NICE 2021: From Brains to Silicon – Applying Lessons from Neuroscience to Machine Learning
Jeff Hawkins and Subutai Ahmad presents a keynote “From Brains to Silicon — Applying lessons from neuroscience to machine learning.” This keynote was presented on March 17th, 2021 at the virtual NICE workshop.
Jeff Hawkins: How The Brain Uses Reference Frames to Model the World, Why AI Needs to do the Same | NAISys 2020
Jeff Hawkins presents a talk on “How the Brain Uses Reference Frames to Model the World, Why AI Needs to do the Same.” This presentation was presented on November 10, 2020 at the virtual From Neuroscience to Artificially Intelligent Systems (NAISys) conference. In this talk, he gives an overview of The Thousand Brains Theory and discusses how machine intelligence can benefit from working on the same principles as the neocortex.
Brains@Bay – Alternatives to Backpropagation in Neural Networks
In this meetup, we discuss alternatives to backpropagation in neural networks. We invited Prof. Rafal Bogacz (Oxford), Sindy Löwe (Amsterdam) and Jack Kendall (RAIN Neuromorphics) to present their views and latest research on the topic from a neuroscience and machine learning perspective.
Technology Validation: Sparsity Enables 50x Performance Acceleration Deep Learning Networks
This video walks through the results of Numenta’s technology demonstration that shows 50x performance improvements on inference tasks in deep learning networks without any loss in accuracy.
Brains@Bay – The Role of Active Dendrites in Learning
In this meetup, we focus on the role of active dendrites in learning from a neuroscience and a computational perspective. We invited Matthew Larkum (Larkum Lab), Ilenna Jones (Kording Lab) and Blake Richards (Linc Lab) to present their views on the role of active dendrites in machine learning.
Brains@Bay – Lateral Connections in the Neocortex
In this special edition we focus on the function of lateral connections (connections between neurons within a level). Long-range lateral connections are ubiquitous in the neocortex and cannot be explained by pure feedforward models. We have invited researchers from the Allen Institute for Brain Science to discuss their recently published paper on modeling these connections.