In our earlier work, we introduced the concept of Gene Regulatory Neural Network (GRNN), which utilizes natural neural network-like structures inherent in biological cells to perform computing tasks using chemical inputs. We define this form of chemical-based neural network as Wet TinyML. The GRNN structures are based on the gene regulatory network and have weights associated with each link based on the estimated interactions between the genes. The GRNNs can be used for conventional computing by employing an application-based search process similar to the Network Architecture Search. This study advances this concept by incorporating cell plasticity, to further exploit natural cell's adaptability, in order to diversify the GRNN search that can match larger spectrum as well as dynamic computing tasks. As an example application, we show that through the directed cell plasticity, we can extract the mathematical regression evolution enabling it to match to dynamic system applications. We also conduct energy analysis by comparing the chemical energy of the GRNN to its silicon counterpart, where this analysis includes both artificial neural network algorithms executed on von Neumann architecture as well as neuromorphic processors. The concept of Wet TinyML can pave the way for the new emergence of chemical-based, energy-efficient and miniature Biological AI.

翻译：在先前的工作中，我们提出了基因调控神经网络（GRNN）的概念，该网络利用生物细胞中固有的类自然神经网络结构，通过化学输入执行计算任务。我们将这种基于化学的神经网络形式定义为湿TinyML。GRNN结构基于基因调控网络，并根据基因间估计的相互作用为每个连接赋予权重。通过采用类似网络架构搜索的基于应用的搜索过程，GRNN可用于常规计算。本研究通过引入细胞可塑性进一步推进这一概念，以更充分地利用自然细胞的适应性，从而扩展GRNN搜索范围，使其能够匹配更广泛的动态计算任务。作为示例应用，我们展示了通过定向细胞可塑性提取数学回归演化过程，从而使其能够适配动态系统应用。我们还通过比较GRNN与其硅基对应物的化学能量进行了能量分析，该分析涵盖了在冯·诺依曼架构和神经形态处理器上执行的人工神经网络算法。湿TinyML这一概念为新型节能、微型化化学基生物人工智能的出现铺平了道路。