Computational Neuroscience Commons^™

Learning Expands The Preplanning Horizon In Finger Sequence Tasks, Neda Kordjazi

Electronic Thesis and Dissertation Repository

Many everyday skills involve the production of complex sequences of movements. However, the dynamics of the interplay between action selection and execution processes in sequential movements is poorly understood.Here, we set out to investigate the extent to which information regarding upcoming actions is utilized by the motor system to preplan into the future and furthermore, how this ability is influenced by learning. We designed a finger sequence taskwhere participants were shown only a fixed number of upcoming cues regarding future presses in every trial (viewing window, W). W varied between 1 (next digit revealed with pressing the current digit – …

Finding Nonlinear Relationships In Functional Magnetic Resonance Imaging Data With Genetic Programming, James Hughes

Electronic Thesis and Dissertation Repository

The human brain is a complex, nonlinear dynamic chaotic system that is poorly understood. When faced with these difficult to understand systems, it is common to observe the system and develop models such that the underlying system might be deciphered. When observing neurological activity within the brain with functional magnetic resonance imaging (fMRI), it is common to develop linear models of functional connectivity; however, these models are incapable of describing the nonlinearities we know to exist within the system.

A genetic programming (GP) system was developed to perform symbolic regression on recorded fMRI data. Symbolic regression makes fewer assumptions than …