BI 180 Panel Discussion: Long-term Memory Encoding and Connectome Decoding
Dec 11, 2023
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Panel discussion on using neuroscience technologies to decode memory from connectomes, featuring a group of experts including Kenneth Hayworth. Topics include advancements in connectomics, decoding memory and connectomes, analyzing connectome complexity, the role of molecules, deep learning parallelism, studying connectome data with cultured neurons, understanding neuronal interactions, and the rules of connectome interpretation.
Understanding individual differences and correlating multiple connectomes are crucial in decoding non-trivial memory from a static connectome.
Decoding memories from connectomes requires integrating biophysics and studying the relationship between individual neuronal function and network dynamics.
Determining the relationship between the 3D structure of cells and their functional properties is vital for decoding memories from connectomes.
Deep dives
Aspirational Neuroscience and Connectomics
Aspirational neuroscience is an outreach project that aims to bring neuroscientists together to explore the possibilities of mapping larger pieces of brains at the synaptic level. Recent advancements in electron microscopy connectomics have allowed mapping of the fruit fly brain and a cubic millimeter of mouse cortex. Expansion microscopy has also shown promise in connectomics using light microscopy. The goal of the panel discussion was to discuss how current and developing neuroscience technologies can decode non-trivial memory from a static connectome.
Challenges and Obstacles
Several challenges were mentioned during the panel discussion. One challenge is the need for correlating multiple connectomes to understand individual differences and the richness of memories. Another challenge is the complexity of neural networks and the difficulty in deciphering causality and flow of information. There is also a need to better define the questions and hypotheses to be tested in order to bridge the gap between whole connectomes and synaptic properties. Furthermore, the understanding of the different levels of information storage in the connectome, from molecules to network structures, needs to be further explored.
Decoding Memories and Behavior
The panelists discussed the possibility of decoding memories from connectomes. Suggestions included decoding visual images or familiar stimuli from mouse visual cortex connectomes, identifying associative memories in C. elegans, and modeling engrams in simpler organisms like flies and C. elegans. The importance of integrating biophysics and understanding the relationship between individual neuronal function and network dynamics was emphasized. The panelists also highlighted the need to define what constitutes non-trivial memories and to focus on deciphering specific behavioral tasks.
Future Directions and Conclusion
The panel discussion emphasized that this is just the beginning of the debate and exploration of decoding memories from connectomes. The aspiration is to develop a clearer understanding of what is necessary to decode memories at an individual level and to develop testable hypotheses and experiments. There was a consensus on the importance of advancing technology to acquire larger scale connectomes and to integrate measurements at different scales within a coherent modeling framework. The panel discussion was seen as a starting point for a broader conversation in the field.
Decoding Memories from Connectomes
The podcast episode discusses the challenges and potential of decoding memories from connectomes. The speakers highlight the importance of understanding the baseline behavior and correlating it with the connectome and genome to distinguish between genetically defined aspects and those learned through experience. While the feasibility of the approach using real brains is uncertain, the idea of decoding memories from cultured neurons or smaller circuits is intriguing. They emphasize the need for determining the relationship between the 3D structure of cells and their functional properties, suggesting that as long as a single mapping is achieved, structure can be translated into function.
Defining and Decoding Memory
The discussion focuses on the key question of what constitutes a non-trivial memory and the challenges in decoding it from connectomes. The speakers highlight the importance of behavioral output as the primary indicator of encoded memory, but also acknowledge the need to move beyond simple binary decoding and embrace computational models at the system level. They emphasize the need to distinguish between memories and innate preferences, and suggest that understanding the mechanism of memory formation and the correlation between experience and anatomical changes is crucial. They also discuss the statistical nature of memory in vertebrate brains, where factors like connectivity and memory capacity play significant roles.
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Welcome to another special panel discussion episode.
I was recently invited to moderate at discussion amongst 6 people at the annual Aspirational Neuroscience meetup. Aspirational Neuroscience is a nonprofit community run by Kenneth Hayworth. Ken has been on the podcast before on episode 103. Ken helps me introduce the meetup and panel discussion for a few minutes. The goal in general was to discuss how current and developing neuroscience technologies might be used to decode a nontrivial memory from a static connectome - what the obstacles are, how to surmount those obstacles, and so on.
There isn't video of the event, just audio, and because we were all sharing microphones and they were being passed around, you'll hear some microphone type noise along the way - but I did my best to optimize the audio quality, and it turned out mostly quite listenable I believe.
