论文标题
热力学一致的化学尖峰神经元，能够自动hebbian学习
A thermodynamically consistent chemical spiking neuron capable of autonomous Hebbian learning
论文作者
论文摘要
我们提出了一组完全自主的，热力学一致的化学反应集，该反应实现了尖峰神经元。该化学神经元能够以Hebbian的方式学习输入模式。该系统可扩展到任意许多输入通道。我们证明了其在输入中的学习频率偏见以及不同输入通道之间的相关性方面的性能。时间相关的有效计算需要高度非线性激活函数。讨论了非线性激活函数的资源要求。除了CN的热力学一致模型外，我们还提出了一个可以在合成生物学环境中设计的生物学上合理的版本。
We propose a fully autonomous, thermodynamically consistent set of chemical reactions that implements a spiking neuron. This chemical neuron is able to learn input patterns in a Hebbian fashion. The system is scalable to arbitrarily many input channels. We demonstrate its performance in learning frequency biases in the input as well as correlations between different input channels. Efficient computation of time-correlations requires a highly non-linear activation function. The resource requirements of a non-linear activation function are discussed. In addition to the thermodynamically consistent model of the CN, we also propose a biologically plausible version that could be engineered in a synthetic biology context.
