Implementing Fuzzy Reasoning on a Spiking Neural Network Cornelius Glackin, Liam McDaid, Liam Maguire, and Heather Sayers University of Ulster, Faculty of Engineering, School of Computing & Intelligent Systems, Magee Campus, Londonderry, BT48 7JL, Northern Ireland {glackin-c1,lj.mcdaid,lp.maguire,hm.sayers}@ulster.ac.uk Abstract. This paper presents a supervised training algorithm that im- plements fuzzy reasoning on a spiking neural network. Neuron selectivity is facilitated using receptive fields that enable individual neurons to be responsive to certain spike train frequencies. The receptive fields behave in a similar manner as fuzzy membership functions. The network is su- pervised but learning only occurs locally as in the biological case. The connectivity of the hidden and output layers is representative of a fuzzy rule base. The advantages and disadvantages of the network topology for the IRIS classification task are demonstrated and directions of current and future work are discussed. Key words: Spiking Neuron Model, Dynamic Synapse, Supervised Learn- ing, Receptive Field, Fuzzy Reasoning 1 Introduction The history of neural network research is characterised by a progressively greater emphasis paid to biological plausibility. The evolution of neuron modelling with regard to the complexity of computational units can be classified into three dis- tinct generations [1]. The third generation of neuron modelling (spiking neurons) is based on the realisation that the precise mechanism by which biological neu- rons encode and process information is poorly understood. The spatio-temporal distribution of spikes in biological neurons holds the key to understanding the brains neural code. There exists a multitude of spiking neuron models that can be employed in spiking neural networks (SNNs). The extensive amount and variety of neuron models exist in acknowledgement of the fact that there is a trade-off between the individual complexity of spiking neurons and the number of neurons that can be modelled in a neural network. In addition to the variety of neuron models, biological neurons can have two different roles to play in the flow of information within neural circuits. These two roles are excitatory and inhibitory respectively. Excitatory neurons are responsible for relaying information whereas inhibitory