Help Privacy Policy Disclaimer
  Advanced SearchBrowse





Correlations strike back (again): the case of associative memory re-trieval

There are no MPG-Authors in the publication available
External Resource
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available

Savin, C., Dayan, P., & lengyel, M. (2013). Correlations strike back (again): the case of associative memory re-trieval. Poster presented at Computational and Systems Neuroscience Meeting (COSYNE 2013), Salt Lake City, UT, USA.

Cite as: https://hdl.handle.net/21.11116/0000-0008-0DB6-D
It has long been recognised that statistical dependencies in neuronal activity need to be taken into account whendecoding stimuli encoded in a neural population. It is far less well appreciated that the same decoding challengesarise in the context of autoassociative memory, when retrieving information stored in correlated synapses. Suchcorrelations have been well documented experimentally (Song et al, 2005); here we show how they can arisebetween synapses that share pre- or post-synaptic partners when any of several well-known additive (Hopfield,1982) or metaplastic (Fusi et al, 2005) learning rules is applied. To assess the importance of these dependen-cies for recall, we adopt the strategy of comparing the performance of decoders which either do, or do not, takethem into account, but are otherwise optimal, showing that ignoring synaptic correlations has catastrophic conse-quences for retrieval. We therefore study how recurrent circuit dynamics can implement decoding that is sensitiveto correlations. Optimal retrieval dynamics in the face of correlations require substantial circuit complexities. Bycontrast, we show that it is possible to construct approximately optimal retrieval dynamics that are biologicallyplausible. The difference between these dynamics and those that ignore correlations is a set of non-linear circuitmotifs that have been suggested on experimental grounds, including forms of feedback inhibition and experimen-tally observed dendritic nonlinearities (Branco et al, 2011). We therefore show how assuaging an old enemy leadsto a novel functional account of key biophysical features of the neural substrate.