Neural Circuits and Computations Unit

Computational neuroscience research group at RIKEN Center for Brain Science

When an experience in our daily life gets stored as a memory, something must change in our brain—what is it?

We study how neural circuits in the hippocampal region enable us to remember events along with where they occurred. To do so, we use a variety of theoretical techniques, including neural network simulations and mathematical analysis. We aim to uncover how these circuits are disrupted in neurological diseases such as dementia and epilepsy.
Louis Kang
Unit Leader

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.

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Sebastian Eydam
Postdoctoral Researcher

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.

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Ismaeel Ramzan
Postdoctoral Researcher

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!

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Ulker Isayeva
PhD Student Intern

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.

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Memory hierarchy from complementary encoding pathways

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.

Noise resilience in continuous attractor networks

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).

Topological data analysis for neuroscience

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).

Grid cell organization and dynamics

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).