On multi-area cortex models - with Sacha van Albada - #4
Nov 18, 2023
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In this engaging conversation, Sacha van Albada, a leading expert in theoretical neuroanatomy, shares insights on developing advanced multi-area cortex models for macaques and humans. He discusses the challenges of linking single-neuron activity to larger systems, the intricacies of simulating neural dynamics on supercomputers, and the importance of physiological data for accuracy. Sacha also highlights the significance of collaboration in improving models and explores concepts like predictive coding, making complex neuroscience more accessible for researchers.
Sasha van Albada's research underscores the importance of multi-area models in linking single-neuron dynamics to cognitive functions in extensive cortical networks.
The construction of these neural models involves meticulous decisions around neuron types and their connections, significantly impacting their biological realism and functionality.
Validation of the models against experimental data ensures their biological relevance, as adjustments to parameters align model predictions with observed neural activity.
Deep dives
Understanding Neuronal Networks
The podcast highlights the current understanding of individual neuron functions versus the collective behavior of neurons in networks. While significant progress has been made in understanding how single neurons receive signals and generate action potentials, the intricacies of how interconnected neurons operate together in a network remain largely unexplored. Researchers have insight into general properties of neural networks, such as the balance of excitation and inhibition, but lack comprehensive models that illustrate the specific dynamics of different cortical areas. This gap emphasizes the need for advancing computational neuroscience to develop mathematical models that link individual neuron activity to broader cognitive functions.
Bold Advances in Multi-Area Modeling
Sasha van Albada's work on large-scale neural network models is a significant leap in bridging the gap between single-neuron dynamics and overall neuronal behavior in extensive cortical networks. Her research team created a model of the macaque visual cortex, incorporating 32 cortical areas and millions of neurons, and later expanded this effort to develop a model for the human cortex encompassing 34 areas. These multi-area models help in understanding relationships between cortical structure and functional dynamics, challenging existing paradigms in neuroscience. Through detailed simulations, the models can replicate observed cortical activities and dynamics under various conditions, illuminating how different brain regions communicate with one another.
Model Construction and Computational Challenges
Building complex neural models involves detailed decision-making regarding neuron types, their connections, and circuit structures within each cortical area. The podcast discusses the methodology of constructing these models, including the choice of neuron models and how connectivity is established between different areas of the cortex using data from anatomical studies. These models, based on the leaky integrate-and-fire neuron type, simulate large-scale activity across multiple regions and require significant computational resources, often run on supercomputers. The challenges in tuning these models and ensuring they accurately reflect biological reality underscore the intricate relationship between anatomy and function in neural networks.
Insights Gained from Experimental Data Comparison
A critical aspect of validating these neural models involves comparing their outputs with experimental data from biological systems. The models are first tested at the microscopic level against spiking activity data obtained from experiments on macaques, ensuring their predictions align with real-world observations of neural dynamics. By adjusting parameters such as synaptic strengths, researchers can successfully match model predictions with functional connectivity measured through fMRI scans. This dual-level validation process not only reinforces the models' biological relevance but also offers insights into how various neural connections and activity levels influence cognitive states.
Future Directions and Theories in Neuroscience
The discussion explores potential future directions in the field of theoretical neuroscience, particularly the inclusion of feedback mechanisms and prediction coding in neural models. Innovations such as incorporating more complex neuron types and studying larger cortical distances are necessary to enhance model realism. The role of top-down processing, where higher-level brain areas influence lower areas during sensory inputs, needs to be investigated further. These advancements could provide a robust framework for testing bold hypotheses in cognitive neuroscience and potentially reveal how the brain integrates functions across different scales.
A key goal of computational neuroscience is to build mathematical models linking single-neuron activity to systems-level activity.
The guest has taken some bold steps in this direction by developing and exploring a multi-area model for the macaque visual cortex, and later also a model for the human cortex, using millions of simplified spiking neuron models.
We discuss the many design choices, the challenge of running the models, and what has been learned so far.
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