Machine Learning Models for Neuroscience

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Related lectures (31)

Explores the optimization of neuroprosthetic systems, including sensory feedback restoration and neural stimulation strategies.

Graph Signal Processing for the Brain

Explores Graph Signal Processing applied to brain networks, emphasizing the relationship between brain function and structure using methods like Graph Fourier Transform and Structural-Decoupling Index.

Neural Taskonomy and Historical Perspectives in Visual Intelligence

Covers Neural Taskonomy, the evolution of neural networks, and historical perspectives in visual intelligence.

Neural System Organization

Explores the organization of the nervous system, including neuron structure, synapses, neurotransmitters, and neural circuits.

Ring Attractor Dynamics in Drosophila Brain

Explores ring attractor networks in the Drosophila brain for maintaining heading direction during navigation.

Machine Learning for Feature Extraction

Explores machine learning for feature extraction, 3D vision, and neural networks in mobile robotics.

Feedback & Adaptation: Visual Intelligence

Covers feedback mechanisms in visual intelligence, human pose estimation, motor adaptation in legged robots, and PID controllers.

Dynamical modeling, decoding, and control of multiscale brain network activity

Explores dynamical modeling, decoding, and control of brain network activity for personalized therapy.

Neuroscience and AI: Bridging the Gap

Explores the gap between AI and human intelligence through neuroscience-inspired models and algorithms.

Deep Learning: Convolutional Neural Networks

Covers Convolutional Neural Networks, standard architectures, training techniques, and adversarial examples in deep learning.

Neuroscience and Machine Learning

Explores the intricate relationship between neuroscience and machine learning, highlighting the challenges of analyzing neural data and the role of machine learning tools.

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