Data-Driven Modeling in Neuroscience: Meenakshi Khosla

Related lectures (32)

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Explores the relationship between neuroscience and machine learning in visual intelligence.

Explores the reconstruction of a full-scale mouse striatal cellular level model to integrate and interpret striatal data.

Explores computational models of the ventral visual system, focusing on optimizing networks for real-world tasks and comparing to brain data.

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

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

Explores the overview, rationale, and strategies of simulation neuroscience, emphasizing the challenges of reconstructing and simulating the brain.

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

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

Explores neural networks, activation functions, backpropagation, and PyTorch implementation.

Explores advancing system models of human intelligence through integrative benchmarking and the importance of Brain-Score for fair model comparisons.