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SUMMARY:Computational Neuroscience Journal Club - Brian Trippe (CBL)
DTSTART:20161122T160000Z
DTEND:20161122T170000Z
UID:TALK69231@talks.cam.ac.uk
CONTACT:Daniel McNamee
DESCRIPTION:Brian Trippe will cover:\n\n* Tensor Analysis Reveals Distinct
  Population Structure that Parallels the Different Computational Roles of 
 Areas M1 and V1\n* Jeffrey S. Seely\, Matthew T. Kaufman\, Stephen I. Ryu\
 , Krishna V. Shenoy\, John P. Cunningham\, Mark M. Churchland\n* PLoS Comp
 utational Biology (November 2016)\n* http://journals.plos.org/ploscompbiol
 /article?id=10.1371/journal.pcbi.1005164\n\nABSTRACT:\nCortical firing rat
 es frequently display elaborate and heterogeneous temporal structure. One 
 often wishes to compute quantitative summaries of such structure—a basic
  example is the frequency spectrum—and compare with model-based predicti
 ons. The advent of large-scale population recordings affords the opportuni
 ty to do so in new ways\, with the hope of distinguishing between potentia
 l explanations for why responses vary with time. We introduce a method tha
 t assesses a basic but previously unexplored form of population-level stru
 cture: when data contain responses across multiple neurons\, conditions\, 
 and times\, they are naturally expressed as a third-order tensor. We exami
 ned tensor structure for multiple datasets from primary visual cortex (V1)
  and primary motor cortex (M1). All V1 datasets were ‘simplest’ (there
  were relatively few degrees of freedom) along the neuron mode\, while all
  M1 datasets were simplest along the condition mode. These differences cou
 ld not be inferred from surface-level response features. Formal considerat
 ions suggest why tensor structure might differ across modes. For idealized
  linear models\, structure is simplest across the neuron mode when respons
 es reflect external variables\, and simplest across the condition mode whe
 n responses reflect population dynamics. This same pattern was present for
  existing models that seek to explain motor cortex responses. Critically\,
  only dynamical models displayed tensor structure that agreed with the emp
 irical M1 data. These results illustrate that tensor structure is a basic 
 feature of the data. For M1 the tensor structure was compatible with only 
 a subset of existing models.
LOCATION:Cambridge University Engineering Department\, CBL\, BE-438 (http:
 //learning.eng.cam.ac.uk/Public/Directions)
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