For 11/8:
Article Source: Brain cell differences could be key to learning in humans and AI (https://www.sciencedaily.com/releases/2021/10/211006112626.htm )
Article Summary:
A recent study shows that by diversifying the electrical properties of brain cells, learning becomes more efficient. The researchers who conducted the study wanted to see if having a similar diversity in the neural networks of computers could improve artificial intelligence, and saw that the models with a similar variability to human brains performed the best.
Journal Source: Neural heterogeneity promotes robust learning (https://www.nature.com/articles/s41467-021-26022-3 )
Journal Study:
Nicolas Perez-Nieves, and two other researchers study how neural heterogeneity helped improve the performance spiked neural networks. This specifically occurred due to the heterogeneous network having a more stable training trajectory and better test accuracy. The networks had to make judgements based on complex, but very real decisions humans make, such as recognizing digits from different handwriting, and identifying gestures or commands.
Comparisons:
- Both the article and the journal discuss the importance of time constants and the diversity of cells (simulated or otherwise) for heterogeneity to thrive.
- Other than the abbreviation of the word AI, the article did not try to use any colloquial terms to simplify concepts, and even quotes the researchers involved in the study.
- Only the full research article includes any graphs to represent data
