Neuromorphic Engineering is, to put it briefly, the exploitation of key principles by which brains work to build artificial computing systems that work in the real world. The long-term bet is that these systems will show large advantages in terms of robustness in uncertain environments and in power efficiency.
I have been involved in a relatively small way in two projects:
- Cerebellum chip: this paper describes a hardware simulation of a part of the circuitry comprised by the cerebellum and associated deep brain nuclei. This circuit is of interest because it may be involved in the regulation of fine timing of motor responses. [Hofstötter 2004]
- Topology learning: imagine a highly distributed sensor system that is placed somewhere at random. These sensors need to work out where they are relative to the other sensors. Under certain situations, they can do this using the correlations between their sensor inputs. This paper demonstrates the learning principle using tactile data from the interactive floor used in the Ada project and the neurmorphic vision sensor we are now selling via the spin-off company iniLabs. [Boerlin 2009]