Involving motor capabilities in the formation of sensory space representations.
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Involving motor capabilities in the formation of sensory space representations. / Weiller, Daniel; Märtin, Robert; Dähne, Sven; Engel, Andreas K.; König, Peter.
In: PLOS ONE, Vol. 5, No. 4, 4, 2010, p. 10377.Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review
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TY - JOUR
T1 - Involving motor capabilities in the formation of sensory space representations.
AU - Weiller, Daniel
AU - Märtin, Robert
AU - Dähne, Sven
AU - Engel, Andreas K.
AU - König, Peter
PY - 2010
Y1 - 2010
N2 - A goal of sensory coding is to capture features of sensory input that are behaviorally relevant. Therefore, a generic principle of sensory coding should take into account the motor capabilities of an agent. Up to now, unsupervised learning of sensory representations with respect to generic coding principles has been limited to passively received sensory input. Here we propose an algorithm that reorganizes an agent's representation of sensory space by maximizing the predictability of sensory state transitions given a motor action. We applied the algorithm to the sensory spaces of a number of simple, simulated agents with different motor parameters, moving in two-dimensional mazes. We find that the optimization algorithm generates compact, isotropic representations of space, comparable to hippocampal place fields. As expected, the size and spatial distribution of these place fields-like representations adapt to the motor parameters of the agent as well as to its environment. The representations prove to be well suited as a basis for path planning and navigation. They not only possess a high degree of state-transition predictability, but also are temporally stable. We conclude that the coding principle of predictability is a promising candidate for understanding place field formation as the result of sensorimotor reorganization.
AB - A goal of sensory coding is to capture features of sensory input that are behaviorally relevant. Therefore, a generic principle of sensory coding should take into account the motor capabilities of an agent. Up to now, unsupervised learning of sensory representations with respect to generic coding principles has been limited to passively received sensory input. Here we propose an algorithm that reorganizes an agent's representation of sensory space by maximizing the predictability of sensory state transitions given a motor action. We applied the algorithm to the sensory spaces of a number of simple, simulated agents with different motor parameters, moving in two-dimensional mazes. We find that the optimization algorithm generates compact, isotropic representations of space, comparable to hippocampal place fields. As expected, the size and spatial distribution of these place fields-like representations adapt to the motor parameters of the agent as well as to its environment. The representations prove to be well suited as a basis for path planning and navigation. They not only possess a high degree of state-transition predictability, but also are temporally stable. We conclude that the coding principle of predictability is a promising candidate for understanding place field formation as the result of sensorimotor reorganization.
U2 - 10.1371/journal.pone.0010377
DO - 10.1371/journal.pone.0010377
M3 - SCORING: Zeitschriftenaufsatz
VL - 5
SP - 10377
JO - PLOS ONE
JF - PLOS ONE
SN - 1932-6203
IS - 4
M1 - 4
ER -
