01.117 Brain-Inspired Computing and its Applications (Term 8)

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Course Description

This course is offered to both undergraduates and graduates, and focuses on understanding the fundamental principles of brain and cognitive functions used for edge computing, artificial intelligence (AI) tasks and bioinformatics. This includes advanced theoretical models and practical aspect of major processes, such as description of neurons, the response of neurons to sensory stimuli, neuronal networks, state-of-the-art brain-inspired computer architectures and chips. Other topics covered include: statistical inference, decision making and so on. Speakers will be invited for advanced topics.

Pre-requisite

Learning Objective

  1. Understand the basic biophysics of neurons and networks and other principles underlying brain and cognitive functions
  2. Use mathematical techniques (e.g., equivalent circuit model, differential equations, etc) to analyze simple models of neurons and networks
  3. Use mathematical techniques (i.e., equivalent circuit model, differential equations, etc) to do data analysis of behavioral and neuronal data
  4. Become proficient at using numerical methods to implement these techniques (Matlab or similar software)

Measurable Outcome

  1. Translate a simple model of neurons and neural circuits into a mathematical model
  2. Simulate simple model of neurons and networks using Matlab or similar software
  3. Analyze neuronal data or model output of neurons and networks using Matlab or similar software.

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