In trying to mimic biological functions of the brain, artificial neural network (ANN) research has, out of computational necessity, made a number of assumptions. Firstly, it is assumed that the complexity of biological processes can be usefully replicated artificially by abstracting a relatively few key or essential characteristics from the biological system. Secondly, it is often assumed that a single entity, the neuron, is solely responsible for biological cognitive processing or computation. Thirdly, it is also often assumed that this processing is entirely dependant on microscopic factors within the neuron. Recent research using spiking neural networks (SNNs) has addressed the first assumption, highlighting that emphasizing alternative biological functionality may afford massive computational gain. In an attempt to address the last two assumptions, the authors propose that the glial network may be acting as a feature extraction network in a way that is similar to the function of a reservoir computer.