Modeling the Brain: Machine Learning and Neuroscience

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Data-Driven Modeling in Neuroscience: Meenakshi Khosla

By Meenakshi Khosla explores data-driven modeling in large-scale naturalistic neuroscience, focusing on brain activity representation and computational models.

Brain Intelligence: Continual Learning of Representational Models

Delves into the continual learning of representational models after deployment, highlighting the limitations of current artificial neural networks.

Introduction to Systems Neuroscience: Memory Systems Overview

Introduces systems neuroscience, focusing on neural circuits, memory systems, and course logistics.

Building Physical Neural Networks

Discusses challenges in building physical neural networks, focusing on depth, connections, and trainability.

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.

Controlling Behavior in Animals and Robots

Explores embodied behavioral control in animals and robots through group presentations and hands-on exercises.

Scientific Computing in Neuroscience

Explores the history and tools of scientific computing in neuroscience, emphasizing the simulation of neurons and networks.

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.

Morphological Reconstruction in Neuroscience

Explores the challenges and significance of classifying neurons based on morphology for accurate brain modeling and understanding brain function.

Neural Modeling: From Sensory Systems to Behavior

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