No abstract

Astrocytes, the predominant glial cell type in the brain, were traditionally considered as merely passive supportive cells without any important roles in synaptic information processing. In contrast, the contemporary view was given rise to show that astrocytes play active roles in synaptic neurotransmission and information processing. Hence, recently two terms have been emerged, tripartite synapse, to describe the communication between an astrocyte and two neurons, and the term astrocytic syncytium or astrocytic network to describe the communication among the astrocytes by gap junction. Therefore, we propose mathematical models for tripartite synapseand astrocytic syncytium based on twostate kinetics models, several probabilistic methods and Spiking Neural Network (SNN) to introduce new Artificial Astrocytic Syncytium (AAS) model. The simulation results have shown that proposed model could represent the cellular intrinsic properties of astrocyte based on the spatial and temporal aspects to emulate the astrocytic network functions related to cognitive, learning and memory.

In this paper we propose simple recurrent network (SRN) and mathematical paradigm to model real time interaction of astrocyte in simplified spiking neural network (SRM0). Both Elman neural network and Jordan neural network are used in the proposed network. Elman neural network is connected to the synapse and Jordan neural network is connected to the postsynaptic neuron in tripartite synapse, the simulation results showed that whenever astrocytes were activating in a time window, more spikes will be fired for excitatory neurons. This is biologically justified since astrocytes induce excitability with variations in Ca concentrations.