Astrocytes are glial cells making up 50% of brain volume, and playing multiple important roles, e.g. control of synaptic transmission. We are developing tools to include “effective” astrocytes in neuronal network models in an easy-to-implement, and relatively computationally-efficient way. In our approach we first consider neuron-astrocyte interaction at fine spatial scale, and then extract essential ways in which the network is influenced by the presence of the astrocytes.

 

For example, the tightness of astrocyte wrapping (or “degree of ensheathement”) and the number of the synapses ensheathed varies by brain region and in certain disease states such as some forms of epilepsy. Do the changes in ensheathment properties contribute to the diseased state of the network or, conversely, play a protective role?  To address this question, first, we consider an individual synapse as a DiRT (Diffusion with Recharging Traps) model: diffusing particles can escape through absorbing parts of the boundary, or can be captured by traps on the boundary. We show that a synapse tightly ensheathed by an astrosyte makes neuronal connection faster, weaker, and less reliable. These influences can then be included in a neuronal network model by adding a simplified “effective” astrocyte on each synapse. We find that depending on the number of synapses ensheathed, and the ensheathment strength, the astrocytes are able to push the network to synchrony and to exhibiting strong spatial patterns, possibly contributing to epileptic disorder.