On Applications of Cellular Automata Memristor Networks for Reservoir Computing: Classifying Protein Toxicity
Ignacio Del Amo and Z. Konkoli
We explore the computing capacity of large memristor networks in a reservoir computing setup. Memristor networks are modelled as random cellular automata networks. It is inevitable that the cellular automata model does not describe properly certain aspects of memristor dynamics. However, owing to the simplicity of the model one can explore extremely large memristor networks and specifically focus on issues related to the network topology. To investigate the computing capacity of such systems we studied a challenging classification problem. The goal was to distinguish whether a given protein sequence resembles a toxin or not. Different network topologies have been investigated with the overarching goal of understanding how the topology of the memristor network relates to its information processing capacity, and ultimately affects the accuracy of the prediction. We demonstrate the existence of “sweet spots” in the space of network topologies. There are network structures that can generalize very well and are robust with regard to the change in the data set.
Keywords: Reservoir computing, cellular automata, protein classification, memristor networks, network models