Abstract
The paper is devoted to implementation and exploration of evolutionary development of the short-term memory mechanism in spiking neural networks (SNN) starting from initial chaotic state. Short-term memory is defined here as a network ability to store information about recent stimuli in form of specific neuron activity patterns. Stable appearance of this effect was demonstrated for so called stabilizing SNN, the network model proposed by the author. In order to show the desired evolutionary behavior the network should have a specific topology determined by “horizontal” layers and “vertical” columns.
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Kiselev, M. (2011). Self-organized Short-Term Memory Mechanism in Spiking Neural Network. In: Dobnikar, A., Lotrič, U., Šter, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2011. Lecture Notes in Computer Science, vol 6593. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20282-7_13
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DOI: https://doi.org/10.1007/978-3-642-20282-7_13
Publisher Name: Springer, Berlin, Heidelberg
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