01626nas a2200169 4500008003900000245010800039210006900147260001700216520098600233653001901219100001601238700001501254700001401269700001701283700002001300856013601320 2013 d00aNovel Spiking Neuron-Astrocyte Networks based on NoNlinearTransistor-Like Models of Tripartite Synapses0 aNovel Spiking NeuronAstrocyte Networks based on NoNlinearTransis aOsaka, Japan3 a
In this paper a novel and efficient computational implementation of a Spiking Neuron-Astrocyte Network (SNAN) is reported. Neurons are modeled according to the Izhikevich formulation and the neuron-astrocyte interactions are intended as tripartite synapsis and modeled with the previ- ously proposed nonlinear transistor-like model. Concerning the learning rules, the original spike-timing dependent plasticity is used for the neural part of the SNAN whereas an ad-hoc rule is proposed for the astrocyte part. SNAN performances are compared with a standard spiking neural network (SNN) and evaluated using the polychronization concept, i.e., number of co-existing groups that spontaneously generate patterns of polychronous activity. The astrocyte-neuron ratio is the biologically inspired value of 1.5. The proposed SNAN shows higher number of polychronous groups than SNN, remarkably achieved for the whole duration of simulation (24 hours).
10aBioengineering1 aValenza, G.1 aTedesco, L1 aLanata, A1 aDe Rossi, D.1 aScilingo, E. P. uhttps://www.centropiaggio.unipi.it/publications/novel-spiking-neuron-astrocyte-networks-based-nonlineartransistor-models-tripartite