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I am a neuroscientist in the Center for Cognition and Sociality at the Institute for Basic Science, South Korea.

Research interests

I am interested in how physical building blocks of neural systems work together, broadly speaking. The main focus of my research is uncovering how biophysical mechanisms in neurons and neural networks determine their functions. A neural system typically has a vast repertoire of those interacting with each other. Such complexity somehow enables the whole system to perform information processing in specific yet adaptive ways. This phenomenon raises many questions, such as how we can characterize computations by neural systems, how we can uncover what cellular mechanisms are responsible, etc.

To address those issues, I extensively use computational modeling, a powerful tool that allows us to explore systems often far beyond the regime accessible to available experiments, thereby efficiently generating experimentally testable hypotheses. While building those models based on experimental data is rarely a simple task, ideas from statistical learning theory have proven useful for overcoming difficulties in model building. I have endeavored to bring together the approaches to this whole range of problems and contribute to our understanding of the physical basis of neural information processing.

To find out more, please check out the full list of publications.

Computer models and software projects

I have developed the following computer models and data analysis packages:

  1. Source data and codes for "Multidimensional cerebellar computations for flexible kinematic control of movements"
  2. HetFFN is a set of codes for our paper "Lamina-specific neuronal properties promote robust, stable signal propagation in feedforward networks", NeurIPS 2020.
  3. Pycabnn is a software tool to efficiently construct an anatomical basis of a physiologically detailed neural network model (reference).
  4. Synchronized oscillator model of the choroid plexus contains my simulation codes for the twisted oscillators with ranged interactions (reference).
  5. Model of the cerebellar granular network is a physiologically realistic large-scale model of the neural network in the cerebellar granular layer (reference).
  6. Correlation contains the simulation and analysis code used for my study about how single neuron properties affect correlation-based coding in a neural population (reference).
  7. ImagingAnalysis contains my variant of multi-scale spectral clustering (reference).
  8. CSPRC is an implementation of the Compressive Sensing-based method to compute a phase responsive curve from small-sized data (see reference).

Course materials

I have been teaching how to build computational models of neural systems. Here are some materials and codes for the classes

  1. A310 Computational Neuroscience 2022/2023