Network of evolvable neural units can learn synaptic learning rules and spiking dynamics

Paul Bertens, Seong Whan Lee

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


Although deep neural networks have seen great success in recent years through various changes in overall architectures and optimization strategies, their fundamental underlying design remains largely unchanged. Computational neuroscience may provide more biologically realistic models of neural processing mechanisms, but they are still high-level abstractions of empirical behaviour. Here we propose an evolvable neural unit (ENU) that can evolve individual somatic and synaptic compartment models of neurons in a scalable manner. We demonstrate that ENUs can evolve to mimic integrate-and-fire neurons and synaptic spike-timing-dependent plasticity. Furthermore, by constructing a network where an ENU takes the place of each synapse and neuron, we evolve an agent capable of learning to solve a T-maze environment task. This network independently discovers spiking dynamics and reinforcement-type learning rules, opening up a new path towards biologically inspired artificial intelligence.

Original languageEnglish
Pages (from-to)791-799
Number of pages9
JournalNature Machine Intelligence
Issue number12
Publication statusPublished - 2020 Dec

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications
  • Human-Computer Interaction
  • Software


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