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SUMMARY:How much to gain: Controlling space and time via gain modulation i
 n  cortical networks - Jake Stroud (Tim Vogels Lab. University of Oxford)
DTSTART:20171113T143000Z
DTEND:20171113T153000Z
UID:TALK95548@talks.cam.ac.uk
CONTACT:Rodrigo Echeveste
DESCRIPTION:Animals perform an extraordinary variety of movements over man
 y different time scales. To support this diversity\, the motor cortex (M1)
  exhibits a similarly rich repertoire of activities (Shenoy et al.\, 2013)
 . Although recent neuronal network models capture many qualitative aspects
  of M1 dynamics\, such as complex multiphasic activity transients\, they c
 an generate only a few distinct movements with a fixed duration (Hennequin
  et al.\, 2014 and Sussillo et al.\, 2015). Therefore\, it is unclear how 
 M1 efficiently controls movements over a wide range of shapes and speeds.\
 n\nHere we demonstrate that simple modulation of neuronal input-output gai
 ns in recurrent neuronal network models with fixed connectivity can substa
 ntially and predictably affect downstream muscle outputs. Consistent with 
 the observation of diffuse neuromodulatory projections to M1 (Molina-Luna 
 et al.\, 2009 and Hosp et al.\, 2011)\, our results suggest that a relativ
 ely small number of modulatory control units provide sufficient flexibilit
 y to adjust high-dimensional network activity on behaviourally relevant ti
 me scales. Such modulatory gain patterns can be obtained through a simple 
 reward-based learning rule. Novel movements can also be assembled from pre
 viously learned primitives\, thereby facilitating fast acquisition of hith
 erto untrained muscle outputs. Moreover\, we show that it is possible to s
 eparately change movement speed while preserving movement shape\, thus ena
 bling efficient and independent movement control in space and time. Our re
 sults provide a new perspective on the role of neuromodulatory systems in 
 controlling recurrent cortical activity and suggests that modulation of si
 ngle-neuron excitability is an important aspect of learning.\n
LOCATION:Cambridge University Engineering Department\, CBL\, BE4-38 (http:
 //learning.eng.cam.ac.uk/Public/Directions)
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