通过精确模拟评估神经元培养的模式识别性能
Assessing Pattern Recognition Performance of Neuronal Cultures through Accurate Simulation
摘要：
先前的工作已经表明，在多电极阵列（MEA）上训练神经元培养物识别非常简单的模式是可能的。然而，这项工作主要集中在证明有可能在文化中诱导可塑性，而不是对其模式识别性能进行严格的评估。在本文中，我们通过开发一种方法来解决这一差距，使我们能够评估神经元文化的学习任务的性能。具体来说，我们提出了一个数字模型的真正培养的神经元网络;我们确定生物学上合理的模拟参数，使我们能够可靠地再现真实文化的行为;我们使用模拟文化进行手写数字识别，并严格评估其性能;我们还表明，它是可能的，以找到改进的模拟参数的特定任务，这可以指导创建真正的文化。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
