Abstract
Time is crucially involved in most of the activities of humans and animals. However, the cognitive mechanisms that support experiencing and processing time remain largely unknown. In the present work we follow a self-organized connectionist modeling approach to study how time may be encoded in a neural network based cognitive system in order to provide suggestions for possible time processing mechanisms in the brain. A particularly interesting feature of our study regards the implementation of a single computational model to accomplish two different robotic behavioral tasks which assume diverse manipulation of time intervals. Examination of the implemented cognitive systems revealed that it is possible to integrate the main theoretical models of time representation existing today into a new and particularly effective theory that can sufficiently explain a series of neuroscientific observations.
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Maniadakis, M., Trahanias, P. (2013). Self-organized Neural Representation of Time. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42054-2_10
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DOI: https://doi.org/10.1007/978-3-642-42054-2_10
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