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BI 132 Ila Fiete: A Grid Scaffold for Memory

BI 132 Ila Fiete: A Grid Scaffold for Memory

FromBrain Inspired


BI 132 Ila Fiete: A Grid Scaffold for Memory

FromBrain Inspired

ratings:
Length:
77 minutes
Released:
Apr 3, 2022
Format:
Podcast episode

Description

Announcement:



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Ila discusses her theoretical neuroscience work suggesting how our memories are formed within the cognitive maps we use to navigate the world and navigate our thoughts. The main idea is that grid cell networks in the entorhinal cortex internally generate a structured scaffold, which gets sent to the hippocampus. Neurons in the hippocampus, like the well-known place cells, receive that scaffolding and also receive external signals from the neocortex- signals about what's happening in the world and in our thoughts. Thus, the place cells act to "pin" what's happening in our neocortex to the scaffold, forming a memory. We also discuss her background as a physicist and her approach as a "neurophysicist", and a review she's publishing all about the many brain areas and cognitive functions being explained as attractor landscapes within a dynamical systems framework.



The Fiete Lab.Related papersA structured scaffold underlies activity in the hippocampus.Attractor and integrator networks in the brain.





0:00 - Intro
3:36 - "Neurophysicist"
9:30 - Bottom-up vs. top-down
15:57 - Tool scavenging
18:21 - Cognitive maps and hippocampus
22:40 - Hopfield networks
27:56 - Internal scaffold
38:42 - Place cells
43:44 - Grid cells
54:22 - Grid cells encoding place cells
59:39 - Scaffold model: stacked hopfield networks
1:05:39 - Attractor landscapes
1:09:22 - Landscapes across scales
1:12:27 - Dimensionality of landscapes
Released:
Apr 3, 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.