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DTSTART:20190331T030000
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DTSTART:20181028T020000
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DTSTAMP:20260404T133248Z
UID:5c2f8c1f41dae728499720@ist.ac.at
DTSTART:20190129T090000
DTEND:20190129T100000
DESCRIPTION:Speaker: Tim Vogels\nhosted by Maximilian Jösch\nAbstract: Ani
 mals perform an extraordinary variety of movements over many different tim
 e scales. To support this diversity\, the motor cortex (M1) exhibits a sim
 ilarly rich repertoire of activity. Although recent neuronal network model
 s capture many qualitative aspects of M1 dynamics\, such as complex multip
 hasic activity transients\, they can generate only a few distinct movement
 s. Additionally\, it is unclear how M1 efficiently controls movements over
  a wide range of speeds and shapes. Here we demonstrate that simple modula
 tion of neuronal input--output gains in recurrent network models with fixe
 d connectivity can substantially and predictably affect downstream muscle 
 outputs. Consistent with the observation of diffuse neuromodulatory projec
 tions to motor areas\, our results suggest that a relatively small number 
 of modulatory control units can provide sufficient flexibility to adjust h
 igh-dimensional network activity on behaviourally relevant time scales. Su
 ch modulatory gain patterns can be obtained through a simple reward-based 
 learning rule. Novel movements can also be assembled from previously learn
 ed primitives\, thereby facilitating fast acquisition of hitherto untraine
 d muscle outputs. Moreover\, we show that it is possible to separately cha
 nge movement speed while preserving movement shape\, thus enabling efficie
 nt and independent movement control in space and time. Our results provide
  a new perspective on the role of neuromodulatory systems in controlling r
 ecurrent cortical activity and suggests plasticity of single-neuron excita
 bility as an important substrate of learning.
LOCATION:Big Seminar room Ground floor / Office Bldg West (I21.EG.101)\, IS
 TA
ORGANIZER:tguggenb@ist.ac.at
SUMMARY:Tim Vogels: Motor primitives in time and space by targeted gain mod
 ulation in recurrent cortical networks
URL:https://talks-calendar.ista.ac.at/events/1734
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