Chris featured on the latest episode of 'Machine Learning Street Talk'. Dive into an episode where Chris talks about Machine Learning, modelling Brain, spiking Neural Networks and more.
Here is the preview of the episode on youtube and the full spotify link to the podcast.
'Machine Learning Street Talk' is a science podcast by Dr.Tim Scarfe, that engages in dynamic discussion on research and current affairs in the field of AI.
This is our latest ‘overview’ paper. This one is targeted at the neuromorphics community, but also can be seen as a recent update to the latest NEF theoretical advances.
It covers how any linear synapse can be used in a network, and the ‘delay network’, a recent advance that not only explains time cells in the brain, but is setting state-of-the-art records in a wide variety of machine learning contexts.
This is coming out in the 2021 Springer Handbook for Neuromorphics.
We had an amazing talk by Dr. Guru Guruganesh from Google Research team on their recent NeurIPS paper: "Big Bird: Transformers for Longer Sequences".
It was a great opportunity to learn about the novel optimization strategies and techniques they are exploring, in order to train and scale up transformer models making them bigger, better and more efficient.
Unfortunately the Nengo Summer School had to be cancelled this year due to COVID-19, but that doesn't mean you can't get a taste of what you'll learn at Brain Camp!
Chris and Terry have been hard at work recording a set of lectures covering the topics typically presented in the first few days of the summer school.
The videos are freely available to view here.
While no substitute for the hand-on experience of the actual summer school, these lectures and tutorials give an excellent introduction to the Neural Engineering Framework and how to develop spiking neural network models using Nengo.
Congratulations to Pete and Nat on presenting their work at SfN this year!
Both gave excellent poster presentations!
Pete explored the effects of different drugs on his rodent model of fear conditioning. He developed a spiking neural model of the different nuclei of the amygdala with enough detail to allow biophysical manipulations. "A spiking neuron model of pharmacologically-biased fear conditioning in the amygdala"
Nat developed a spiking neural model of adaptation in motor control. This includes an extension of the REACH model with a multimodal Kalman filter and replicates data in both human and non-human primates. "Spiking neuron model of motor control with adaptation to visuomotor rotation"