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SUMMARY:Computational Neuroscience Journal Club - Ryutaro Tanno (Universit
 y of Cambridge)
DTSTART:20150210T160000Z
DTEND:20150210T170000Z
UID:TALK57898@talks.cam.ac.uk
CONTACT:Guillaume Hennequin
DESCRIPTION:Ryutaro Tanno will cover:\n\n* A Probabilistic Palimpsest Mode
 l of Visual Short-term Memory\n* Loic Matthey\, Paul Bays and Peter Dayan\
 n* PLoS Comp. Biol. (2015)\n* http://journals.plos.org/ploscompbiol/articl
 e?id=10.1371/journal.pcbi.1004003\n\nWorking memory plays a key role in co
 gnition\, and yet its mechanisms remain much debated. Human performance on
  memory tasks is severely limited\; however\, the two major classes of the
 ory explaining the limits leave open questions about key issues such as ho
 w multiple simultaneously-represented items can be distinguished. We propo
 se a palimpsest model\, with the occurrent activity of a single population
  of neurons coding for several multi-featured items. Using a probabilistic
  approach to storage and recall\, we show how this model can account for m
 any qualitative aspects of existing experimental data. In our account\, th
 e underlying nature of a memory item depends entirely on the characteristi
 cs of the population representation\, and we provide analytical and numeri
 cal insights into critical issues such as multiplicity and binding. We con
 sider representations in which information about individual feature values
  is partially separate from the information about binding that creates sin
 gle items out of multiple features. An appropriate balance between these t
 wo types of information is required to capture fully the different types o
 f error seen in human experimental data. Our model provides the first prin
 cipled account of misbinding errors. We also suggest a specific set of sti
 muli designed to elucidate the representations that subjects actually empl
 oy.
LOCATION:Cambridge University Engineering Department\, CBL\, BE-438 (http:
 //learning.eng.cam.ac.uk/Public/Directions)
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