Hebbian theory seeks to explain how the neurons in the brain adapt to
stimuli, to enable learning. An interesting feature of Hebbian learning is that
it is an unsupervised method and as such, does not require feedback, making it
suitable in contexts where systems have to learn autonomously. This paper
explores how molecular systems can be designed to show such proto-intelligent
behaviours, and proposes the first chemical reaction network (CRN) that can
exhibit autonomous Hebbian learning across arbitrarily many input channels. The
system emulates a spiking neuron, and we demonstrate that it can learn
statistical biases of incoming inputs. The basic CRN is a minimal,
thermodynamically plausible set of micro-reversible chemical equations that can
be analysed with respect to their energy requirements. However, to explore how
such chemical systems might be engineered de novo, we also propose an extended
version based on enzyme-driven compartmentalised reactions. Finally, we also
show how a purely DNA system, built upon the paradigm of DNA strand
displacement, can realise neuronal dynamics. Our analysis provides a compelling
blueprint for exploring autonomous learning in biological settings, bringing us
closer to realising real synthetic biological intelligence.

论文探讨了如何设计分子系统以展现类似于自主学习的行为，提出了一种可对任意多个输入通道展现自主型 Hebb 学习的化学反应网络，并指出在构建新的化学系统和 DNA 系统中如何实现神经元动力学。

编程分子系统以模拟学习尖峰神经元

Programming molecular systems to emulate a learning spiking neuron

In this paper, we propose an optimization-based sparse learning approach to
identify the set of most influential reactions in a chemical reaction network.
This reduced set of reactions is then employed to construct a reduced chemical
reaction mechanism, which is relevant to chemical interaction network modeling.
The problem of identifying influential reactions is first formulated as a
mixed-integer quadratic program, and then a relaxation method is leveraged to
reduce the computational complexity of our approach. Qualitative and
quantitative validation of the sparse encoding approach demonstrates that the
model captures important network structural properties with moderate
computational load.

本文提出了基于优化的稀疏学习方法，以识别化学反应网络中最具影响力的反应集。该方法减少了反应集，并用于构建与化学相互作用网络建模相关的化学反应机制。我们将识别关键反应问题首先制定为混合整数二次规划问题，然后利用松弛方法降低了方法的计算复杂度。对稀疏编码方法的定性和定量验证表明，该模型以适度的计算负担捕获重要的网络结构属性。