@conference {1813,
	title = {Novel Spiking Neuron-Astrocyte Networks based on NoNlinearTransistor-Like Models of Tripartite Synapses},
	booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC{\textquoteright}13)},
	year = {2013},
	address = {Osaka, Japan},
	abstract = {<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>
},
	keywords = {Bioengineering},
	author = {G. Valenza and L. Tedesco and A Lanata and D. De Rossi and E. P. Scilingo}
}
