Data-driven methods for discovering the structure of neural and cognitive representations (Part 1...) Kenneth Whang Bryn Mawr College

Three main ideas  hypothesis testing and model building  population coding, distributed representations  dimension reduction and individual differences modeling Outline Prologue: statement of the problem An approach: multidimensional scaling Compare/critique: what needs to be done

behavior/experience “implementation” what’s going on in here?

behavior/experience “implementation” Experimental Psychology develop model, build instrument, measure Example: attention Problems: limited by model’s ontology doesn’t scale well (how to capture complex behavior?) hypothesis formation itself can be a form of observer bias

behavior/experience “implementation” Physiology classify cells, characterize their properties Example: “oriented bars” Problems: unclear or speculative connection to function continuity of types– how many types are there? ad hoc

behavior/experience “implementation” Computer Science? concentrate on data and what they might represent; let data be primary driver of modeling and interpretation

About representations Computer Science  representations and computational strategies closely linked  what you can compute is related to what you can express Neurophysiology  some examples indicative of function (orderly maps, tuning, graded responses)  others much more muddled

Distributed or localized? “dense”“sparse”“grandmother cell” representational capacity highmediumlow learning/adaptationslowfast generalizationgood none fault tolerancehigh none energy usehighlow Examples: motor cortex (direction of reach); visual cortex (natural scenes) Except for grandmother cell, interested in relationships