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Computational Cognitive Neuroscience Lab

Computational Cognitive Neuroscience Lab. Today: Second session. Computational Cognitive Neuroscience Lab. Today: Homework is due Friday, Feb 10 This homework has more projects than the last, but fewer questions per project. Bias Weights. Why do we have them?

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Computational Cognitive Neuroscience Lab

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  1. Computational Cognitive Neuroscience Lab Today: Second session

  2. Computational Cognitive Neuroscience Lab • Today: • Homework is due Friday, Feb 10 • This homework has more projects than the last, but fewer questions per project

  3. Bias Weights • Why do we have them? • Why are some higher than others in the transform project?

  4. Local vs. Distributed Representations • Counting on your fingers--how high can you count?? • 10, using a localist representation • Using a distributed representation, such as a binary code, we can count to 1024!

  5. What is clamping? • An analogy to cellular physiology, where electrodes are inserted into cells to control the membrane potential • Types of clamps: current clamp, voltage clamp • Clamping just means to externally force a state upon the cell

  6. Bidirectional connectivity • What we have seen so far: bottom-up, or stimulus-driven excitation • Now: top-down, or hypothesis-driven excitation • What does top-down mean? Imagine

  7. What does top-down mean? • Customary terminology: bottom is stimulus, top is a brain-state • Bottom-up: think about a loud noise that makes you jump • Top-down: What if you knew to expect a loud noise? That expectation might make you jump less when you finally hear it

  8. Inhibition • Benefits • Mechanisms • K-winners-take-all • Sparse, distributed codes

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