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A scale-invariant neural architecture for cognitive computation (talk recording available)

Dr. Marc Howard

Tuesday, October 06, 2015, 01:00pm - 02:00pm

Boston University, Department of Psychological and Brain Sciences and Center for Memory and Brain

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The Weber-Fechner law is among the oldest quantitative relationships in experimental psychology.  Neural codes with Weber-Fechner spacing are widely-observed in the nervous system, most famously extrafoveal retinal position.  We show new evidence suggesting that a neural representation for time in the rodent hippocampus obeys Weber-Fechner spacing; preliminary evidence suggests a similar relationship may also hold in the mPFC and striatum.  We describe a neural mechanism for constructing Weber-Fechner scales.  The mechanism relies on taking the Laplace transform of incoming experience and can be applied to generate scale-invariant representations of time, space and number.  The apparent ubiquity of Weber-Fechner scales in the brain and the Laplace method for constructing representations of time, space, and number suggest a general framework for cognitive computation.  Operations such as translation, convolution, and cross-correlation can be efficiently computed in the Laplace domain, enabling flexible computation on scale-invariant representations.   Circuits constructed in this way obey properties such as compositionality that are challenging for traditional connectionist models.  A simple circuit for performing subtraction is demonstrated.

To view a recording of this talk click here (You will need a Rutgers NetID and password)

Dr. Marc Howard