Spiking Neural Networks for Reconfigurable POEtic Tissue

Vertebrate and most invertebrate organisms interact with their environment through processes of adaptation and learning. Such processes are generally controlled by complex networks of nerve cells, or neurons, and their interactions. Neurons are characterized by all-or-none discharges—the spikes— and the time series corresponding to the sequences of the discharges—the spike trains — carry most of the information used for intercellular communication. This paper describes biologically inspired spiking neural network models suitable for digital hardware implementation. We consider bio-realism, hardware friendliness, and performance as factors which influence the ability of these models to integrate into a flexible computational substrate inspired by evolutionary, developmental and learning aspects of living organisms. Both software and hardware simulations have been used to assess and compare the different models to determine the most suitable spiking neural network model.