POLLACK PERIODICA An International Journal for Engineering and Information Sciences DOI: 10.1556/Pollack.4.2009.3.13 Vol. 4, No. 3, pp. 143–154 (2009) www.akademiai.com HU ISSN 1788–1994 © 2009 Akadémiai Kiadó, Budapest EMBEDDED NEURAL CONTROLLERS BASED ON SPIKING NEURON MODELS László BAKÓ, Sándor Tihamér BRASSAI Department of Electrical Engineering, Faculty of Technical and Human Sciences Tirgu-Mures, Sapientia - Hungarian University of Transylvania, Calea Sighisoarei 1C Corunca, Romania, e-mail: lbako@ms.sapientia.ro Received 12 January 2009; accepted 20 March 2009 Abstract: This paper demonstrates, that input patterns can be encoded in the synaptic weights by local Hebbian delay-learning of spiking neurons (SN), where, after learning, the firing time of an output neuron reflects the distance of the evaluated pattern to its learned input pattern thus realizing a kind of RBF behavior. Furthermore, the paper shows, that temporal spike-time coding and Hebbian learning is a viable means for unsupervised computation in a network of SNs, as the network is capable of clustering realistic data. Then, two versions - with and without embedded micro-controllers - of a SNN are implemented for the aforementioned task. Keywords: Embedded systems, Hardware/software co-design, Spiking neural networks, FPGA 1. Introduction The neuron models involved in Spiking Neural Networks (SNNs) are typically more complex than in conventional rate-coded artificial neural networks and the information passed between these neurons is expressed as temporally separated discrete events or spikes. SNNs and Pulse-Coded Neural Networks can generate behaviors and reproduce coding schemes closely analogous to biological neural systems. On the other hand, these types of neural models may present important advantages in terms of digital hardware implementation, due to the fact, that timing delay coding offers considerable resource utilization gains and it is more robust than the analog implementation [1], [2]. An interesting proposition related to the possible applications of networks of spiking