IEE/CSE 598: Lecture 7G (2020-04-15) - Intro. to Spiking Neural Networks and Neuromorphic Computing

In this lecture, we continue discussing associative/Hebbian learning in neural networks – starting with the inspiration from real neurons and leading to the approximation of this learning mechanism in Artificial Neural Networks (ANN). We discuss a simple pattern-recognition example that is well suited for spike-timing-dependent plasticity (STDP). We then setup the foundations for discussing true spiking neural networks (SNN), introducing some of the existing platforms (IBM TrueNorth, Intel Loihi, and SpiNNaker). We will start the next lecture with a discussion of memristor-based crossbar arrays that implement STDP, as first described in Boyn et al. (2017) in Nature Communications. Whiteboard notes for this lecture can be found at: https://www.dropbox.com/s/g3e73roz5v5... This lecture was recorded by Theodore Pavlic as part of IEE/CSE 598 (Bio-Inspired AI and Optimization) at Arizona State University.