Unsupervised methods for neural data analysis with single trial resolution
Abstract/Contents
- Abstract
- The activity of single neurons is stochastic: dissimilar spike patterns are often produced over nominally identical trials. This randomness has often limited the scope of neuroscience research to trial-averaged responses; however, advances in large-scale recording technologies are increasingly enabling statistical analyses with single-trial resolution. Nonetheless, single-trial dynamics are still poorly characterized in many cases, and approaches to characterizing these dynamics have not reached consensus. To meet this challenge, this thesis describes three statistical models that capture common forms of neural circuit activity and variability. First, single-trial variations in neural amplitude are statistically modeled through tensor decomposition. Second, an unsupervised time warping framework is developed to capture single-trial variations in temporal latency and duration. Finally, convolutional matrix factorization is used to extract recurring temporal motifs in the absence of human-annotated experimental trials. All methods are fit purely to neural data, and thus make few assumptions about the experimental design or behavioral task. Further, all methods flexibly model both linear and nonlinear dynamical behaviors in an interpretable and transparent manner. Practical results are shown on experimental data collected from diverse species (mice, rats, song birds, and nonhuman primates), behavioral contexts (olfaction, motor learning, decision-making, and motor production), and brain regions (olfactory bulb, premotor & motor cortex, and prefrontal cortex). These approaches revealed a wide variety of single-trial dynamical patterns including behavioral error detection and correction, incremental learning, spike-level oscillations, pulsatile responses to latent behavioral events, and neural ensembles that fire in sparse temporal sequences.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2019; ©2019 |
| Publication date | 2019; 2019 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Williams, Alexander Henry |
|---|---|
| Degree supervisor | Ganguli, Surya, 1977- |
| Thesis advisor | Ganguli, Surya, 1977- |
| Thesis advisor | Baccus, Stephen A |
| Thesis advisor | Druckmann, Shaul |
| Thesis advisor | Shenoy, Krishna V. (Krishna Vaughn) |
| Degree committee member | Baccus, Stephen A |
| Degree committee member | Druckmann, Shaul |
| Degree committee member | Shenoy, Krishna V. (Krishna Vaughn) |
| Associated with | Stanford University, Neurosciences Program. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Alex Henry Williams. |
|---|---|
| Note | Submitted to the Neurosciences Program. |
| Thesis | Thesis Ph.D. Stanford University 2019. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2019 by Alexander Henry Williams
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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