no code implementations • 2 Aug 2023 • Yanis Bahroun, Dmitri B. Chklovskii, Anirvan M. Sengupta
In this work, we focus not on developing new algorithms but on showing that the Representer theorem offers the perfect lens to study biologically plausible learning algorithms.
no code implementations • 2 Aug 2023 • Yanis Bahroun, Shagesh Sridharan, Atithi Acharya, Dmitri B. Chklovskii, Anirvan M. Sengupta
This study focuses on the primarily unsupervised similarity matching (SM) framework, which aligns with observed mechanisms in biological systems and offers online, localized, and biologically plausible algorithms.
no code implementations • 20 Feb 2023 • David Lipshutz, Yanis Bahroun, Siavash Golkar, Anirvan M. Sengupta, Dmitri B. Chklovskii
These NN models account for many anatomical and physiological observations; however, the objectives have limited computational power and the derived NNs do not explain multi-compartmental neuronal structures and non-Hebbian forms of plasticity that are prevalent throughout the brain.
1 code implementation • 27 Oct 2022 • Siavash Golkar, Tiberiu Tesileanu, Yanis Bahroun, Anirvan M. Sengupta, Dmitri B. Chklovskii
The network we derive does not involve one-to-one connectivity or signal multiplexing, which the phenomenological models required, indicating that these features are not necessary for learning in the cortex.
no code implementations • 16 Feb 2022 • Romain Cosentino, Randall Balestriero, Yanis Bahroun, Anirvan Sengupta, Richard Baraniuk, Behnaam Aazhang
This enables (i) the reduction of intrinsic nuisances associated with the data, reducing the complexity of the clustering task and increasing performances and producing state-of-the-art results, (ii) clustering in the input space of the data, leading to a fully interpretable clustering algorithm, and (iii) the benefit of convergence guarantees.
no code implementations • NeurIPS 2021 • Yanis Bahroun, Dmitri B Chklovskii, Anirvan M Sengupta
The brain effortlessly solves blind source separation (BSS) problems, but the algorithm it uses remains elusive.
no code implementations • 10 Feb 2021 • Yanis Bahroun, Dmitri B. Chklovskii
However, no biologically plausible networks exist for minor subspace analysis (MSA), a fundamental signal processing task.
no code implementations • 10 Feb 2021 • Yanis Bahroun, Anirvan M. Sengupta, Dmitri B. Chklovskii
Unfortunately, it is difficult to map their model onto a biologically plausible neural network (NN) with local learning rules.
no code implementations • 16 Dec 2020 • Romain Cosentino, Randall Balestriero, Yanis Bahroun, Anirvan Sengupta, Richard Baraniuk, Behnaam Aazhang
We design an interpretable clustering algorithm aware of the nonlinear structure of image manifolds.
no code implementations • 30 Nov 2020 • Siavash Golkar, David Lipshutz, Yanis Bahroun, Anirvan M. Sengupta, Dmitri B. Chklovskii
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 of brain function.
no code implementations • NeurIPS 2020 • Siavash Golkar, David Lipshutz, Yanis Bahroun, Anirvan M. Sengupta, Dmitri B. Chklovskii
Here, adopting a normative approach, we model these instructive signals as supervisory inputs guiding the projection of the feedforward data.
1 code implementation • 1 Oct 2020 • David Lipshutz, Yanis Bahroun, Siavash Golkar, Anirvan M. Sengupta, Dmitri B. Chklovskii
For biological plausibility, we require that the network operates in the online setting and its synaptic update rules are local.
no code implementations • NeurIPS 2019 • Yanis Bahroun, Dmitri Chklovskii, Anirvan Sengupta
Unfortunately, it is difficult to map their model onto a biologically plausible neural network (NN) with local learning rules.
no code implementations • 21 Feb 2017 • Yanis Bahroun, Andrea Soltoggio
Recently, a new class of Hebbian-like and local unsupervised learning rules for neural networks have been developed that minimise a similarity matching cost-function.