Performance Transition

Related lectures (32)

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Covers the integration of visual perception and robotic actions in embodied AI.

Explores spatial continuum models, field equations, and coupling mechanisms in computational neuroscience.

Provides an overview of classical vision theories, exploring how perception is shaped by psychological and environmental factors.

Explores Generalized Linear Models, Bayesian methods, compressed sensing, and perception in high-dimensional statistics.

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

Explores the components of neuro robots and compares them to actual animals.

Explores visual intelligence, perception, image classification, and the connection between vision and action in complex systems.

Explores anatomic conventions, homunculus mapping, and body scheme integration in perception and action.

Explores human vision optics, eye structure, image formation, magnification, and wave divergence.

Explores cortical fields, orientation columns, solution types, and perception in computational neuroscience.