Reconstructing the perceptual organization of sound from neural responses

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Background: Sets of stimuli can span different stimulus spaces. Examples include linear, circular  or  planar.  Since  the  neural  system  does  not  know  the  geometry  of  this  stimulus  space, it needs to have a way of estimating it from information contained in the neural population  responses.  Recently,  a  set  of  techniques  was  proposed  that  can  achieve  this  estimation, known as representational similarity analysis (RSA). Methods:  We  expand  the  current  framework  of  RSA  by  establishing  a  criterion  for  taking  into account the local geometry of the neural response manifold. We refer to this expansion as gRSA (global RSA). To do so, we compute distances between stimuli within the response manifold  (Local  Distance  Matrix,  LDM).  Once  pairwise  distances  have  been  identified,  we  reconstruct the global geometry from the local geometry by recreating the neighborhoods of the  manifold  (Global  Distance  Matrix,  GDM).  The  GDM  is  constructed  by  stochastic  exploration of the LDM. Once a certain value of cross correlation is established two neighbors are identified based on a local decoder. That way, the path between two stimuli in the  response  manifold  can  be  thought  as  the  shortest  distance  between  two  responses  within that manifold. Results: We applied gRSA to simulations and real data (neural responses from the auditory cortex of the ferret). We successfully reconstructed the stimulus geometry of the simulated data. The analysis led to a satisfactory reconstruction of the stimulus space geometry for the real responses. Conclusion:  The  perseverance  of  similarity  from  the  external  to  the  internal  space  (2nd  order isomorphism) is only achieved when the local geometry is taken into account. Our results  showed  that  when  this  local  aspect  is  not  taken  into  account,  the  2nd  order  isomorphism is sometimes violated and the stimulus space reconstruction can fail.
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