In this paper, we present a biologically grounded approach to reservoir computing (RC), in which a network of cultured biological neurons serves as the reservoir substrate. This system, referred to as biological reservoir computing (BRC), replaces artificial recurrent units with the spontaneous and evoked activity of living neurons. A multi-electrode array (MEA) enables simultaneous stimulation and readout across multiple sites: inputs are delivered through a subset of electrodes, while the remaining ones capture the resulting neural responses, mapping input patterns into a high-dimensional biological feature space. We evaluate the system through a case study on digit classification using a custom dataset. Input images are encoded and delivered to the biological reservoir via electrical stimulation, and the corresponding neural activity is used to train a simple linear classifier. To contextualize the performance of the biological system, we also include a comparison with a standard artificial reservoir trained on the same task. The results indicate that the biological reservoir can effectively support classification, highlighting its potential as a viable and interpretable computational substrate. We believe this work contributes to the broader effort of integrating biological principles into machine learning and aligns with the goals of human-inspired vision by exploring how living neural systems can inform the design of efficient and biologically plausible models.

翻译：本文提出了一种基于生物学的储备池计算方法，其中培养的生物神经元网络作为储备池基质。该系统被称为生物储备池计算，用活体神经元的自发和诱发活动替代了人工循环单元。多电极阵列能够实现跨多个位点的同步刺激与读取：输入通过部分电极传递，而其余电极则捕获相应的神经响应，从而将输入模式映射到高维生物特征空间。我们通过使用定制数据集进行数字分类的案例研究来评估该系统。输入图像经过编码后通过电刺激传递至生物储备池，相应的神经活动被用于训练简单的线性分类器。为评估生物系统的性能表现，我们还将其与在相同任务上训练的标准人工储备池进行了对比。结果表明，生物储备池能有效支持分类任务，凸显了其作为一种可行且可解释的计算基质的潜力。我们相信这项工作有助于推动将生物学原理整合到机器学习中的更广泛努力，并通过探索活体神经系统如何为高效且生物可信的模型设计提供启示，与人类启发的视觉研究目标相契合。