networks of neurons
DESCRIPTION
Networks of Neurons. Computational Cognitive Neuroscience Randall O’Reilly. Networks. Biology of Neocortex (“cortex”) Categorization and Distributed Reps Bidirectional Excitation and Attractors Inhibitory Competition and Activity Regulation. Neurons: Excitatory and Inhibitory. - PowerPoint PPT PresentationTRANSCRIPT
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Networks of Neurons
Computational Cognitive NeuroscienceRandall O’Reilly
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Networks
Biology of Neocortex (“cortex”) Categorization and Distributed Reps Bidirectional Excitation and Attractors Inhibitory Competition and Activity Regulation
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Neurons: Excitatory and Inhibitory
Excitatory = main info processing, long-range connectionsInhibitory = local, activity regulation and competition
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The 6 Layer Cake..
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Is Actually Only 3..
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Patterns of Connectivity
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Bidirectional Symmetry
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Biology => Function
Feedforward excitation = categorization of inputs
Feedback excitation = attractor dynamics Lateral inhibition = competition, activity
regulation
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We Think in Categories
(much easier than disconnected pixels..)
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Hierarchy of Categories
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The Chair Category
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Getting the right ones is key..
Two men are dead in a cabin in the woods. What happened??
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Categories are Interesting!
What makes a mental categorization accurate? Is there something “real” about a “chair?”
Stereotypes are mental categories.. Can you encode multiple categories at the
same time??
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Distributed Representations
Let a 1,000 categories bloom.. You’ve got the room in your head (billions of neurons)
Each neuron can respond to multiple things (graded similarity)
And each thing activates many neurons (who knows what is going to be relevant this time?)
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Graded Responses
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Distributed Patterns
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Topographic Organization
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Distributed Parts
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Not Just Monkeys
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Coarse Coding Efficiency
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Localist Representations?
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Bidirectional Excitatory Dynamics
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Top-down Ambiguity Resolution
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What Are These?
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A Big Network Model..
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Bidirectional Dynamics
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Inhibition
Competition: selection pressure, survival of the “fittest”, picking the best detector for the job..
Interacts with learning: “rich get richer” (but also narrower – no hogging the inputs please!)
“Sparse distributed representations” (and also essential for controlling activity, like
an air conditioner)
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Feedforward and Feedback Inhib
• Feedback “reacts” (AC comes on after it gets hot enough)• Feedforward “anticipates” (e.g., if AC measured outdoor temp,
or weather forecast)
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kWTA Approximation