The hippocampal code - Towards understanding neural representations underlying spatial navigation

Archit Gupta

EECS Department
University of California, Berkeley
Technical Report No. UCB/EECS-2023-25
May 1, 2023

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2023/EECS-2023-25.pdf

Navigation is central to the survival of species. From a systems neuroscience perspective, the Hippocampus is one of the principal regions that enables mammalian navigation. Academic and medical literature have also implicated this part of the archicortex in the long-term storage of memories. The ability to record from hundreds of neurons at sub-millisecond time-scale from the hippocampus now provides a unique window into its inner workings, allowing us to test broader computational theories about the brain. In this dissertation, we explore the representation of space in the brain from multiple perspectives. First, we study oscillatory interactions in the brain - we combine a novel opto-genetic experimental paradigm with in-vivo electrophysiology to study inter-connected regions in the rodent brain. We then use cross-frequency analyses to study how two of the most prominent oscillations in the hippocampus, theta and gamma, interact. We show that the latter, gamma oscillations, are synchronized across a large extent of anatomical tissue and that this synchrony is driven by the CA3 sub-region of the Hippocampus. We then discuss functional representations of space in the brain that constitute a cognitive map. We demonstrate tools and algorithms that were developed as a part of our projects to perform Bayesian inference of neural activity in real time, as well as to detect known oscillatory phenomenon called Sharp-Wave Ripples (SWR) that have been associated with consolidation of memories in literature. We lay out experimental work to test the role of SWR in online spatial learning. Our work has been released as a publicly available open-sourced tool called ActiveLink. We discuss technological challenges in real-time inference of neural activity and offer a machine-learning solution based on neural networks to outperform existing methods for spatial inference.

Advisor: Jose M. Carmena and David Foster


BibTeX citation:

@phdthesis{Gupta:EECS-2023-25,
    Author = {Gupta, Archit},
    Title = {The hippocampal code - Towards understanding neural representations underlying spatial navigation},
    School = {EECS Department, University of California, Berkeley},
    Year = {2023},
    Month = {May},
    URL = {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2023/EECS-2023-25.html},
    Number = {UCB/EECS-2023-25},
    Abstract = {Navigation is central to the survival of species. From a systems neuroscience perspective, the Hippocampus is one of the principal regions that enables mammalian navigation. Academic and medical literature have also implicated this part of the archicortex in the long-term storage of memories. The ability to record from hundreds of neurons at sub-millisecond time-scale from the hippocampus now provides a unique window into its inner workings, allowing us to test broader computational theories about the brain. In this dissertation, we explore the representation of space in the brain from multiple perspectives. First, we study oscillatory interactions in the brain - we combine a novel opto-genetic experimental paradigm with in-vivo electrophysiology to study inter-connected regions in the rodent brain. We then use cross-frequency analyses to study how two of the most prominent oscillations in the hippocampus, theta and gamma, interact. We show that the latter, gamma oscillations, are synchronized across a large extent of anatomical tissue and that this synchrony is driven by the CA3 sub-region of the Hippocampus. We then discuss functional representations of space in the brain that constitute a cognitive map. We demonstrate tools and algorithms that were developed as a part of our projects to perform Bayesian inference of neural activity in real time, as well as to detect known oscillatory phenomenon called Sharp-Wave Ripples (SWR) that have been associated with consolidation of memories in literature. We lay out experimental work to test the role of SWR in online spatial learning. Our work has been released as a publicly available open-sourced tool called ActiveLink. We discuss technological challenges in real-time inference of neural activity and offer a machine-learning solution based on neural networks to outperform existing methods for spatial inference.}
}

EndNote citation:

%0 Thesis
%A Gupta, Archit
%T The hippocampal code - Towards understanding neural representations underlying spatial navigation
%I EECS Department, University of California, Berkeley
%D 2023
%8 May 1
%@ UCB/EECS-2023-25
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2023/EECS-2023-25.html
%F Gupta:EECS-2023-25