  Based on bottom-up assembly of highly variable neural cells units, the
nervous system can reach unequalled level of performances with respect to
standard materials and devices used in microelectronic. Reproducing these basic
concepts in hardware could potentially revolutionize materials and device
engineering which are used for information processing. Here, we present an
innovative approach that relies on both iono-electronic materials and intrinsic
device physics to show pattern classification out of a 12-unit bio-sensing
array. We use the reservoir computing and learning concept to demonstrate
relevant computing based on the ionic dynamics in 400-nm channel-length organic
electrochemical transistor (OECT). We show that this approach copes efficiently
with the high level of variability obtained by bottom-up fabrication using a
new electropolymerizable polymer, which enables iono-electronic device
functionality and material stability in the electrolyte. We investigate the
effect of the array size and variability on the performances for a real-time
classification task paving the way to new embedded sensing and processing
approaches.
