Abstract
Neuromorphic engineering is a discipline of electrical engineering that aims to develop signal processing systems using biological neural systems as inspiration. As such it has the dual goal of building models of neural systems in order to better understand them, and of building systems to perform useful functions for particular applications. While neuromorphic engineers extensively develop and use computer models of neural systems, which are generally less expensive and faster to create, the main goal is to implement the models in electronic hardware. The greatest advantage of a hardware model is that it gives solutions in real time. Where it may take hours and even days to simulate a small time period in a complex software model, results from a hardware model are obtained instantly. As computational power increases, neural models are also becoming more and more complex, so that for the foreseeable future there is a need for hardware implementations to allow models to interact with the world in real time. An often underestimated benefit of real-time operation is that in watching the behavior of the system change during the tuning of parameters, the researcher develops a much better intuition about the influence of these parameters. In addition to real-time operation, hardware models suffer from unavoidable mismatch and noise, just like neural systems. This forces the models to be robust to noise and mismatch, which is not the case for computer models, where noise and mismatch would have to be specifically added.
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van Schaik, A., Hamilton, T.J., Jin, C. (2010). Silicon Models of the Auditory Pathway. In: Meddis, R., Lopez-Poveda, E., Fay, R., Popper, A. (eds) Computational Models of the Auditory System. Springer Handbook of Auditory Research, vol 35. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-5934-8_10
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