BI 178 Eric Shea-Brown: Neural Dynamics and Dimensions
Nov 13, 2023
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Eric Shea-Brown, a theoretical neuroscientist, discusses dynamics and dimensionality in neural networks, exploring how they change during tasks. He highlights research findings on structural connection motifs and dimensionalities related to different modes of learning. The podcast also covers the impact of model architectures on neural dynamics, the complexity of the biological brain, and the concept of rich brain vs lazy brain. The chapter on paths and motifs in neural networks showcases a student's prediction abilities. Finally, the guest expresses desires for advancements in neuroscience and support for the podcast.
Dimensionality in neural networks determines their capacity to represent and process information.
The brain strikes a balance between high-dimensional representations for rich sensory information and low-dimensional representations for navigation.
Local connectivity motifs in neural networks can provide insights into overall dimensionality and capacity.
Understanding dimensionality in neural networks is essential for comprehending their computational capabilities and information processing.
Deep dives
The Importance of Dimensionality in Neural Networks
Dimensionality refers to the number of degrees of freedom in a system, determining how many different states a system can be in. In the context of neural networks, dimensionality is crucial because it determines the system's capacity to represent and process information. For example, a one-dimensional brain in which every neuron is tightly coupled and controlled by a single neuron would have limited capacity and functionality. However, as dimensionality increases, the system can represent and process more independent pieces of information, leading to a significant expansion in capacity. Understanding and studying dimensionality helps reveal the brain's computational capabilities and how it efficiently represents and processes information.
Balancing Dimensionality for Navigation and Computation
Dimensionality plays a role in various aspects of brain function. While high-dimensional representations are useful for capturing rich sensory information, low-dimensional representations are more suitable for tasks such as navigation. The brain appears to strike a balance between these two requirements. For example, in visual processing, there is a hierarchy of dimensionality, with early visual areas capturing detailed information and later areas abstracting and reducing dimensionality. This hierarchical processing allows the brain to extract relevant features while maintaining navigational capabilities. Furthermore, recent research suggests that the brain can encode abstract representations in low-dimensional spaces, ensuring both cognitive flexibility and the ability to navigate the complex high-dimensional world.
Motifs and Dimensionality Prediction
Motifs are local structures or patterns of connections between neurons that play a role in determining dimensionality. Researchers have explored whether the presence and arrangement of these motifs can predict the dimensionality of neural networks. By examining the percentage of specific local connection patterns, such as two-by-two connections, researchers have found correlations between motif patterns and overall dimensionality. These findings suggest that local connectivity motifs can provide insights into the dimensionality and capacity of neural networks. However, further research is needed to fully understand the relationship between motifs and dimensionality and its implications for brain function.
Exploring Dimensionality in Neural Networks
Studying dimensionality in neural networks is essential for understanding their computational capabilities and information processing. Dimensionality determines the capacity of a system to represent and process information. It impacts navigation, computation, and the balance between capturing rich sensory information and maintaining cognitive flexibility. Researchers have explored the role of motifs, local connectivity patterns, in predicting dimensionality. Understanding how dimensionality emerges and is controlled in neural networks provides valuable insights into the brain's functionality. Further research in this field will deepen our understanding of how dimensionality impacts brain function and computation.
Understanding Neural Dynamics and Predictions
Neural dynamics can be described by dynamical laws that predict future activity based on current activity. A network of neurons influences each other's activity, leading to predictions based on complex pathways in the network.
The Role of Connectivity Motifs
Connectivity motifs are specific patterns of connections in a network. These motifs can explain various network dynamical properties and can be decomposed into different paths of increasing length. These paths contribute to the overall activity and can be used to predict network dimensionality.
Challenges and Importance of Bridging Theory and Brain Anatomy
There are challenges in bridging theoretical models and brain anatomy. Learning objectives and assumptions in modeling are often oversimplified. Ignoring crucial anatomical features, such as thalamo-cortical interactions, can limit our understanding of brain dynamics. Incorporating detailed knowledge of anatomy and cognitive psychology is crucial for comprehensive models of brain function.
Eric Shea-Brown is a theoretical neuroscientist and principle investigator of the working group on neural dynamics at the University of Washington. In this episode, we talk a lot about dynamics and dimensionality in neural networks... how to think about them, why they matter, how Eric's perspectives have changed through his career. We discuss a handful of his specific research findings about dynamics and dimensionality, like how dimensionality changes when one is performing a task versus when you're just sort of going about your day, what we can say about dynamics just by looking at different structural connection motifs, how different modes of learning can rely on different dimensionalities, and more.We also talk about how he goes about choosing what to work on and how to work on it. You'll hear in our discussion how much credit Eric gives to those surrounding him and those who came before him - he drops tons of references and names, so get ready if you want to follow up on some of the many lines of research he mentions.
0:00 - Intro
4:15 - Reflecting on the rise of dynamical systems in neuroscience
11:15 - DST view on macro scale
15:56 - Intuitions
22:07 - Eric's approach
31:13 - Are brains more or less impressive to you now?
38:45 - Why is dimensionality important?
50:03 - High-D in Low-D
54:14 - Dynamical motifs
1:14:56 - Theory for its own sake
1:18:43 - Rich vs. lazy learning
1:22:58 - Latent variables
1:26:58 - What assumptions give you most pause?
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