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With the rapid advance of modern micro- and nanoelectronics, devices become more vulnerable to failure as well as to noise. The need for fault tolerant devices and circuits, that can compensate degradation and failure by adaptation, are inherently immune to noise, and compute at low-power gives new incentive to the motivation and goals of neuromorphic computing again. In particular, the realization of sensor signal conditioning and conversion by neural circuits coexisting with potentially large digital circuits on the same substrate is an attractive field for these techniques. In our work, for the case of a selected magnetic sensor (AMR), a spike-coded circuit employing principles from the auditory system is advanced, that already meets several of the goals mentioned above. Now we achieve 6.3 bit ADC with a sampling rate of 1 MHz by using Rank Order Coding and a simple look-up table for decoding. The increase of resolution and linearity as well as introduction of adaptivity will be regarded next before considering microelectronic implementation.

P5 - Increasing the Resolution of an Integrated Adaptive Spike Coded Sensor to Digital Conversion Neuro-Circuit by an Enhanced Place Coding Layer