BI 009 Blake Richards: Deep Learning in the Brain

Brain Inspired

00:00 / 01:11:00

Mentioned in the show

Follow Blake on twitter: @tyrell_turing
Blake’s Learning in Neural Circuits (LiNC) Laboratory.
He’s a Fellow with the Learning in Machines and Brains Program of the Canadian Institute for Advanced Research (CIFAR).
The paper we discuss:
- Towards Deep Learning With Segregated Dendrites.
Code to run the model on Github.
If you’d rather watch a talk, here’s the same topic in a great talk by Blake.
The idea of approaching neuroscience from the perspective there are general principles of computation applicable to both brains and AI:
- Geoffrey Hinton.
- Cybernetics.
- McCullough and Pitts artificial neuron: Their original paper and a nice tutorial.
- Frank Rosenblaut.
- Demis Hassabis, who founded Deepmind, wrote a great review of how AI and neuroscience can work together.
Donald Hebb of the famed Hebbian Learning in his famous book The Organization of Behavior: A Neuropsychological Theory.
Konrad Kording’s 2001 paper articulating the same idea we discuss: Supervised and Unsupervised Learning with Two Sites of Synaptic Integration.
MNIST dataset of handwritten digits – used to train and test a lot of machine learning networks.
Eliminative Materialism, the idea our common sense conception of the mind is false.