國立虎尾科技大學 |

Machine Learning Approaches Towards Understanding Movement Planning in Naturalistic Settings.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Machine Learning Approaches Towards Understanding Movement Planning in Naturalistic Settings./
作者:	Glaser, Joshua I.
面頁冊數:	1 online resource (265 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.
Contained By:	Dissertation Abstracts International79-08B(E).
標題:	Neurosciences. -
電子資源:	click for full text (PQDT)
ISBN:	9780355824711

Machine Learning Approaches Towards Understanding Movement Planning in Naturalistic Settings.
Glaser, Joshua I.

Machine Learning Approaches Towards Understanding Movement Planning in Naturalistic Settings. - 1 online resource (265 pages)

Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.

Thesis (Ph.D.)--Northwestern University, 2018.

Includes bibliographical references

A central question in neuroscience is how the brain plans movements. Here, I apply neural data analysis and machine learning methods to better understand both eye and arm movement planning, in particular focusing on naturalistic settings. First, I built encoding models to investigate the factors that led to neural activity in macaque Frontal Eye Field (FEF) during a natural scene search task (Ch. 2,3,4). One central finding was that FEF neurons did not represent task-relevant visual features within natural scenes. Another central finding was that separate populations of neurons represented preliminary and definitive plans for movement. The neurons that represented preliminary plans represented the probabilities of potential upcoming saccades. I found similar characteristics in dorsal premotor cortex, where populations of neurons represented the probability distributions of possible upcoming reaches (Ch. 5). Finally, I compared many different methods for neural decoding to demonstrate that modern machine learning methods lead to performance improvements, even for limited amounts of data (Ch. 6). Overall, I have provided insights into neural activity across a wide range of motor behaviors in more naturalistic settings, and have demonstrated the value of using machine learning methods within neuroscience.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355824711Subjects--Topical Terms:

593561
Neurosciences.
Index Terms--Genre/Form:

554714
Electronic books.

Machine Learning Approaches Towards Understanding Movement Planning in Naturalistic Settings.
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500 $a Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.
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502 $a Thesis (Ph.D.)--Northwestern University, 2018.
504 $a Includes bibliographical references
520 $a A central question in neuroscience is how the brain plans movements. Here, I apply neural data analysis and machine learning methods to better understand both eye and arm movement planning, in particular focusing on naturalistic settings. First, I built encoding models to investigate the factors that led to neural activity in macaque Frontal Eye Field (FEF) during a natural scene search task (Ch. 2,3,4). One central finding was that FEF neurons did not represent task-relevant visual features within natural scenes. Another central finding was that separate populations of neurons represented preliminary and definitive plans for movement. The neurons that represented preliminary plans represented the probabilities of potential upcoming saccades. I found similar characteristics in dorsal premotor cortex, where populations of neurons represented the probability distributions of possible upcoming reaches (Ch. 5). Finally, I compared many different methods for neural decoding to demonstrate that modern machine learning methods lead to performance improvements, even for limited amounts of data (Ch. 6). Overall, I have provided insights into neural activity across a wide range of motor behaviors in more naturalistic settings, and have demonstrated the value of using machine learning methods within neuroscience.
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