Programmable logic construction kit for massive qualitative analysis of neuronal networks with an application to machine olfaction

In this thesis, a construction kit for the implementation of neuronal networks on Field Programmable Gate Arrays (FPGA) is presented. The utility of this technology for the implementation of neuronal networks becomes apparent when we show that is possible to perform hyper-real time operation, a feature which allows neuronal networks designers to analyse in great detail the dynamics of their networks through the means of a comprehensive behavioural analysis. Additionally, the construction kit presented here is used to implement a biologically inspired version of the mammalian olfactory bulb based on previous work by T. C. Pearce et al. (2005). The results we obtain show that tasks such as identification, classification and segmentation of odours are successfully performed by the olfactory bulb model. We illustrate the practical utility of the model by including experiments using real odour information. Finally, a comprehensive behavioural analysis is performed by means of a massive exploration of the characteristics of the response of the model as it was subjected to different conditions of certain parameters. Results show that as the stimulating input approaches the trained input (target odour), the response tends to reach an attractor. This comprehensive analysis, made it possible to observe the effects of the training on the model response.