A goal-driven approach to systems neuroscience
Abstract/Contents
- Abstract
- Humans and animals exhibit a range of interesting behaviors in dynamic environments, and it is unclear how our brains actively reformat this dense sensory information to enable these behaviors. Experimental neuroscience is undergoing a revolution in its ability to record and manipulate hundreds to thousands of neurons while an animal is performing a complex behavior. As these paradigms enable unprecedented access to the brain, a natural question that arises is how to distill these data into interpretable insights about how neural circuits give rise to intelligent behaviors. The classical approach in systems neuroscience has been to ascribe well-defined operations to individual neurons and provide a description of how these operations combine to produce a circuit-level theory of neural computations. While this approach has had some success for small-scale recordings with simple stimuli, designed to probe a particular circuit computation, often times these ultimately lead to disparate descriptions of the same system across stimuli. Perhaps more strikingly, many response profiles of neurons are difficult to succinctly describe in words, suggesting that new approaches are needed in light of these experimental observations. In this thesis, we offer a different definition of interpretability that we show has promise in yielding unified structural and functional models of neural circuits, and describes the evolutionary constraints that give rise to the response properties of the neural population, including those that have previously been difficult to describe individually. Specifically, our approach is to "reverse engineer" neural circuits by simulating the evolutionary process to build in-silico neural networks that are subject to the combined interaction of three types of constraints: the "task", expressed as an objective function to be maximized or minimized given a data stream; the network "architecture", expressed as the connections through which inputs flow; and the "learning rule", expressed as synaptic weight updates. This joint set of constraints is an interpretable hypothesis for the evolutionary design principles that enable a biological circuit to perform its computations, and crucially, the set of combinations of these constraints gives rise to multiple hypotheses that will be quantitatively evaluated against high-throughput neural and behavioral recordings. We demonstrate the utility of this framework across multiple brain areas and species to study the roles of recurrent processing in the primate ventral visual pathway; mouse visual processing; heterogeneity in rodent medial entorhinal cortex; and facilitating biological learning.
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 | 2022; ©2022 |
| Publication date | 2022; 2022 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Nayebi, Aran | |
|---|---|---|
| Degree supervisor | Ganguli, Surya, 1977- | |
| Degree supervisor | Yamins, Daniel | |
| Thesis advisor | Ganguli, Surya, 1977- | |
| Thesis advisor | Yamins, Daniel | |
| Thesis advisor | Baccus, Stephen A | |
| Thesis advisor | Druckmann, Shaul | |
| Thesis advisor | Sussillo, David | |
| Degree committee member | Baccus, Stephen A | |
| Degree committee member | Druckmann, Shaul | |
| Degree committee member | Sussillo, David | |
| Associated with | Stanford University, Neurosciences Program |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Aran Nayebi. |
|---|---|
| Note | Submitted to the Neurosciences Program. |
| Thesis | Thesis Ph.D. Stanford University 2022. |
| Location | https://purl.stanford.edu/qk457cr2641 |
Access conditions
- Copyright
- © 2022 by Aran Nayebi
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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