News

20 May 2025

[NEW] Our work on the enhancement in compound-protein interaction prediction models by utilizing data imbalance was accepted to publish in Advanced Intelligent Systems.

8 May 2025

[NEW] Our work on the impact of modulations of release probability distribution on attractor dynamics was accepted to publish in Neurocomputing.

3 May 2024

Our work on a noval pretraining method for molecular graphs is now published in ACS Omega. For detail, please check the Publications page.

2 Nov 2023

Our work on an implication of short-term post-synaptic plasticity (STPP) in continuous attractors is now published in Frontiers in Computational Neuroscience. For detail, please check the Publications page.

2 Jun 2023

Our work on an implication of synaptic competition is now published in PNAS Nexus. For detail, please check the Publications page.

1 Mar 2022

The Lab get started. Please check the Opportunities page for more recruitment information.

About the Lab

This computational lab is under the Department of Neuroscience of the City University of Hong Kong. It was founded in February 2022.

This lab focuses on computational issues of Neuroscience. Those issues include:

  1. Computational Models for Basic Brain Science;
  2. Implementing Neural Phenomena in Machine Learning Algorithms; and
  3. Analyzing Neural Data by Data Science Algorithms.
Mathematical tools and numerical methods will be used to address the above issues. The ultimate goals of this lab are summarized in the following diagram.

Implementation of neural phenomena in machine learning algorithms will be helpful to understand the functional meaning of those phenomena. On the other hand, hopefully, the implementation will inspire innovations in machine learning. Also, we aim to see how advances in machine learning can help discoveries in Neuroscience.