化学玻尔兹曼机
Chemical Boltzmann Machines
摘要：
一个微米大小的化学品袋子能有多聪明？人造或真实的细胞如何对其环境做出推断？化学反应网络可以从哪种概率分布中采样？我们通过展示随机化学反应网络实现玻尔兹曼机的四种方式来开始解决这些问题，玻尔兹曼机是一种随机神经网络模型，可以生成各种概率分布并计算条件概率。由此产生的模型，以及相关的定理，提供了一个路线图，利用其天然的随机性作为计算资源，构建化学反应网络。最后，为了展示我们的模型的潜力，我们模拟了一个化学玻尔兹曼机来分类和生成MNIST数字。
英文摘要：
How smart can a micron-sized bag of chemicals be? How can an artificial or real cell make inferences about its environment? From which kinds of probability distributions can chemical reaction networks sample? We begin tackling these questions by showing four ways in which a stochastic chemical reaction network can implement a Boltzmann machine, a stochastic neural network model that can generate a wide range of probability distributions and compute conditional probabilities. The resulting models, and the associated theorems, provide a road map for constructing chemical reaction networks that exploit their native stochasticity as a computational resource. Finally, to show the potential of our models, we simulate a chemical Boltzmann machine to classify and generate MNIST digits in-silico.
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。
