Emergent perceptual biases from state-space geometry in trained spiking recurrent neural networks

• Published in: Cell reports (Cambridge) Vol. 43; no. 7; p. 114412
• Main Authors: Serrano-Fernández, Luis, Beirán, Manuel, Parga, Néstor
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 23.07.2024
• Subjects: Action Potentials - physiology; Animals; Bayes Theorem; Bayesian computations; decision-making; dynamical systems; Memory, Short-Term - physiology; Models, Neurological; Nerve Net - physiology; Neural Networks, Computer; Neurons - physiology; perceptual biases; recurrent neural networks; state-space geometry; dynamical systems; state-space geometry; perceptual biases; recurrent neural networks; decision-making; CP: Neuroscience; Bayesian computations

A stimulus held in working memory is perceived as contracted toward the average stimulus. This contraction bias has been extensively studied in psychophysics, but little is known about its origin from neural activity. By training recurrent networks of spiking neurons to discriminate temporal interva...