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Poster Session C: Friday, August 15, 2:00 – 5:00 pm, de Brug & E‑Hall

Decoding Movement from Neural Spike Trains: A Comparison of Linear and Nonlinear Models across Brain Regions and Temporal Delays

Minsung Cho¹, Jaesung Yoo¹, Stefan M Lemke¹, Jian Zhong Guo¹, Simone Appaqaq, Adam W Hantman; ¹University of North Carolina at Chapel Hill

Presenter: Minsung Cho

Neural spike trains, which represent the spiking activity of neural population over time, provide critical insights into how the brain encodes information and generates behavior. Despite significant advances, the extent to which these spike trains encode behavioral variables—particularly movement—remains not fully understood. In this study, we compare the performance of linear and nonlinear models in predicting behavior from neural spike trains, focusing on how prediction accuracy varies with different temporal lags between neural activity and movement onset. Furthermore, we examine how prediction performance depends on the specific brain regions from which the neural signals are recorded. Our findings provide new insights into the behavioral decoding with respect to both the temporal structure of neural spike activity and the specificity of brain regions.

Topic Area: Predictive Processing & Cognitive Control

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