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SUMMARY:Learning and retaining tasks in redundant brain circuits - Timothy
  O'Leary\, University of Cambridge
DTSTART:20191121T140000Z
DTEND:20191121T150000Z
UID:TALK131122@talks.cam.ac.uk
CONTACT:Alberto Padoan
DESCRIPTION:Neuronal networks have many tunable parameters such as synapti
 c strengths that are shaped during learning of a task. The number of degre
 es of freedom for representing a task can vastly exceed the minimum requir
 ed for good performance. I will describe recent work that explores the con
 sequences of such additional ‘redundant’ degrees of freedom for learni
 ng and for task representation in animals. We find that additional redunda
 ncy in network parameters can make a fixed task easier to learn and compen
 sate for deficiencies in learning rules. However\, we also find that in a 
 biologically relevant setting where synapses are subject to unavoidable no
 ise there is an upper limit to the level of useful redundancy in a network
 \, suggesting an optimal network size for a given task.
LOCATION: Cambridge University Engineering Department\,  Seminar Room JDB
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