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Researchers believe that the connections between cortical columns store knowledge about how concepts are related to each other, while the neurons within these columns perform computations in order to draw inferences and make sense of information.
31:57 - 34:02 (02:05)
Summary
Researchers believe that the connections between cortical columns store knowledge about how concepts are related to each other, while the neurons within these columns perform computations in order to draw inferences and make sense of information. The encoding of relationships between random variables is thought to be represented by the connections between cortical columns.
ChapterThe Implementation of Knowledge in the Cortical Columns
Episode#115 – Dileep George: Brain-Inspired AI
PodcastLex Fridman Podcast
This transcript discusses the differences between artificial and cortical neural networks, and how the latter is much more complex and structured within each layer.
34:02 - 37:03 (03:00)
Summary
This transcript discusses the differences between artificial and cortical neural networks, and how the latter is much more complex and structured within each layer. It also touches on feature detection and pooling as one level of an artificial neural network.
ChapterThe Implementation of Knowledge in the Cortical Columns
Episode#115 – Dileep George: Brain-Inspired AI
PodcastLex Fridman Podcast
This podcast discusses the use of a fully functional model for the microcircuits of the visual cortex, known as the recursive cortical network model, to address various questions, including breaking captures.
37:03 - 38:47 (01:44)
Summary
This podcast discusses the use of a fully functional model for the microcircuits of the visual cortex, known as the recursive cortical network model, to address various questions, including breaking captures.