A geometrical solution underlies general neural principle for serial ordering

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A geometrical solution underlies general neural principle for serial ordering

Title: A geometrical solution underlies general neural principle for serial ordering

Authors: Gabriele Di Antonio (Natl. Center for Radiation Protection and Computational Physics, Istituto Superiore di Sanità, Rome, PhD Program in Applied Electronics, ‘Roma Tre’ University of Rome, Research Center ‘Enrico Fermi’), Sofia Raglio (Natl. Center for Radiation Protection and Computational Physics, Istituto Superiore di Sanità, Rome, Italy, PhD Program in Behavioral Neuroscience, ‘Sapienza’ University of Rome), Maurizio Mattia (Natl. Center for Radiation Protection and Computational Physics, Istituto Superiore di Sanità, Rome)

Published on Nature Communications volume 15, Article number: 8238 (2024) DOI: https://doi.org/10.3390/polym16131850

Abstract

A general mathematical description of how the brain sequentially encodes knowledge remains elusive. We propose a linear solution for serial learning tasks, based on the concept of mixed selectivity in high-dimensional neural state spaces. In our framework, neural representations of items in a sequence are projected along a “geometric” mental line learned through classical conditioning. The model successfully solves serial position tasks and explains behaviors observed in humans and animals during transitive inference tasks amidst noisy sensory input and stochastic neural activity. This approach extends to recurrent neural networks performing motor decision tasks, where the same geometric mental line correlates with motor plans and modulates network activity according to the symbolic distance between items. Serial ordering is thus predicted to emerge as a monotonic mapping between sensory input and behavioral output, highlighting a possible pivotal role for motor-related associative cortices in transitive inference tasks.

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