From statistical mechanics to machine learning: effective models for neural activity
| dc.contributor.advisor | Rohwer, Christian | |
| dc.contributor.advisor | Shock, Jonathan | |
| dc.contributor.author | Schonfeldt , Abram | |
| dc.date.accessioned | 2023-04-28T10:18:07Z | |
| dc.date.available | 2023-04-28T10:18:07Z | |
| dc.date.issued | 2022 | |
| dc.date.updated | 2023-04-28T10:17:44Z | |
| dc.description.abstract | In the retina, the activity of ganglion cells, which feed information through the optic nerve to the rest of the brain, is all that our brain will ever know about the visual world. The interactions between many neurons are essential to processing visual information and a growing body of evidence suggests that the activity of populations of retinal ganglion cells cannot be understood from knowledge of the individual cells alone. Modelling the probability of which cells in a population will fire or remain silent at any moment in time is a difficult problem because of the exponentially many possible states that can arise, many of which we will never even observe in finite recordings of retinal activity. To model this activity, maximum entropy models have been proposed which provide probabilistic descriptions over all possible states but can be fitted using relatively few well-sampled statistics. Maximum entropy models have the appealing property of being the least biased explanation of the available information, in the sense that they maximise the information theoretic entropy. We investigate this use of maximum entropy models and examine the population sizes and constraints that they require in order to learn nontrivial insights from finite data. Going beyond maximum entropy models, we investigate autoencoders, which provide computationally efficient means of simplifying the activity of retinal ganglion cells. | |
| dc.identifier.apacitation | Schonfeldt , A. (2022). <i>From statistical mechanics to machine learning: effective models for neural activity</i>. (). ,Faculty of Science ,Department of Mathematics and Applied Mathematics. Retrieved from http://hdl.handle.net/11427/37848 | en_ZA |
| dc.identifier.chicagocitation | Schonfeldt , Abram. <i>"From statistical mechanics to machine learning: effective models for neural activity."</i> ., ,Faculty of Science ,Department of Mathematics and Applied Mathematics, 2022. http://hdl.handle.net/11427/37848 | en_ZA |
| dc.identifier.citation | Schonfeldt , A. 2022. From statistical mechanics to machine learning: effective models for neural activity. . ,Faculty of Science ,Department of Mathematics and Applied Mathematics. http://hdl.handle.net/11427/37848 | en_ZA |
| dc.identifier.ris | TY - Master Thesis AU - Schonfeldt , Abram AB - In the retina, the activity of ganglion cells, which feed information through the optic nerve to the rest of the brain, is all that our brain will ever know about the visual world. The interactions between many neurons are essential to processing visual information and a growing body of evidence suggests that the activity of populations of retinal ganglion cells cannot be understood from knowledge of the individual cells alone. Modelling the probability of which cells in a population will fire or remain silent at any moment in time is a difficult problem because of the exponentially many possible states that can arise, many of which we will never even observe in finite recordings of retinal activity. To model this activity, maximum entropy models have been proposed which provide probabilistic descriptions over all possible states but can be fitted using relatively few well-sampled statistics. Maximum entropy models have the appealing property of being the least biased explanation of the available information, in the sense that they maximise the information theoretic entropy. We investigate this use of maximum entropy models and examine the population sizes and constraints that they require in order to learn nontrivial insights from finite data. Going beyond maximum entropy models, we investigate autoencoders, which provide computationally efficient means of simplifying the activity of retinal ganglion cells. DA - 2022_ DB - OpenUCT DP - University of Cape Town KW - applied mathematics LK - https://open.uct.ac.za PY - 2022 T1 - ETD: From statistical mechanics to machine learning: effective models for neural activity TI - ETD: From statistical mechanics to machine learning: effective models for neural activity UR - http://hdl.handle.net/11427/37848 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/37848 | |
| dc.identifier.vancouvercitation | Schonfeldt A. From statistical mechanics to machine learning: effective models for neural activity. []. ,Faculty of Science ,Department of Mathematics and Applied Mathematics, 2022 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/37848 | en_ZA |
| dc.language.rfc3066 | eng | |
| dc.publisher.department | Department of Mathematics and Applied Mathematics | |
| dc.publisher.faculty | Faculty of Science | |
| dc.subject | applied mathematics | |
| dc.title | From statistical mechanics to machine learning: effective models for neural activity | |
| dc.type | Master Thesis | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationlevel | MSc |