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PODDAR-DISSERTATION-2015.Pdf (14.54Mb) A high-throughput system for automated training combined with continuous long-term neural recordings in rodents The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Poddar, Rajesh. 2015. A high-throughput system for automated training combined with continuous long-term neural recordings in rodents. Doctoral dissertation, Harvard University, Graduate School of Arts & Sciences. Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:17463149 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA A high-throughput system for automated training combined with continuous long-term neural recordings in rodents A dissertation presented by Rajesh Poddar to The Division of Medical Sciences in partial fulfillment of the requirements for the degree of Doctor of Philosophy In the subject of Neurobiology Harvard University Cambridge, Massachusetts April 2015 © 2015 Rajesh Poddar All rights reserved. Dissertation Advisor: Dr. Bence P. Ölveczky Rajesh Poddar A high-throughput system for automated training combined with continuous long-term neural recordings in rodents Abstract Addressing the neural mechanisms underlying complex learned behaviors requires training animals in well-controlled tasks and concurrently measuring neural activity in their brains, an often time-consuming and labor-intensive process that can severely limit the feasibility of such studies. To overcome this constraint, we developed a fully computer-controlled general purpose system for high-throughput training of rodents. By standardizing and automating the -cage our system dramatically reduces the efforts involved in animal training while also removing human errors and biases from the process. We deployed this system to train rats in a variety of sensorimotor tasks, achieving learning rates comparable to existing, but more laborious, methods. By incrementally and systematically increasing the difficulty of the task over weeks of training, rats were able to master motor tasks that, in complexity and structure, resemble ones used in primate studies of motor sequence learning. We also developed a low-cost system that can be attached to the home-cages for recording neural activity continuously in an unsupervised fashion for the iii entire months-long training process. Our system allows long-term tethering of animals and is designed for recording and processing tens of terabytes of raw data at very high speeds. We developed a novel spike-sorting algorithm that allows us to track the activity of many simultaneously recorded single neurons for weeks despite large gradual changes in their spike waveforms. This is done with minimal human input enabling, for the first time, the identification of almost every single spike from a single neuron over many weeks of training. We used these systems to record from the motor cortex of rats as they learned to perform a sequence of highly stereotyped movements. We found that neural activity in the motor cortex was exquisitely correlated with the behavior. Surprisingly, the pattern of neural activity in the motor cortex was similar before and after learning despite the fact that motor cortex is required to learn the task, but not to perform it once it has been acquired. iv Table of Contents Introduction .......................................................................................................................... 1 Chapter 1 - A fully automated high-throughput training system for rodents ........................... 4 Introduction ............................................................................................................................................ 4 System Architecture ............................................................................................................................... 6 Behavioral Training Methods .............................................................................................................. 10 System Validation ................................................................................................................................. 13 Discussion .............................................................................................................................................. 18 Attribution ............................................................................................................................................. 20 Chapter 2 - A system for continuous long-term neural recordings in rodents ........................ 22 Introduction .......................................................................................................................................... 22 System Design ....................................................................................................................................... 24 Long Term Tethering of Behaving Animals .................................................................................. 24 Signal Processing and Recording Hardware ................................................................................ 27 Data Storage and Computing Infrastructure ............................................................................... 30 Spike Sorting to Track Single Units over Time .............................................................................. 32 Spike Identification ..................................................................................................................... 35 Local clustering and de-noising ................................................................................................. 40 Automated sorting and tracking of de-noised spike waveforms ......................................... 47 Visualization and manual verification ...................................................................................... 55 System Validation ................................................................................................................................. 58 Discussion .............................................................................................................................................. 63 Attribution ............................................................................................................................................. 66 Chapter 3 - Neural representation of learned motor sequences in the motor cortex of rats .. 67 Introduction .......................................................................................................................................... 67 Results ..................................................................................................................................................... 70 Discussion .............................................................................................................................................. 81 Methods ................................................................................................................................................. 84 Attribution ............................................................................................................................................. 88 Supplementary File S1 ......................................................................................................... 89 References .......................................................................................................................... 96 v Table of Figures Figure 1.1 ........................................................................................................................................ 6 Figure 1.2 ...................................................................................................................................... 11 Figure 1.3 ...................................................................................................................................... 15 Figure 1.4 ...................................................................................................................................... 18 Figure 2.1 ...................................................................................................................................... 26 Figure 2.2 ...................................................................................................................................... 29 Figure 2.3 ...................................................................................................................................... 32 Figure 2.4 ...................................................................................................................................... 34 Figure 2.5 ...................................................................................................................................... 36 Figure 2.6 ...................................................................................................................................... 38 Figure 2.7 ...................................................................................................................................... 43 Figure 2.8 ...................................................................................................................................... 46 Figure 2.9 .....................................................................................................................................
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