Circuit simulators are used to verify circuit functionality and to obtain detailed timing information before the expensive fabrication process takes place. They have become an essential CAD tool in an era of sub-micron technology. We have developed a new event-driven MOS circuit simulator to replace a direct method circuit simulator. In our simulator, partitioned subcircuits are analyzed by a direct method matrix solver, and these are controlled by an event-driven scheme to maintain accuracy. The key of this approach is how to manage events for circuit simulation. We introduced two types of events: self-control events for a subcircuit and prediction correcting events between subcircuits. They control simulation accuracy, and bring simulation efficiency through multi-rate behavior of a large scale circuit. The event-driven scheme also brings some useful functions which are not available from a direct method circuit simulator, such as a selected block simulation function and a batch simulation function for load variation. We simulated logic modules (buffer, adder, and counter) with about 1000 MOSFETs with our event-driven MOS circuit simulator. Our simulator was 5-7 times faster than a SPICE-like circuit simulator, while maintaining the less than 1% error accuracy. The selected block simulation function enables to shorten simulation time without losing any accuracy by selecting valid blocks in a circuit to simulate specified node waveforms. Using this function, the logic modules were simulated 13-28 times faster than the SPICE-like circuit simulator while maintaining the same accuracy.

Although intervention practices like mindfulness meditation have proven effective in treating psychosis, there is no clarity on the mechanism of information propagation in the brain. In this study, we formulated a network optimization problem and searched for the optimal solution using Digital Annealer developed by Fujitsu Ltd. This is inspired by quantum computing and is effective in solving large-scale combinatorial optimization problems to find the information propagation pathway in the brain that contributes to the realization of mindfulness. Specifically, we defined the optimal network state as the state of the brain network that is considered to be associated with the mindfulness state. We formulated the problem into two network optimization problems — the minimum vertex-cover problem and the maximum-flow problem — to search for the information propagation pathway that is important for realizing the state. In the minimum vertex-cover problem, we aimed to identify brain regions that are important for the realization of the mindfulness state, and identified eight regions, including four that were suggested to be consistent with previous studies. We formulated the problem as a maximum-flow problem to identify the information propagation pathways in the brain that contribute to the activation of these four identified regions. As a result, approximately 30% of the connections in the brain network structure of this study were identified, and the pathway with the highest flow rate was considered to characterize the bottom-up emotion regulation during mindfulness. The findings of this study could be useful for more direct interventions in the context of mindfulness, which are being investigated by neurofeedback and other methods. This is because existing studies have not clarified the information propagation pathways that contribute to the realization of the brain network states that characterize mindfulness states. In addition, this approach may be useful as a methodology to identify information propagation pathways in the brain that contribute to the realization of higher-order human cognitive activities, such as mindfulness, within large-scale brain networks.