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Related lectures (28)
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Centrality and Hubs
Delves into centrality and hubs in network neuroscience, exploring node importance, small-world networks, brain structural connectome, and percolation theory.
Energy landscape: Symmetric Interactions
Explores the energy landscape in symmetric interactions, emphasizing the importance of symmetric weights and random patterns.
Optimization: Mathematical Principles and Algorithms
Covers mathematical principles and algorithms of optimization, using real-world examples and Python implementation.
Mathematical Displays
Covers the use of mathematical displays and symbols in mathematics.
Stochastic Hopfield model
Explores the Stochastic Hopfield model, noisy neurons, firing probabilities, memory retrieval, and overlap equations in attractor networks.
Attractor Networks and Spiking Neurons
Explores attractor networks, spiking neurons, memory data, and realistic networks in neural dynamics.
Handling Networks: Graph Theory
Explores graph theory concepts, centrality measures, and real-world network properties, providing insights into handling diverse types of networks.
Attractor Networks and Generalizations
Explores attractor networks, Hopfield model generalizations, and memory dynamics in computational neuroscience.
