FIAS Scientific Report 2011 - Frankfurt Institute for Advanced Studies ...

Unifying procedural memory consolidation and structure learning in a recurrent network
model of motor control
Collaborators: Q. Wang 1 , C.A. Rothkopf 1 , J. Triesch 1
1 Frankfurt Institute for Advanced Studies
Humans can improve their performance in movement sequence tasks through practice, but such motor learning
has shown a wide variety of puzzling and partly contradictory effects. Blocked training of multiple sequences
has been shown to lead to reduced retention compared to interleaved training and a wide variety of proactive
and retroactive facilitation and interference effects have been observed. Furthermore, recent studies have shown
that transfer of learning between different tasks is based on structural task similarities. Here we address the
question of how these phenomena can be understood in terms of the shaping of neuronal representations through
different plasticity mechanisms in a recurrent network model.
We use a sparsely connected recurrent network whose connectivity is shaped by STDP, intrinsic plasticity, and
synaptic scaling. Additionally, this network is connected to a layer of motor neurons mediating the movement
sequence. We apply this network to a series of experiments about movement sequence learning and use a single
set of parameters in all simulations. The network learns to carry out the correct movement sequences over trials
and shows striking similarity to the human behavior in a variety of training schedules over different sequence
similarities.
We show how psychophysical performance measures are reflective of the underlying neuronal representations
in the recurrent network. These results are interpreted in terms of the changes in the neuronal sequence representations
and testable predictions for further experiments are derived. Specifically, we show how sequence
similarity and training schedule interact to produce a rich set of interference and facilitation effects thereby
unifying structure learning and procedural consolidation.
Left: Illustration of reservoir computing with recurrent network. Center: Empirical data (Panzer et al., 2006). Right:
Simulation results showing facilitation for sequence 2 and retroactive interference for sequence 1.
Related publications in 2011:
1) Q. Wang, C. A.Rothkopf, J. Triesch, Unifying procedural memory consolidation and structure learning in
motor control, COSYNE - Computational and Systems Neuroscience, February 24-27, 2011, Salt Lake City,
Utah, USA.
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