A brain-machine interface for investigating neural representations of navigation in a virtual environment

Simultaneously recording behaviour and neural activity can only take researchers so far in understanding the function of different regions of the brain, and the neurons therein. Novel methods are required for probing further beyond standard correlational and knockout analysis. The activity of one brain region might correlate with task relevant variables, and silencing this region can show its necessity for task success. However, can the activity of this region independently drive successful completion of the task? We propose that Brain-Machine Interfaces (BMIs) are a uniquely poised technology capable of answering such questions. In this thesis, we investigate the neural representations of navigation in the Posterior Parietal Cortex (PPC) of mice during a virtual navigation task. We recorded neural activity during completion of a T-maze task using 2-photon calcium imaging, and trained decoders to decode task relevant variables from recorded neural activity. We then closed the loop, and allowed mice to navigate through the virtual maze directly using these brain signals, using the output of our decoders. These experiments showed that mice can successfully navigate through the virtual maze using this BMI, showing that these neural representations in the PPC are sufficient for driving behaviour. Through further investigation of the behaviour and neural activity during BMI use, we also showed that the representations being used for closed-loop control were related to high-level navigational signals, as opposed to low-level motor commands. These results show that the PPC, a region of the brain at the interface between sensory and motor brain regions, is capable of driving navigational behaviour even when bypassing standard downstream neural pathways. We propose that our methods could be applied to other brain regions and experiments to enable researchers to investigate other challenging questions about encoding and function in the brain.

Name dc:type.qualificationname

Doctor of Philosophy (PhD)

Level dc:type.qualificationlevel

Doctoral

Grantor dc:publisher.institution

University of Cambridge

Year dc:date.issued

2023

Author dc:creator

• Sorrell, Ethan

Advisor dc:contributor.advisor

• O'Leary, Timothy

• BMI
• Brain-machine interface
• Closed-loop control
• Decoding
• Neural engineering
• Neural representation
• Posterior parietal cortex
• PPC
• Virtual navigation

Licence

• https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/d1b6d79d-6789-44a5-b0d6-05ff9edf09bd/download
• https://www.rioxx.net/licenses/all-rights-reserved/

Language dc:language

eng

DOI dc:identifier.doi

https://doi.org/10.17863/CAM.105780

OAI identifier oai:identifier

oai:www.repository.cam.ac.uk:1810/363901