sengupta, Dmitri Chklovskii
NeurIPS 2021spotlight

A major problem in motor control is understanding how the brain plans and executes proper movements in the face of delayed and noisy stimuli. A prominent framework for addressing such control problems is Optimal Feedback Control (OFC). OFC generates control actions that optimize behaviorally relevan…

Cited by 16Source ↗PDF ↗Scholar ↗