Work

Bayesian reconstruction of memories stored in neural networks from their connectivity

Year: 2023

Type: article

Abstract: The advent of comprehensive synaptic wiring diagrams of large neural circuits has created the field of connectomics and given rise to a number of open research questions. One such question is whether it is possible to reconstruct the information stored in a recurrent network of neurons, given its synaptic connectivity matrix. Here, we address this question by determining when solving such an inference problem is theoretically possible in specific attractor network models and by providing a practical algorithm to do so. The algorithm builds on ideas from statistical physics to perform approximate Bayesian inference and is amenable to exact analysis. We study its performance on three different models, compare the algorithm to standard algorithms such as PCA, and explore the limitations of reconstructing stored patterns from synaptic connectivity.

Source: PLoS Computational Biology

DOI: 10.1371/journal.pcbi.1010813

Authors: Sebastian Goldt, Florent Krzakala, Lenka Zdeborová, Nicolas Brunel

Institutions: Scuola Internazionale Superiore di Studi Avanzati, École Polytechnique Fédérale de Lausanne, Duke University

Language: English

Topic: Neural dynamics and brain function

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $2,655

Funders: European Commission, Horizon 2020 Framework Programme