Abstract
Autonomously developing organisms face several challenges when learning
reaching movements,
such as reaching for a glass. First, motor control has to be learned
unsupervised, or selfsupervised.
Second, knowledge of sensorimotor contingencies has to be acquired
in contexts
in which action consequences unfold in time. Third, motor redundancies
need to be resolved.
To solve all three of these problems, we propose a sensorimotor, unsupervised,
redundancyresolving
control architecture (SURE REACH), which is based on the ideomotor
principle.
Given a three degree of freedom arm in a 2-D environment, SURE REACH
encodes two spatial
arm representations with neural population codes: a hand end-point
coordinate space and
an angular arm posture space. A posture memory solves the inverse
kinematics problem by
associating hand end-point neurons with neurons in posture space.
An inverse sensorimotor
model associates posture neurons with each other action-dependently.
Together, population
encoding, redundant posture memory, and inverse sensorimotor model
enable SURE REACH
to learn and represent sensorimotor grounded distance measures and
to use dynamic programming
to reach goals flexibly and efficiently. The architecture does not
only solve the redundancy
problem, but significantly increases goal reaching flexibility, accounting
for additional
task constraints or realizing obstacle avoidance. While the spatial
population codes resemble
neurophysiological structures, simulations confirm the plausibility
and flexibility of the model,
mimicking various previously published behavior data in arm reaching
tasks.
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