Self-organized Neural Representation of Time
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.
Unable to display preview. Download preview PDF.
- 1.Bueti, D.: The sensory representation of time. Frontiers in Integrative Neuroscience 5(34) (2011)Google Scholar
- 4.Gibbon, J., Church, R., Meck, W.: Scalar timing in memory. In: Gibbon, J., Allan, L.G. (eds.) Timing and Time Perception, pp. 52–77. New York Academy of Sciences, New York (1984)Google Scholar
- 8.Laje, R., Cheng, K., Buonomano, D.: Learning of temporal motor patterns: an analysis of continuous versus reset timing. Front. Integr. Neurosc. 5(61) (2011)Google Scholar
- 9.Maniadakis, M., Trahanias, P.: Temporal cognition: a key ingredient of intelligent systems. Frontiers in Neurorobotics 5 (2011)Google Scholar
- 11.Maniadakis, M., Wittmann, M., Trahanias, P.: Time experiencing by robotic agents. In: Proc. 11th European Symposium on Artificial Neural Networks (2011)Google Scholar