Neural and Synaptic modelling on bio-inspired hardware

The presented thesis proposes to explore the implementation of different neural appli- cations, in particular, the Adaptive Exponential Integrate and Fire (aEIF) neural model on a neuromorphic device called HEENS, and a simulation of a Spiking Neural Network with a Reservoir topology, along with the comparison of the results with an analogue neural counterpart, implemented in CMOS technology. For doing so, initially, some basic concepts about neuron’s modeling and Spiking Neural Network are exposed, and then HEENS multiprocessor is introduced, both in the architecture and its software support. Afterwards, the focus is moved toward four different spiking neural models, explaining some theory and their equations, and for one of them, also the HEENS implementation. Lastly, a comparison between an analogue and a digital technologies implementing the same model over a reservoir network topology is discussed, presenting similarities and differences of the two approaches.