My research goal is uncovering how hippocampal circuits produce memory and how they are disrupted in neurological diseases. My unique background in both theoretical physics and clinical medicine enables me to employ innovative yet rigorous approaches towards tackling this crucial problem. Now I'm trying to find opportunities to play piano and beach volleyball in Tokyo.
I'm a third year undergraduate student at UC Berkeley. At the Redwood Center for Theoretical Neuroscience at UC Berkeley, I pursue research at the intersection of neuroscience, computer science and mathematics. My interests include understanding memory through a biological and computational perspective.
When information enters the hippocampus, it is split into two pathways. Experiments suggest that they encode information with different amounts of sparsity and decorrelation. Yet, the computational capabilities granted by these two pathways are not clear.
We demonstrate how complementary encoding pathways can enable the hippocampus to perform unsupervised categorization while maintaining its ability to recall individual examples. The circuit can alternate on-the-fly between these two operating modes, generalization and differentiation, by adjusting its level of inhibition. Thus, representing information at different resolutions, which is considered a key feature of memory, can be implemented by neural circuits in the hippocampus.
To operate effectively, the enormous number of neurons in brain circuits must coordinate their activity. Detecting signatures of coordination in large, complex sets of neural data may help us understand neural computation. One such signature is topological structure, such as loops and voids, formed by the data in high-dimensional phase space.
Persistent cohomology is a powerful technique for discovering topological structure in data. Strategies for its use in neuroscience are still undergoing development. We explore the application of persistent cohomology to the brain’s spatial representation system. Our results suggest guidelines for applying persistent cohomology to experimental neural recordings.
|link||save_alt||Kang L, Xu B & Morozov D. Evaluating state space discovery by persistent cohomology in the spatial representation system. Front Comput Neurosci 15, 616748 (2021).|
The entorhinal cortex (EC) contains grid cells, each of which only fires when we approach certain locations that form a triangular lattice in space. There is experimental evidence that the grid cell network can be modeled as a continuous attractor, in which neural activity evolves through a set of attractor states that represent different positions in the 2D environment.
However, existing attractor models did not capture several key phenomena exhibited by the grid system. Grid cells belong to modules, which suggests that spatial information is discretized in memories, and grid cells can fire in rapid sequences that may be related to memory consolidation or planning. Through simulations, we demonstrated how these phenomena arise in continuous attractors with the addition of experimentally observed or biologically plausible features of EC. Our results suggest mechanisms through which the hippocampal region performs memory-related computations.
|link||save_alt||Kang L & Balasubramanian V. A geometric attractor mechanism for self-organization of entorhinal grid modules. eLife 8, e46687 (2019).|
|link||save_alt||Kang L & DeWeese MR. Replay as wavefronts and theta sequences as bump oscillations in a grid cell attractor network. eLife 8, e46351 (2019).|
09 Jun 2021
Check out our new Instagram account! It highlights our engagement with the scientific and public communities that make our research possible and hopefully benefit from our work. It also gives us a way to support similar efforts by others around the world.
09 Apr 2021
Louis joins the Graduate School of Informatics at Kyoto University as an Adjunct Associate Professor. He is delighted to be part of this esteemed institution and looks forward to interacting with students, staff, and faculty.
08 Apr 2021
Our paper “Evaluating state space discovery by persistent cohomology in the spatial representation system” is now published in Frontiers in Computational Neuroscience! We have improved upon our preprint with more rigorous analyses that have produced more impactful conclusions.
11 Feb 2021
If you missed him at the SfN Global Connectome, Raymond will be presenting his work “Multiple bumps enhance robustness to noise in continuous attractor networks” at Cosyne. Visit him virtually at 19:00 US Pacific Time on 26 February 2021.
11 Jan 2021
Are you interested in computational neuroscience, machine learning, and living in Tokyo? The Neural Circuits and Computations Unit is looking to hire a postdoc or research scientist! View our posting on the RIKEN Center for Brain Science website for more information.
08 Dec 2020
Raymond will be presenting his work “Multiple bumps enhance robustness to noise in continuous attractor networks” at the Society for Neuroscience Global Connectome. Visit him virtually at 11:00 am US Pacific Time on 11 January 2021.
01 Nov 2020
Raymond, an undergraduate from University of California, Berkeley, officially joins the group as an intern. He has been working with Louis since March. He is studying how noise in continuous attractor networks produce errors in path integration and methods of suppressing these errors. Welcome!
08 Oct 2020
Read our preprint “State space discovery in spatial representation circuits with persistent cohomology” to bioRxiv. This project explores how topological data analysis can be applied to neuroscience data. It is a collaboration with computational topologists Dmitriy Morozov and Boyan Xu at Lawrence Berkeley National Laboratory.