In today’s Edge, we’d like to offer a short but insightful overview of a fascinating paper that forces us to rethink the analogies between biological and DNN neurons; you’ll also find the list of our recent recaps to catch up with during the holidays. 🍁 Happy Thanksgiving! 🍁 Only a few more days to become Premium with 30% OFF (forever!). Don’t miss out ;) Share it with your friends! And as always, thank you for reading and spreading the word❤️

Become Premium for only $35/YEAR

💥 What’s New in AI: How Many AI Neurons Does It Take to Simulate a Brain Neuron?

Simulating the cognitive abilities of the human brain has been the goal of artificial intelligence (AI) since its early days. Not surprisingly, the most common AI architectures are inspired by interconnected neurons in the brain. Neurons are the brain's computation building blocks; understanding how they work has puzzled neuroscientists since the days of Santiago Ramon y Cajal’s “Neuron Doctrine”. In the world of AI, deep neural networks (DNNs) are the most common class of architectures inspired by the computational capabilities of neurons.

One of the most challenging aspects of understanding the relationships between DNNs and the human brain is to model correspondence between neurons in both structures. We know a brain neuron is not equivalent to a DNN neuron but what are the differences from a computational standpoint? New research from the Hebrew University of Jerusalem provides a shocking answer. →read further to find out

Want to read something else? Find a topic that matches your interests:

• 🤓 Self-Supervised Learning Recap

• 🦾 Transformer Architectures Recap

• 📚 Reinforcement Learning Recap

• ⏳Time-Series Forecasting Recap

• 💪🏻 AutoML Recap

• 🔐 Security and Privacy Recap

• 🤓 Emerging ML Methods Recap

• 📖 Natural Language Understanding Recap

This is just a small part of what we’ve covered, and there is SO MUCH interesting ahead, as the AI&ML pace of development has speeded up tremendously.