Abstract
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.
Highlights
In order to predict neuronal response properties on the basis of their sensory input, a number of generic sensory coding principles have been proposed
Optimized macrostate configurations were analyzed with respect to their topographical properties, tested for their usefulness as a basis of path planning and navigation, and compared to states generated by a temporal stability objective
Usefulness for Planning & Navigation Are macrostate configurations that are generated by our predictability-optimization algorithm useful to an agent that is actively engaging with its environment? We investigated whether these representations form a suitable basis for path planning and navigation, and compared their performance to that of control maps generated by randomly distributing compact macrostates
Summary
In order to predict neuronal response properties on the basis of their sensory input, a number of generic sensory coding principles have been proposed. The most prominent are sparse coding [1] and temporal coherence [2,3] The former is closely related to efficient coding and redundancy reduction [4], the latter to slow feature analysis and stability [5,6]. These principles successfully capture some characteristic neuronal properties of early sensory cortices [7,8,9]. The description of neuronal response properties by quantitatively defined principles has proven to be quite successful in furthering our understanding of sensory processing [11]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
