ETH Zürich, Computational Physics for Engineering Materials, IfB, Wolfgang-Pauli-Strasse 27, 8093, Züurich, Switzerland.
Department of Industrial and Information Engineering, University of Campania "Luigi Vanvitelli", 81031, Aversa(CE), Italy.
Sci Rep. 2017 Sep 8;7(1):11016. doi: 10.1038/s41598-017-11424-5.
Recent studies have proposed that the diffusion of messenger molecules, such as monoamines, can mediate the plastic adaptation of synapses in supervised learning of neural networks. Based on these findings we developed a model for neural learning, where the signal for plastic adaptation is assumed to propagate through the extracellular space. We investigate the conditions allowing learning of Boolean rules in a neural network. Even fully excitatory networks show very good learning performances. Moreover, the investigation of the plastic adaptation features optimizing the performance suggests that learning is very sensitive to the extent of the plastic adaptation and the spatial range of synaptic connections.
最近的研究表明，信使分子（如单胺类物质）的扩散可以介导神经网络监督学习中突触的可塑性适应。基于这些发现，我们开发了一种神经学习模型，其中假设可塑性适应的信号通过细胞外空间传播。我们研究了在神经网络中学习布尔规则的条件。即使是完全兴奋性的网络也表现出非常好的学习性能。此外，对优化性能的可塑性适应特征的研究表明，学习对可塑性适应的程度和突触连接的空间范围非常敏感。
