Abstract
Several computational models have been designed to help our understanding of the conditions under which persistent activity can be sustained in cortical circuits during working memory tasks. Here we focus on one such model that has shown promise, that uses polychronization and short term synaptic dynamics to achieve this reverberation, and explore it with respect to different physiological parameters in the brain, including size of the network, number of synaptic connections, small-world connectivity, maximum axonal conduction delays, and type of cells (excitatory or inhibitory). We show that excitation and axonal conduction delays greatly affect the sustainability of spatio-temporal patterns of spikes called polychronous groups.
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Ioannou, P., Casey, M., Grüning, A. (2014). Factors Influencing Polychronous Group Sustainability as a Model of Working Memory. In: Wermter, S., et al. Artificial Neural Networks and Machine Learning – ICANN 2014. ICANN 2014. Lecture Notes in Computer Science, vol 8681. Springer, Cham. https://doi.org/10.1007/978-3-319-11179-7_91
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DOI: https://doi.org/10.1007/978-3-319-11179-7_91
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