In this paper, we present a neuro-inspired approach to reservoir computing (RC) in which a network of in vitro cultured cortical neurons serves as the physical reservoir. Rather than relying on artificial recurrent models to approximate neural dynamics, our biological reservoir computing (BRC) system leverages the spontaneous and stimulus-evoked activity of living neural circuits as its computational substrate. A high-density multi-electrode array (HD-MEA) provides simultaneous stimulation and readout across hundreds of channels: input patterns are delivered through selected electrodes, while the remaining ones capture the resulting high-dimensional neural responses, yielding a biologically grounded feature representation. A linear readout layer (single-layer perceptron) is then trained to classify these reservoir states, enabling the living neural network to perform static visual pattern-recognition tasks within a computer-vision framework. We evaluate the system across a sequence of tasks of increasing difficulty, ranging from pointwise stimuli to oriented bars, clock-digit-like shapes, and handwritten digits from the MNIST dataset. Despite the inherent variability of biological neural responses-arising from noise, spontaneous activity, and inter-session differences-the system consistently generates high-dimensional representations that support accurate classification. These results demonstrate that in vitro cortical networks can function as effective reservoirs for static visual pattern recognition, opening new avenues for integrating living neural substrates into neuromorphic computing frameworks. More broadly, this work contributes to the effort to incorporate biological principles into machine learning and supports the goals of neuro-inspired vision by illustrating how living neural systems can inform the design of efficient and biologically grounded computational models.

翻译：本文提出了一种神经启发的储备池计算（RC）方法，其中体外培养的皮层神经元网络作为物理储备池。与依赖人工循环模型来近似神经动力学不同，我们的生物储备池计算（BRC）系统利用活体神经回路自发的及刺激诱发的活动作为其计算基底。高密度多电极阵列（HD-MEA）通过数百个通道提供同步刺激与读出：输入模式通过选定电极传递，而其余电极则捕获由此产生的高维神经响应，从而形成一种基于生物学的特征表示。随后训练一个线性读出层（单层感知机）对这些储备池状态进行分类，使得活体神经网络能够在计算机视觉框架内执行静态视觉模式识别任务。我们在一个难度递增的任务序列上评估该系统，任务范围从点状刺激到定向条形、类时钟数字形状以及来自MNIST数据集的手写数字。尽管生物神经响应存在固有的变异性（源于噪声、自发活动及不同记录会话间的差异），该系统仍能持续生成支持精确分类的高维表示。这些结果表明，体外皮层网络能够作为静态视觉模式识别的有效储备池，为将活体神经基底整合到神经形态计算框架中开辟了新途径。更广泛而言，这项工作致力于将生物学原理融入机器学习，并通过展示活体神经系统如何为设计高效且基于生物学的计算模型提供启示，支持了神经启发视觉的研究目标。