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Computational Cognitive Neuroscience Lab Today: Second session

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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? - PowerPoint PPT Presentation

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Page 1: Computational Cognitive Neuroscience Lab

Computational Cognitive

Neuroscience LabToday: Second session

Page 2: Computational Cognitive Neuroscience Lab

Computational Cognitive Neuroscience Lab

» Today:» Homework is due Friday, Feb 10» This homework has more projects than

the last, but fewer questions per project

Page 3: Computational Cognitive Neuroscience Lab

Bias Weights» Why do we have them?» Why are some higher than others

in the transform project?

Page 4: Computational Cognitive Neuroscience Lab

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!

Page 5: Computational Cognitive Neuroscience Lab

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

Page 6: Computational Cognitive Neuroscience Lab

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

Page 7: Computational Cognitive Neuroscience Lab

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

Page 8: Computational Cognitive Neuroscience Lab

Inhibition» Benefits» Mechanisms» K-winners-take-all» Sparse, distributed codes