Methods for neural decoding using machine learning, deep learning, and quantum-inspired algorithms

Note: The format of this event has changed from hybrid to Zoom only. However, you will still be able to watch it on the screen in Room #359 of the Main Research Building.

(This is a joint seminar with iTHEMS Biology group.)
Recent advances in machine learning have enabled the extraction of intrinsic information from neural activities, a field known as neural decoding. In this presentation, I will introduce several machine learning methods recently developed for neural decoding analysis: 1) a method for visualizing subjective images in the human mind based on brain activity [1], 2) a supervised algorithm designed for predicting discrete ordinal variables [2], and 3) a fast classical algorithm algorithm inspired by quantum computation for approximating principal component analysis (PCA) and canonical correlation analysis (CCA), potentially allowing for the analysis of vast-dimensional neural data [3]. Following these presentations, I am eager to engage in discussions with participants at the RIKEN Quantum Seminar regarding potential collaborations.