Reconfigurable logic gates in biological crossbar neural networks using STDP learning.

Abstract

McCulloch and Pitts provided a way to describe brain functions in logic gates. The idea of Boolean neurons made a tremendous contribution to the development of artificial intelligence. However, logic gates based on biological neural networks have never been implemented. To investigate where logic gates can be demonstrated using biological neural networks. We constructed biological crossbar neural networks on multi-electrodes arrays (MEA) using PDMS microfluidic channels. The fluorescence optical images revealed that crossbar array was connected by axons. We measured the activation curves using the stimulus-evoked spiking probability, and found that the threshold voltage of activation curves were affected by the spike-timing-dependent-plasticity (STDP) learning. As a result, reconfigurable logic gates of AND and OR could be successfully implemented through STDP learning. Many neuromorphic systems have the crossbar array structure; thus, our study may contribute to the development of neuromorphic research.