Correlative Information Maximization: A Biologically Plausible Approach to Supervised Deep Neural Networks without Weight Symmetry

1 code implementation • NeurIPS 2023 • Bariscan Bozkurt, Cengiz Pehlevan, Alper T Erdogan

Furthermore, our approach provides a natural resolution to the weight symmetry problem between forward and backward signal propagation paths, a significant critique against the plausibility of the conventional backpropagation algorithm.

Correlative Information Maximization Based Biologically Plausible Neural Networks for Correlated Source Separation

1 code implementation • 9 Oct 2022 • Bariscan Bozkurt, Ates Isfendiyaroglu, Cengiz Pehlevan, Alper T. Erdogan

Here, we relax this limitation and propose a biologically plausible neural network that extracts correlated latent sources by exploiting information about their domains.

Biologically-Plausible Determinant Maximization Neural Networks for Blind Separation of Correlated Sources

2 code implementations • 27 Sep 2022 • Bariscan Bozkurt, Cengiz Pehlevan, Alper T. Erdogan

Previous work on biologically-plausible BSS algorithms assumed that observed signals are linear mixtures of statistically independent or uncorrelated sources, limiting the domain of applicability of these algorithms.

blind source separation

On Identifiable Polytope Characterization for Polytopic Matrix Factorization

1 code implementation • 25 Apr 2022 • Bariscan Bozkurt, Alper T. Erdogan

We show how this problem can be efficiently solved by using a graph automorphism algorithm.