Full metadata
Title
Neural dynamics of single units in rat's agranular medial and agranular lateral areas during learning of a directional choice task
Description
Learning by trial-and-error requires retrospective information that whether a past action resulted in a rewarded outcome. Previous outcome in turn may provide information to guide future behavioral adjustment. But the specific contribution of this information to learning a task and the neural representations during the trial-and-error learning process is not well understood. In this dissertation, such learning is analyzed by means of single unit neural recordings in the rats' motor agranular medial (AGm) and agranular lateral (AGl) while the rats learned to perform a directional choice task. Multichannel chronic recordings using implanted microelectrodes in the rat's brain were essential to this study. Also for fundamental scientific investigations in general and for some applications such as brain machine interface, the recorded neural waveforms need to be analyzed first to identify neural action potentials as basic computing units. Prior to analyzing and modeling the recorded neural signals, this dissertation proposes an advanced spike sorting system, the M-Sorter, to extract the action potentials from raw neural waveforms. The M-Sorter shows better or comparable performance compared with two other popular spike sorters under automatic mode. With the sorted action potentials in place, neuronal activity in the AGm and AGl areas in rats during learning of a directional choice task is examined. Systematic analyses suggest that rat's neural activity in AGm and AGl was modulated by previous trial outcomes during learning. Single unit based neural dynamics during task learning are described in detail in the dissertation. Furthermore, the differences in neural modulation between fast and slow learning rats were compared. The results show that the level of neural modulation of previous trial outcome is different in fast and slow learning rats which may in turn suggest an important role of previous trial outcome encoding in learning.
Date Created
2014
Contributors
- Yuan, Yu'an (Author)
- Si, Jennie (Thesis advisor)
- Buneo, Christopher (Committee member)
- Santello, Marco (Committee member)
- Chae, Junseok (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
viii, 94 p. : ill. (some col.)
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.I.25030
Statement of Responsibility
by Yuan Yuan
Description Source
Viewed on Aug. 13, 2014
Level of coding
full
Note
thesis
Partial requirement for: Ph.D., Arizona State University, 2014
bibliography
Includes bibliographical references (p. 84-94)
Field of study: Electrical engineering
System Created
- 2014-06-09 02:12:41
System Modified
- 2021-08-30 01:34:38
- 3 years 2 months ago
Additional Formats