Abstract
We propose a model of the hippocampus aimed at learning the timed association between subsequent sensory events. The properties of the neural network allow it to learn and predict the evolution of continuous rate-coded signals as well as the occurrence of transitory events, using both spatial and non-spatial information. The system is able to provide predictions based on the time trace of past sensory events. Performance of the neural network in the precise temporal learning of spatial and non-spatial signals is tested in a simulated experiment. The ability of the hippocampus proper to predict the occurrence of upcoming spatio-temporal events could play a crucial role in the carrying out of tasks requiring accurate time estimation and spatial localization.
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Hirel, J., Gaussier, P., Quoy, M. (2010). Model of the Hippocampal Learning of Spatio-temporal Sequences. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds) Artificial Neural Networks – ICANN 2010. ICANN 2010. Lecture Notes in Computer Science, vol 6354. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15825-4_46
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DOI: https://doi.org/10.1007/978-3-642-15825-4_46
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