Synaptic devices for simulating brain processes in visual-information perception to persisting memory through attention mechanisms

Abstract

In the human brain, attention plays a crucial role in encoding information into memory. Therefore, focused attention during encoding enhances the likelihood of information being effectively encoded and stored in memory. This phenomenon is creatively replicated in our proposed synaptic devices, which regulate the forgetting curves by manipulating the gate voltage. Thus, the proposed transistor devices separate long-term memory from long-lasting memory. TiO₂-based synaptic transistors are used to replicate brain functions, from vision processing to memory retention. The photosensitive nature of TiO₂ enables the utilization of both photo- and electric stimuli. The electrical properties of the synaptic devices induced by photostimulation replicate the human-vision process, while those elicited by electric stimulation simulate memory-retention capabilities. By applying a shallow trap with a short lifetime, light stimulation can be utilized to mimic the effects of short-term memory. A deep trap with a long lifetime is employed in electrical memory to replicate the phenomena associated with persisting memory. A simulation of the MNIST recognition of an artificial neural network constructed with the measured synaptic characteristics exhibit an accuracy rate of 92.96%, which indicates that the proposed device can be successfully incorporated into neuromorphic devices.