Autism spectrum disorder variation as a computational trade-off via dynamic range of neuronal population responses
Year: 2024
Type: article
Abstract: Individuals diagnosed with autism spectrum disorder (ASD) show neural and behavioral characteristics differing from the neurotypical population. This may stem from a computational principle that relates inference and computational dynamics to the dynamic range of neuronal population responses, reflecting the signal levels for which the system is responsive. In the present study, we showed that an increased dynamic range (IDR), indicating a gradual response of a neuronal population to changes in input, accounts for neural and behavioral variations in individuals diagnosed with ASD across diverse tasks. We validated the model with data from finger-tapping synchronization, orientation reproduction and global motion coherence tasks. We suggested that increased heterogeneity in the half-activation point of individual neurons may be the biological mechanism underlying the IDR in ASD. Taken together, this model provides a proof of concept for a new computational principle that may account for ASD and generates new testable and distinct predictions regarding its behavioral, neural and biological foundations. Individuals with autism spectrum disorder show neural patterns different from those of neurotypical individuals. Here the authors show that this variation reflects a computational trade-off between accurate encoding and fast adaptation tuned by the neural population response.
Source: Nature Neuroscience
Authors: Oded Wertheimer, Yuval Hart
Institution: Hebrew University of Jerusalem
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $12,290
Funder: Israel Science Foundation
Award: G4473548987
OpenAlex ID: W4404764810