Publication date: 15th April 2025
The recent rapid progress of AI technology has made our lives more convenient and comfortable, but the energy required for information processing has also increased dramatically. Therefore, we are focusing on the brain, which can perform highly intelligent information processing with extremely low energy and aiming to create a neuromorphic computer that realizes ultra-low power AI processing by imitating the brain’s information processing principle in hardware. We are particularly interested in analog spiking neural network (SNN) hardware, in which information is not represented by digital data but by trains of voltage spikes that propagate in the network of analog neuronal circuits to process information. For learning with SNNs, neurons and synapses should have “plasticity”; parameters such as firing thresholds and synaptic efficacies change dynamically in accordance with their own spiking activities and stay unchanged if no spiking occurs. In addition, it has been argued that uncertainty i.e. variability and/or stochasticity in their dynamics should improve the performance of learning. One of the essential aspects in developing analog SNN hardware is how to implement such plasticity and uncertainty with electronic devices. The resistive change device, often being referred to as the memristor, is a promising candidate. We are currently conducting research into how the plasticity and uncertainty of neurons and synapses affect the information processing performance of analog SNN hardware. In this presentation, we will introduce these results and discuss the possibility of applying memristors.
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