Increased dynamic range as a driver of ASD neuronal and behavioral differences

Successful social interactions rely on combining robust perception, quick updating of prior beliefs, and learning and generalizing from feedback. Individuals with Autistic Spectrum Disorder (ASD) exhibit difficulties in their social interactions compared with the neurotypical population (NT). Studying ASD enables decomposing social interactions to their basic cognitive processes to infer fundamental computational principles of social interactions. ASD compared to NT show heightened discriminability between stimuli, higher variance in neuronal activity, slower updating, deficits in learning and generalization, and reduced encoding capacity. Here, we suggest that these myriad of differences stem from a computational principle that relies on the dynamic range of the neuronal population response. The dynamic range of a sensing system is the range of signal values for which the system is responsive. We show that an increased dynamic range in the neuronal population response accounts for the neuronal and behavioral differences seen in ASD, across all outlined tasks and conditions. We further specify a plausible biological mechanism for the increase in the dynamic range, namely increased heterogeneity in the half-activation point of individual neurons in ASD. These findings suggest that the dynamic range of the neuronal population serves as a key factor to support the cognitive processes underlying social interactions as well as a principled way to tune these in artificial agents.