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Emergence of Sensory Representations Using Prediction in Partially Observable Environments

Kulak, T. and Garcia Ortiz, M. ORCID: 0000-0003-4729-7457 (2018). Emergence of Sensory Representations Using Prediction in Partially Observable Environments. In: Artificial Neural Networks and Machine Learning – ICANN 2018. Lecture Notes in Computer Science, 11140. (pp. 489-498). Springer. ISBN 978-3-030-01420-9

Abstract

n order to explore and act autonomously in an environment,an agent can learn from the sensorimotor information that is capturedwhile acting. By extracting the regularities in this sensorimotor stream,it can build a model of the world, which in turn can be used as a basis foraction and exploration. It requires the acquisition of compact representa-tions from possibly high dimensional raw observations. In this paper, wepropose a model which integrates sensorimotor information over time,and project it in a sensory representation. It is trained by preformingsensorimotor prediction. We emphasize on a simple example the role ofmotor and memory for learning sensory representations.

Publication Type: Conference or Workshop Item (UNSPECIFIED)
Additional Information: This is the accepted version of a conference paper published in Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science, vol 11140. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-01421-6_47.
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: School of Mathematics, Computer Science & Engineering > Computer Science
URI: http://openaccess.city.ac.uk/id/eprint/22451
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