LAUSR

Abstract The hardware implementation of neuromorphic computing has attracted growing interest as a promising candidate for confronting the bottleneck of traditional von Neumann computers. Moreover, flexible artificial synapses with learning capabilities are easier to achieve massive parallelism and structural flexibility of the human brain. Most previous reports have focused on the use of electric or light stimulation methods to simulate synaptic behavior through a single mode. To improve the memory and learning characteristics of synaptic transistors, devices with ion-electron coupled electric-double-layer are frequently used. Here we report an organic composite nanoparticle electrolyte TFT based on an aqueous solution is proposed. Some important synaptic behaviors are successfully simulated in our PVP: LATP TFT artificial synapses, including paired pulse promotion (PPF), paired pulse depression (PPD) and signal filtering characteristics. The PPF and PPD index can be modulated by the spike width and spike interval of the presynaptic pulse and the pulse intensity. The minimum energy consumption of the pulse spike of the PVP: LATP-based synaptic transistor is calculated to be 2.28 pJ. We use the synaptic device to simulate “OR” and “YES” logic. Furthermore, we also conducted the classical conditional Pavlov's dog experiments to mimic the associative memory process of human brain using light and electricity combined stimulation. These results migvht provide an alternative route for neural computing.