Uploaded June 2022 | Updated September 2026, 3 hours ago
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Music by: The New Year: thenewyear.net
Show notes: braininspired.co/podcast/138
Matthew Larkum runs his lab at Humboldt University of Berlin, where his group studies how dendrites contribute to computations within and across layers of the neocortex. Since the late 1990s, Matthew has continued to uncover key properties of the way pyramidal neurons stretch across layers of the cortex, their dendrites receiving inputs from those different layers - and thus different brain areas. For example, layer 5 pyramidal neurons have a set of basal dendrites near the cell body that receives feedforward-like input, and a set of apical dendrites all the way up in layer 1 that receives feedback--like input. Depending on which set of dendrites is receiving input, or neither or both, the neuron's output functions in different modes- silent, regular spiking, or burst spiking. Matthew realized the different sets of dendritic inputs could signal different operations, often pairing feedforward sensory--like signals and feedback context-like signals. His research has shown this kind of coincidence detection is important for cognitive functions like perception, memory, learning, and even wakefulness. We discuss many of his ideas and research findings, why dendrites have long been neglected in favor of neuron cell bodies, the possibility of learning about computations by studying implementation-level phenomena, and much more.
0:00 - Intro
5:31 - Background: Dendrites
23:20 - Cortical neuron bodies vs. branches
25:47 - Theories of cortex
30:49 - Feedforward and feedback hierarchy
37:40 - Dendritic integration hypothesis
44:32 - DIT vs. other consciousness theories
51:30 - Mac Shine Q1
1:04:38 - Are dendrites conceptually useful?
1:09:15 - Insights from implementation level
1:24:44 - How detailed to model?
1:28:15 - Do action potentials cause consciousness?
1:40:33 - Mac Shine Q2
Patreon for full episodes and Discord community:
patreon.com/braininspired
Free Video Series: Open Questions in AI and Neuroscience:
braininspired.co/open
Apple podcasts: itunes.apple.com/us/podcast/brain-inspired/id1428880766?mt=2
Spotify: open.spotify.com/show/2UZj8c8Ap5oc2gh2rJxLLe
Music by: The New Year: thenewyear.net
Show notes: braininspired.co/podcast/138
Matthew Larkum runs his lab at Humboldt University of Berlin, where his group studies how dendrites contribute to computations within and across layers of the neocortex. Since the late 1990s, Matthew has continued to uncover key properties of the way pyramidal neurons stretch across layers of the cortex, their dendrites receiving inputs from those different layers - and thus different brain areas. For example, layer 5 pyramidal neurons have a set of basal dendrites near the cell body that receives feedforward-like input, and a set of apical dendrites all the way up in layer 1 that receives feedback--like input. Depending on which set of dendrites is receiving input, or neither or both, the neuron's output functions in different modes- silent, regular spiking, or burst spiking. Matthew realized the different sets of dendritic inputs could signal different operations, often pairing feedforward sensory--like signals and feedback context-like signals. His research has shown this kind of coincidence detection is important for cognitive functions like perception, memory, learning, and even wakefulness. We discuss many of his ideas and research findings, why dendrites have long been neglected in favor of neuron cell bodies, the possibility of learning about computations by studying implementation-level phenomena, and much more.
0:00 - Intro
5:31 - Background: Dendrites
23:20 - Cortical neuron bodies vs. branches
25:47 - Theories of cortex
30:49 - Feedforward and feedback hierarchy
37:40 - Dendritic integration hypothesis
44:32 - DIT vs. other consciousness theories
51:30 - Mac Shine Q1
1:04:38 - Are dendrites conceptually useful?
1:09:15 - Insights from implementation level
1:24:44 - How detailed to model?
1:28:15 - Do action potentials cause consciousness?
1:40:33 - Mac Shine Q2
