Brain Computer Interfacing via spiking neuromorphic networks
Brain Computer Interfacing via spiking neuromorphic networks, Spiking Neuromorphic Computing via PyCARL & Wyrm (Python): Understanding Brain Computer Interfacing (BCI) & Tiny ML.
Despite being quite effective in a variety of tasks across industries, deep learning is constantly evolving, proposing new neural network (NN) architectures such as the Spiking Neural Network (SNN). This exciting course introduces you to the next generation of Machine Learning. You would be able to learn about the fundamentals of Spiking Neural Networks and Brain-Computer Interfacing (BCI).
This course has the rigour enough to enable you not only to understand BCI but its implementation in spiking neural networks and to apply these concepts to Brain Healthcare (IT) even on edge machines using Tiny ML.
TinyML is a field of study in Machine Learning and Embedded Systems that explores the types of models you can run on small, low-powered devices like microcontrollers. It enables low-latency, low power and low bandwidth model inference at edge devices. While a standard consumer CPUs consume between 65 watts and 85 watts and standard consumer GPU consumes anywhere between 200 watts to 500 watts, a typical microcontroller consumes power in the order of milliwatts or microwatts.
That is around a thousand times less power consumption.
The course contents includes;
1. Introduction to Machine Learning, Deep Learning, and Artificial Intelligence.
2. How Quantum Computing is fuelling AI Healthcare Systems including BCIs.
3. Introduction to Recurrent Neural Networks.
4. Introduction to LSTMs.
5. Introduction to Brain-Computer Interfaces.
6. How BCI is used for neuro- rehabilitation.
7. Brain-Computer Interfaces for Stress and Mood Regulation.
8. Brain-Computer Interfaces for Motor Imagery & EEG Signals.
9. Brain Implants using Brain-Computer Interfacing.
10. BCI for Medical Imaging.
11. Introduction to “Brain- on- a Chip.
12. Neuromorphic Computing for Brain Computer Interfacing.
13. Introduction to Tiny ML.
14. Tiny ML for Real Time Applications