01612nas a2200169   4500008003900000245010800039210006900147260001700216520098600233653001901219100001601238700001501254700001401269700001701283700002001300856012201320       2013                          d00aNovel Spiking Neuron-Astrocyte Networks based on NoNlinearTransistor-Like Models of Tripartite Synapses0 aNovel Spiking NeuronAstrocyte Networks based on NoNlinearTransis  aOsaka, Japan3 a<p style="text-align: justify;">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).</p>
10aBioengineering1 aValenza, G.1 aTedesco, L1 aLanata, A1 aDe Rossi, D.1 aScilingo, E. P.  uhttp://95.110.227.47/publications/novel-spiking-neuron-astrocyte-networks-based-nonlineartransistor-models-tripartite