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Research#Neuroscience📝 BlogAnalyzed: Dec 29, 2025 08:07

Sensory Prediction Error Signals in the Neocortex with Blake Richards - #331

Published:Dec 24, 2019 18:55
1 min read
Practical AI

Analysis

This article summarizes a podcast episode from Practical AI featuring Blake Richards, an Assistant Professor at McGill University and a Core Faculty Member at Mila. The episode focuses on Richards' research presented at the Neuro-AI Workshop, specifically his work on "Sensory Prediction Error Signals in the Neocortex." The conversation likely delves into topics such as predictive coding, hierarchical inference, and Richards' recent work on memory systems for reinforcement learning. The article highlights the use of two-photon calcium imaging in the studies discussed, suggesting a focus on the neural mechanisms underlying sensory processing and learning within the neocortex.
Reference

The article doesn't contain a direct quote, but it discusses Richards' research on "Sensory Prediction Error Signals in the Neocortex."

Research#AI Theory📝 BlogAnalyzed: Dec 29, 2025 17:47

Jeff Hawkins: Thousand Brains Theory of Intelligence

Published:Jul 1, 2019 15:25
1 min read
Lex Fridman Podcast

Analysis

This article summarizes Jeff Hawkins' work, particularly his Thousand Brains Theory of Intelligence, as discussed on the Lex Fridman Podcast. It highlights Hawkins' background as the founder of the Redwood Center for Theoretical Neuroscience and Numenta, and his focus on reverse-engineering the neocortex to inform AI development. The article mentions key concepts like Hierarchical Temporal Memory (HTM) and provides links to the podcast and Hawkins' book, 'On Intelligence'. The focus is on Hawkins' contributions to brain-inspired AI architectures.
Reference

These ideas include Hierarchical Temporal Memory (HTM) from 2004 and The Thousand Brains Theory of Intelligence from 2017.

Research#ai📝 BlogAnalyzed: Dec 29, 2025 08:35

The Biological Path Towards Strong AI - Matthew Taylor - TWiML Talk #71

Published:Nov 22, 2017 22:43
1 min read
Practical AI

Analysis

This article discusses a podcast episode featuring Matthew Taylor, Open Source Manager at Numenta, focusing on the biological approach to achieving Strong AI. The conversation centers around Hierarchical Temporal Memory (HTM), a neocortical theory developed by Numenta, inspired by the human neocortex. The discussion covers the basics of HTM, its biological underpinnings, and its distinctions from conventional neural network models, including deep learning. The article highlights the importance of understanding the neocortex and reverse-engineering its functionality to advance AI development. It also references a previous interview with Francisco Weber of Cortical.io, indicating a broader interest in related topics.
Reference

In this episode, I speak with Matthew Taylor, Open Source Manager at Numenta. You might remember hearing a bit about Numenta from an interview I did with Francisco Weber of Cortical.io, for TWiML Talk #10, a show which remains the most popular show on the podcast.