Brain-inspired Computing and Engineering

02.11.2023 | Emre Neftci, John Paul Strachan

Course Plan

Wk	Instr.	Topic
1.1	JPS	Brain-inspired Computing and Engineering: introduction, history, motivation
1.2	EN	Brain Inspiration in AI and Neuromorphic Engineering
2	EN	Perceptrons, Artificial Neural Networks and Gradient Descent / PyTorch Intro
3	EN	Convnets, Recurrent Neural Networks, and State Space Models
4	JPS	History of computing, computing architectures, ANN accelerators. memory hierarchies
5	EN	Biological neurons and synapses, synaptic plasticity and learning
6	JPS	Electronic implementations of synapses, mixed analog-digital CMOS, memristive devices
7	JPS	Electronic neuron implementations, including Mott devices, and Neuristors
8	EN	Attractor neural network models and competitive learning, reservoir computing, neural engineering framework
9	EN	Stochastic neural networks, Boltzmann Machines, Equilibrium Propagation, Bayesian Neural Networks, Variational Approaches, Predictive Coding
10	JPS	Associative Memories, CAMs, Hopfield networks
11	JPS	Dynamics, Hopfield networks for optimization problems
12	EN	Continual learning, metaplasticity and memory consolidation.
?	All	Recent Developments: State-Space Models, Foundation Models, Self-Supervised Learning