评估生物神经网络中闭环分类的编码策略
Evaluating Encoding Strategies for Closed-Loop Classification in Biological Neural Networks
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中文总结 AI 辅助
研究在闭环神经分类任务中比较多种编码策略，发现基于脉冲的时间编码性能最高，在二元分类任务中准确率达95.6%，还表明性能对刺激空间分布敏感，强调有效交互需联合优化时空编码策略及时间编码的关键作用。
中文摘要 AI 辅助
与生物神经网络（BNN）交互需要将信息编码为可有效处理并能使底层系统适应的刺激模式。然而，刺激编码的作用仍知之甚少。本文在使用培养的BNN的闭环神经分类任务中比较了多种编码策略，包括基于速率、相位、脉冲和首次放电时间的时间编码。将视觉输入编码为通过多电极阵列（MEA）传递的时空刺激模式，并评估每种编码方案的分类性能。发现基于脉冲的时间编码性能最高，在二元分类任务中准确率达95.6%，而基于速率和相位的方法性能低得多。还表明性能对刺激的空间分布高度敏感，电极选择不佳会显著降低准确率。这些发现表明与生物神经系统有效交互需要联合优化时间和空间编码策略，并突出了时间编码作为生物数字计算的关键设计维度。
英文摘要
Interfacing with Biological Neural Networks (BNNs) requires encoding information into stimulation patterns that can be effectively processed and that enable the underlying system to adapt. Nevertheless, the role of stimulation encoding remains poorly understood. In this work, we compare multiple encoding strategies, including rate-based, phase-based, burst-based, and time-to-first-spike temporal encodings, in a closed-loop neural classification task using cultured BNNs. We encode visual inputs as spatiotemporal stimulation patterns delivered via a Multi-Electrode Array (MEA) and evaluate classification performance for each encoding scheme. We find that burst-based temporal encoding yields the highest observed performance, achieving up to 95.6 % accuracy in a binary classification task, compared to substantially lower performance from rate- and phase-based approaches. We further show that performance is highly sensitive to the spatial distribution of stimulation, with suboptimal electrode selection significantly degrading accuracy. These findings indicate that effective interfacing with biological neural systems requires the joint optimization of temporal and spatial encoding strategies, and highlight temporal encoding as a key design dimension for bio-digital computing.
