Self-Excitation as a Mechanism for Adaptive Behavior and Autism Spectrum Variation
Location
Bent Corridor, Science Center
Document Type
Poster - Open Access
Start Date
5-1-2026 12:00 PM
End Date
5-1-2026 2:00 PM
Abstract
In this work, we explore how self-excitation, represented by excitatory self-weights (connection strengths from small populations of neurons to themselves), shapes adaptive behavior in dynamic environments. We hypothesize that the strength of self-excitation defines the autism spectrum in autism spectrum disorder (ASD). We simulate networks where self-excitation ranges between 0 and 1, systematically varying other parameters of the model to confirm that our findings are robust to these changes. Our results suggest that the optimal level of self-excitation depends on environmental stability. In rapidly changing environments, lower self-excitation leads to more flexible responses, favoring individuals lower on the autism spectrum who may adapt more easily. Conversely, in stable environments, higher self-excitation fosters rapid, consistent decisions, favoring individuals higher on the spectrum who may excel in routines. We also investigate the critical role of a decision timer, which modulates response timing. By linking self-excitation and timing mechanisms to environmental demands, this model offers a computational lens on autism-related behaviors. Together, these findings suggest that understanding the interplay between neural dynamics and environmental context may open new avenues for developing strategies that support individuals across the autism spectrum.
Keywords:
Computational neuroscience, Decision making, Autism Spectrum Disorder (ASD), Self-excitation
Recommended Citation
Yuan, Alicia and Tu, Alina, "Self-Excitation as a Mechanism for Adaptive Behavior and Autism Spectrum Variation" (2026). Research Symposium. 2.
https://digitalcommons.oberlin.edu/researchsymp/2026/posters/2
Major
Neuroscience
Project Mentor(s)
Pat Simen, Neuroscience
2026
Self-Excitation as a Mechanism for Adaptive Behavior and Autism Spectrum Variation
Bent Corridor, Science Center
In this work, we explore how self-excitation, represented by excitatory self-weights (connection strengths from small populations of neurons to themselves), shapes adaptive behavior in dynamic environments. We hypothesize that the strength of self-excitation defines the autism spectrum in autism spectrum disorder (ASD). We simulate networks where self-excitation ranges between 0 and 1, systematically varying other parameters of the model to confirm that our findings are robust to these changes. Our results suggest that the optimal level of self-excitation depends on environmental stability. In rapidly changing environments, lower self-excitation leads to more flexible responses, favoring individuals lower on the autism spectrum who may adapt more easily. Conversely, in stable environments, higher self-excitation fosters rapid, consistent decisions, favoring individuals higher on the spectrum who may excel in routines. We also investigate the critical role of a decision timer, which modulates response timing. By linking self-excitation and timing mechanisms to environmental demands, this model offers a computational lens on autism-related behaviors. Together, these findings suggest that understanding the interplay between neural dynamics and environmental context may open new avenues for developing strategies that support individuals across the autism spectrum.
Notes
Presenter: Alicia Yuan