Analysis and Application of a Realtime Closed-loop Hippocampal Sharp-wave Ripple Disruption System
| dc.contributor.advisor | Kemere, Caleb T | en_US |
| dc.creator | Dutta, Shayok | en_US |
| dc.date.accessioned | 2019-08-12T14:01:48Z | en_US |
| dc.date.available | 2019-08-12T14:01:48Z | en_US |
| dc.date.created | 2019-08 | en_US |
| dc.date.issued | 2019-08-09 | en_US |
| dc.date.submitted | August 2019 | en_US |
| dc.date.updated | 2019-08-12T14:01:48Z | en_US |
| dc.description.abstract | Closed-loop modulation of neural activity facilitates causal tests of hypothesis to link temporal neural circuit functions to behavior and cognition. One such dynamically-changing neural circuit in the brain is the hippocampus, responsible for learning and memory. Transient bursts of neural activity in the hippocampus during periods of quiescence, sharp-wave ripple (SWR) events --- ≈80--150 ms periods comprising large 150--250 Hz oscillations --- have previously been established to serve a critical role in memory consolidation and recall through selective closed-loop interactions. The timescale within which these events occur indicate that latency in systems used for closed-loop perturbations is of significant consequence when compared to other longer-lasting events. However, though this approach is beginning to become widespread, performance trade-offs involved in the fabrication of SWR detection and disruptions have not been fully characterized, thereby, limiting the understanding of results using this experimental procedure. In this thesis, I present the development and characterization of a low-latency closed-loop system for SWR detection and modulation engineered to interface with two separate widely-used neural data acquisition hardware platforms. The analysis and characterization of the system is done with a two-pronged approach: (1) generation of a synthetic data to model SWR activity in order explore the parameter space of the realtime detection algorithm and (2) quantification of the realtime in vivo performance of the algorithm and system via offline simulation. Lastly, realtime in vivo detections of SWRs are shown match the simulated detections with added hardware data transmission latency. By performing this characterization, I have established a framework for analyzing other closed-loop systems for neural perturbations. Next, I deploy this system in a neuroscientific closed-loop experiment. In particular, rodents have an innate curiosity to explore novel contexts and objects resulting in the spending more time with novel objects than familiar ones. Work has been done through targeted lesion studies to establish hippocampal dependence of this object recognition memory via novel object test (NOT) paradigms. More recent work has explored correlations of hippocampal CA1 signatures, such as fast-gamma oscillations, to be of importance in NOT object-place recognition memories and demonstrated predictable changes in SWRs. More specifically, SWRs have been shown to increase after encoding of novel objects (or familiar objects in novel locations) and general novelty in the test; however, the concomitant spiking activity has not been correlated with object-place pairings leading to questioning the role of these events in object-place recognition memories. By using the previously engineered system, I demonstrate preliminary results showing suppression of this novelty preference in a rat during a hippocampally dependent novel object test paradigm. This preliminary finding suggests SWRs play a role in object-place recognition memory and not just in sequential goal oriented experimental paradigms as previous results have shown. | en_US |
| dc.format.mimetype | application/pdf | en_US |
| dc.identifier.citation | Dutta, Shayok. "Analysis and Application of a Realtime Closed-loop Hippocampal Sharp-wave Ripple Disruption System." (2019) Master’s Thesis, Rice University. <a href="https://hdl.handle.net/1911/106185">https://hdl.handle.net/1911/106185</a>. | en_US |
| dc.identifier.uri | https://hdl.handle.net/1911/106185 | en_US |
| dc.language.iso | eng | en_US |
| dc.rights | Copyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder. | en_US |
| dc.subject | closed-loop | en_US |
| dc.subject | neuroengineering | en_US |
| dc.subject | sharp-wave ripples | en_US |
| dc.subject | sharp wave ripples | en_US |
| dc.subject | hippocampus | en_US |
| dc.subject | learning | en_US |
| dc.subject | memory | en_US |
| dc.subject | consolidation | en_US |
| dc.subject | novel object test | en_US |
| dc.subject | neuroscience | en_US |
| dc.title | Analysis and Application of a Realtime Closed-loop Hippocampal Sharp-wave Ripple Disruption System | en_US |
| dc.type | Thesis | en_US |
| dc.type.material | Text | en_US |
| thesis.degree.department | Electrical and Computer Engineering | en_US |
| thesis.degree.discipline | Engineering | en_US |
| thesis.degree.grantor | Rice University | en_US |
| thesis.degree.level | Masters | en_US |
| thesis.degree.name | Master of Science | en_US |
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