  Previous work has shown that it is possible to train neuronal cultures on
Multi-Electrode Arrays (MEAs), to recognize very simple patterns. However, this
work was mainly focused to demonstrate that it is possible to induce plasticity
in cultures, rather than performing a rigorous assessment of their pattern
recognition performance. In this paper, we address this gap by developing a
methodology that allows us to assess the performance of neuronal cultures on a
learning task. Specifically, we propose a digital model of the real cultured
neuronal networks; we identify biologically plausible simulation parameters
that allow us to reliably reproduce the behavior of real cultures; we use the
simulated culture to perform handwritten digit recognition and rigorously
evaluate its performance; we also show that it is possible to find improved
simulation parameters for the specific task, which can guide the creation of
real cultures.
