Since 2010 approaches in deep learning have revolutionized fields as diverse as computer vision, machine learning, or artificial intelligence. This course gives a systematic introduction into influential models of deep artificial neural networks, with a focus on Reinforcement Learning.
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No BackProp, Please! How can a biological network learn deep representations without Backprop? This video shows that biologically plausible learning rules with a…
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VideoLecture00.2IntroBiology 2 of 15
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VideoLecture00.2IntroBiology
Introduction to Biological Background:From Biological to Artificial Neural NetworksVideoLecture00.2IntroBiology
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Three-factor learning rules:Three-factor rules add to Hebbian rules a neuromodulator that conveys a global feedback signal.
videoLecture01.3-ThreeFactorRules-rec
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Hebb Rule: a framework for learning rules
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Biological Learning means Synaptic Plasticity
videoLecture01.1HebbRules-part1rec1
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No Backprop, please !BackProp is the most widely used algorithm in machine learning and artificial neural networks, but it is biologically not plausible. Why? And are…
videoLecture00.1noBackProp
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The formalism of low-rank recurrent neural networks is introduced. It is shown that trajectories of the dynamics of all N neurons stays in a D-dimensional space, if the…
Low-rank recurrent neural networks
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This video presents two views of neural activity. The first view is the classic view in the tradition of Hubel and Wiesel, i.e., neurons have characteristic functional…
Two views of neural activity in the brain
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Neural Manifold is the name used by neuroscientists for the following observation: when experimentalists record from, say, N = 500 neurons, the activity vector of all N…
What are Neural Manifolds?
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CS-456 Artificial Neural Networks Summer 2024 -…
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CS-456 Artificial Neural Networks Summer 2024 -…
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CS-456 Artificial Neural Networks Summer 2024 -…
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CS-456 Artificial Neural Networks Summer 2024 -…
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CS-456 Artificial Neural Networks Summer 2024 -…
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CS-456 Artificial Neural Networks Summer 2024 -…
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