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BI 138 Matthew Larkum: The Dendrite Hypothesis

BI 138 Matthew Larkum: The Dendrite Hypothesis

FromBrain Inspired


BI 138 Matthew Larkum: The Dendrite Hypothesis

FromBrain Inspired

ratings:
Length:
112 minutes
Released:
Jun 6, 2022
Format:
Podcast episode

Description

Check out my free video series about what's missing in AI and Neuroscience






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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.



Larkum Lab.Twitter: @mattlark.Related papersCellular Mechanisms of Conscious Processing.Perirhinal input to neocortical layer 1 controls learning. (bioRxiv link: https://www.biorxiv.org/content/10.1101/713883v1)Are dendrites conceptually useful?Memories off the top of your head.Do Action Potentials Cause Consciousness?Blake Richard's episode discussing back-propagation in the brain (based on Matthew's experiments)



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
Released:
Jun 6, 2022
Format:
Podcast episode

Titles in the series (99)

Neuroscience and artificial intelligence work better together. Brain inspired is a celebration and exploration of the ideas driving our progress to understand intelligence. I interview experts about their work at the interface of neuroscience, artificial intelligence, cognitive science, philosophy, psychology, and more: the symbiosis of these overlapping fields, how they inform each other, where they differ, what the past brought us, and what the future brings. Topics include computational neuroscience, supervised machine learning, unsupervised learning, reinforcement learning, deep learning, convolutional and recurrent neural networks, decision-making science, AI agents, backpropagation, credit assignment, neuroengineering, neuromorphics, emergence, philosophy of mind, consciousness, general AI, spiking neural networks, data science, and a lot more. The podcast is not produced for a general audience. Instead, it aims to educate, challenge, inspire, and hopefully entertain those interested in learning more about neuroscience and AI.