Machine Learning Models for Neuroscience

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Related lectures (31)

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Understanding Visual Cortex with Deep Learning Models

Explores using deep learning models to understand the visual cortex and sensory portraits.

Neural Modeling: From Sensory Systems to Behavior

Explores neural modeling of sensory systems and behavior using deep neural networks and task-driven networks.

Neural Signals and Signal Processing

Explores neuronal signals, brain organization, measurement techniques, and MRI principles.

Computer Vision: Historical Insights and Project Inspirations

Explores the historical development of computer vision and inspires innovative project ideas.

Brain Intelligence: Continual Learning of Representational Models

Delves into the continual learning of representational models in brain intelligence, emphasizing rapid adaptation to unstructured environments.

Neural Signals and Signal Processing: Hemodynamic Imaging

Explores hemodynamic imaging techniques to measure brain activity and understand brain structure and function.

Introduction into the module

Introduces nerve cells' electrical activity, focusing on spikes and synaptic potentials.

Understanding the Hippocampus: Structure and Function

Explores the importance of the hippocampus in memory and spatial navigation, discussing its unique structure and implications for broader brain research.

Miniaturized CMOS Interfaces

Explores miniaturized CMOS interfaces for neural signals processing and neurostimulation.

Neural Networks: Two Layers Neural Network

Covers the basics of neural networks, focusing on the development from two layers neural networks to deep neural networks.