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 am a nonlinear dynamics researcher with a focus on coupled oscillators and excitable systems. Currently, I am studying the dynamics of seizures in neuronal models to expand our knowledge about the dynamics of epileptic states. My research builds upon a combination of mathematical modeling, numerical simulations, and theoretical analysis.
My PhD research focused on using machine learning to make predictions of quantum forces in chemical systems. My current research involves looking at spiking neural networks and how they can potentially help us understand epilepsy better. I enjoy a wide range of things and coming up with creative solutions to problems. I usually have multiple projects on the go, and look forward to collaborations with others here at RIKEN!
I am a PhD student in Neuroscience at the University of Cagliari, where my main research focuses on studying clinical and genetic determinants of psychiatric disorders, particularly schizophrenia. At the Neural Circuits and Computations Unit within RIKEN CBS, I am working on a project that studies network computations and seizure susceptibility. My research interests include psychiatric genetics, computational psychiatry, schizophrenia spectrum disorders, computational neuroscience, and machine learning.
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.
Our brains maintain internal representations of values related to the external world, which allows us to, for example, find our way to the door if the lights go off. Continuous attractor networks are one class of neural circuit used to accomplish this. They contain localized regions of activity, called attractor bumps, whose positions can encode the value of a continuous variable. However, the brain is teeming with biological noise that perturbs the positions of the bumps and compromises the accuracy of the network.
We uncover a new means through which continuous attractor networks can enhance their robustness to noise. They can distribute their activity across multiple attractor bumps instead of concentrating it within a single bump. While such configurations have been considered by researchers, the connection between bump number and noise resilience had not been appreciated. This observation contributes to our fundamental knowledge of attractor networks, and it may help to explain why the mammalian grid cell network appears to have evolved a multi-bump configuration.
| link | save_alt | Wang R & Kang L. Multiple bumps can enhance robustness to noise in continuous attractor networks. PLOS Comput Biol 18, e1010547 (2022). |
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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). |
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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). |
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| 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). |
11 Nov 2022
Congraulations to Raymond on publishing his first paper on noise resilience and bump number in continuous attractor networks. Ulya has also published her PhD work on the role of BDNF in the pathogenesis of schizophrenia. These Kang Group interns are off doing great things.
15 Sep 2022
Ismaeel arrives in Japan from the UK, where he grew up and obtained his PhD in computational chemistry at the University of Manchester. His experience in machine learning will be a great asset to the group as he looks to ply his trade in the field of neuroscience. Welcome!
15 Aug 2022
Raymond is off to do great things at MIT as a PhD student in the Department of Brain and Cognitive Sciences. He finished his undergraduate program at UC Berkeley with an outstanding academic record and a paper accepted for publication in PLOS Computational Biology. We look forward to following his progress and reuniting at conferences.
04 Aug 2022
The Unit goes to Sebastian's hometown, Berlin! We will attend the Bernstein Conference during 13-16 September. Sebastian will present on neural oscillators, and Louis will share his work on memory encodings.
16 Jun 2022
Ulker Isayeva, a PhD student at the University of Cagliari, has been accepted to IBRO-RIKEN CBS Summer Program 2022. We look forward to her internship at the Unit over the next few months, during which she will train RNNs in order to study seizures.
04 Mar 2022
Are you interested in applying machine learning to study seizures and living in Tokyo? We are looking to hire a second talented postdoc or research scientist! View our posting on the RIKEN Center for Brain Science website for more information.
25 Feb 2022
Our bioRxiv preprint “Multiple bumps can enhance robustness to noise in continuous attractor networks” seeks to enhance our fundamental understanding of continuous attractor networks. Simulation results were obtained through the tenacious efforts of Raymond Wang.
07 Feb 2022
Raymond and Louis are excited to present “Multiple bumps can enhance robustness to noise in continuous attractor networks” and “Multiscale encodings of memories in hippocampal and artificial networks” at Cosyne 2022. These projects have been updated with many new results.
