November 04, 2021

Sleep, Dreaming, Deep Neural Networks, Machine Learning & Artificial Intelligence, Overfitted Brain Hypothesis, Evolution of Fiction & Art | Erik Hoel | 43

1 hour 27 minutes

Send us a text

Nick talks to neuroscientist and writer Dr. Erik Hoel. Erik is a professor of biology at Tufts University. He received his PhD in neuroscience from the University of Wisconsin, where he studied under the sleep and consciousness researcher, Giulio Tononi. He did postdoctoral work at Columbia University, where he used information theory and other analytical tools to explore the biological basis of consciousness. He has come up with the so-called Overfitted Brain Hypothesis of dreaming, which explains the potential adaptive function of dreams by drawing analogies to techniques used to train Deep Neural Networks in the world of machine learning.

Erik and Nick discuss the biology and phenomenology of dreams and sleep generally, including some of the various theories for why we sleep. They also discuss Deep Learning (on a very basic level) and Erik described the Overfitted Brain Hypothesis of dreaming. They also discuss fiction and the arts, including Erik’s new novel and the potential evolutionary reasons for why humans create and consume fiction, as well as some technology-driven developments that are reshaping how we create and consume written work online.

*Not medical advice.

Support the show

Affiliates:

Seed Oil Scout: Find restaurants with seed oil-free options, scan food products to see what they’re hiding, with this easy-to-use mobile app.
KetoCitra—Ketone body BHB + electrolytes formulated for kidney health. Use code MIND20 for 20% off any subscription (cancel anytime)
Lumen device to optimize your metabolism for weight loss or athletic performance. Code MIND for 10% off
SiPhox Health—Affordable at-home blood testing. Key health markers, visualized & explained. Code TRIKOMES for a 20% discount.

For all the ways you can support my efforts

...more

View all episodes

By Nick Jikomes

4.8

7878 ratings