Involving Motor Capabilities in the Formation of Sensory Space Representations

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

The influence of motor parameters on the average Ψ of optimized macrostate configurations.

<p>The influence of motor parameters on the average Ψ of optimized macrostate configurations.</p

Comparison of the predictability and decorrelation of state configurations generated with and without the knowledge of the agent's motor capabilities.

<p>The horizontal lines in each box represent the 25^th, 50^th (median) and 75^th percentiles, while the black whisker bars mark the range of the data.</p

The relation between the objective function Ψ, predictability and decorrelation for one exemplary optimization process.

<p>Each point represents the average of these measures for one iteration step of the optimization run. The two insets show the spatial layout of the macrostate configurations at selected iterations where each macrostate identity is coded by a color.</p

The influence of motor parameters on the average macrostate area of the optimized configurations.

<p>The influence of motor parameters on the average macrostate area of the optimized configurations.</p

Influence of the weighting factor β (equation [1]) on the predictability and decorrelation values of the optimized macrostate.

<p>The black line represents the mean predictability (A) and decorrelation values (B) over all optimization runs for the corresponding β values. The grey regions represent the area within ± one standard deviation around the mean values.</p

Exemplary control macrostate configuration.

<p>A control macrostate configuration generated by randomly placing 50 Gaussian curves within the environment and applying a winner-take-all operation. Each color represents a different macrostate. Although the small differences in the hues in the display might be interpreted otherwise, all macrostates are simply connected.</p