Uploaded December 2024 | Updated September 2026, 7 hours ago
Rajesh Rao shares his updated theory on how the cortex could implement active predictive coding
Show notes: braininspired.co/podcast/201
Patreon (full episodes and Discord community: patreon.com/braininspired
Apple podcasts: itunes.apple.com/us/podcast/brain-inspired/id1428880766?mt=2
Spotify: open.spotify.com/show/2UZj8c8Ap5oc2gh2rJxLLe
The Transmitter is an online publication that aims to deliver useful information, insights and tools to build bridges across neuroscience and advance research. Visit thetransmitter.org to explore the latest neuroscience news and perspectives, written by journalists and scientists.
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Today I'm in conversation with Rajesh Rao, a distinguished professor of computer science and engineering at the University of Washington, where he also co-directs the Center for Neurotechnology. Back in 1999, Raj and Dana Ballard published what became quite a famous paper, which proposed how predictive coding might be implemented in brains. What is predictive coding, you may be wondering? It's roughly the idea that your brain is constantly predicting incoming sensory signals, and it generates that prediction as a top-down signal that meets the bottom-up sensory signals. Then the brain computes a difference between the prediction and the actual sensory input, and that difference is sent back up to the "top" where the brain then updates its internal model to make better future predictions. So that was 25 years ago, and it was focused on how the brain handles sensory information. But Raj just recently published an update to the predictive coding framework, one that incorporates actions and perception, suggests how it might be implemented in the cortex - specifically which cortical layers do what - something he calls "Active predictive coding." So we discuss that new proposal, we also talk about his engineering work on brain-computer interface technologies, like BrainNet, which basically connects two brains together, and like neural co-processors, which use an artificial neural network as a prosthetic that can do things like enhance memories, optimize learning, and help restore brain function after strokes, for example. Finally, we discuss Raj's interest and work on deciphering an ancient Indian text, the mysterious Indus script.
0:00 - Intro
7:40 - Predictive coding origins
16:14 - Early appreciation of recurrence
17:08 - Prediction as a general theory of the brain
18:38 - Rao and Ballard 1999
26:32 - Prediction as a general theory of the brain
33:24 - Perception vs action
33:28 - Active predictive coding
45:04 - Evolving to augment our brains
53:03 - BrainNet
57:12 - Neural co-processors
1:11:19 - Decoding the Indus Script
1:20:18 - Transformer models relation to active predictive coding
Rajesh Rao shares his updated theory on how the cortex could implement active predictive coding
Show notes: braininspired.co/podcast/201
Patreon (full episodes and Discord community: patreon.com/braininspired
Apple podcasts: itunes.apple.com/us/podcast/brain-inspired/id1428880766?mt=2
Spotify: open.spotify.com/show/2UZj8c8Ap5oc2gh2rJxLLe
The Transmitter is an online publication that aims to deliver useful information, insights and tools to build bridges across neuroscience and advance research. Visit thetransmitter.org to explore the latest neuroscience news and perspectives, written by journalists and scientists.
Read more about our partnership: thetransmitter.org/partners
Sign up for the “Brain Inspired” email alerts to be notified every time a new “Brain Inspired” episode is released: thetransmitter.org/newsletters
To explore more neuroscience news and perspectives, visit thetransmitter.org.
Today I'm in conversation with Rajesh Rao, a distinguished professor of computer science and engineering at the University of Washington, where he also co-directs the Center for Neurotechnology. Back in 1999, Raj and Dana Ballard published what became quite a famous paper, which proposed how predictive coding might be implemented in brains. What is predictive coding, you may be wondering? It's roughly the idea that your brain is constantly predicting incoming sensory signals, and it generates that prediction as a top-down signal that meets the bottom-up sensory signals. Then the brain computes a difference between the prediction and the actual sensory input, and that difference is sent back up to the "top" where the brain then updates its internal model to make better future predictions. So that was 25 years ago, and it was focused on how the brain handles sensory information. But Raj just recently published an update to the predictive coding framework, one that incorporates actions and perception, suggests how it might be implemented in the cortex - specifically which cortical layers do what - something he calls "Active predictive coding." So we discuss that new proposal, we also talk about his engineering work on brain-computer interface technologies, like BrainNet, which basically connects two brains together, and like neural co-processors, which use an artificial neural network as a prosthetic that can do things like enhance memories, optimize learning, and help restore brain function after strokes, for example. Finally, we discuss Raj's interest and work on deciphering an ancient Indian text, the mysterious Indus script.
0:00 - Intro
7:40 - Predictive coding origins
16:14 - Early appreciation of recurrence
17:08 - Prediction as a general theory of the brain
18:38 - Rao and Ballard 1999
26:32 - Prediction as a general theory of the brain
33:24 - Perception vs action
33:28 - Active predictive coding
45:04 - Evolving to augment our brains
53:03 - BrainNet
57:12 - Neural co-processors
1:11:19 - Decoding the Indus Script
1:20:18 - Transformer models relation to active predictive coding
