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.

翻译：先前的工作表明,有可能在多电子阵列(MEAs)上培训神经文化,以识别非常简单的模式;然而,这项工作主要侧重于表明在文化中诱发可塑性,而不是对其模式识别性能进行严格的评估;在本文件中,我们通过开发一种方法,使我们能够评估神经文化在学习任务方面的表现来弥补这一差距。具体地说,我们提议了一个真正的培养神经网络的数字模型;我们确定了生物学上可信的模拟参数,使我们能够可靠地复制真实文化的行为;我们利用模拟文化进行手写数字识别并严格评估其绩效;我们还表明,有可能为具体任务找到更好的模拟参数,从而指导真实文化的创造。