Emergent perceptual biases from state-space geometry in spiking recurrent neural networks trained to discriminate time intervals

• Published in: bioRxiv
• Main Authors: Serrano-Fernandez, Luis, Beiran, Manuel, Parga, Nestor
• Format: Paper
• Language: English
• Published: Cold Spring Harbor Cold Spring Harbor Laboratory Press 26.11.2022; Cold Spring Harbor Laboratory
• Edition: 1.3
• Subjects: Bayesian analysis; Bias; Cognitive ability; Firing pattern; Neural networks; Neuroscience; Psychophysics; Decision Making; Neural Networks; Dynamical Systems
• ISSN: 2692-8205; 2692-8205
• DOI: 10.1101/2022.11.26.518023

The contraction bias has been extensively studied in psychophysics, but only recently has its origin begun to be investigated with neural activity recordings. Leveraging the fact that it is currently possible to train recurrent networks of spiking neurons to perform cognitive tasks, we investigated the causes of the contraction bias and how behavior relates to population firing activity in networks trained to discriminate temporal intervals. A novel normative model containing potential sources of the bias guided us in investigating how it emerged in the networks. Various geometric features of state-space trajectories encoded compressed representations of the durations of the first interval, which were modulated by sensory history. Furthermore, these compressions conveyed a Bayesian estimate of the durations, thus relating activity and behavior. We conjecture that this occurs in areas of the brain where information about the current and preceding stimuli converges, and that it holds generally in delayed comparison tasks.Competing Interest StatementThe authors have declared no competing interest.