Tuning Brain-Inspired Computers for Better Performance

Brain-inspired computers, called Spiking Neural Networks (SNNs), are great for low-power devices like wearables and sensors. But one key part, the leaky time constant (LTC), hasn't been fully studied. The LTC helps these computers process information over time. Scientists tested how changing the LTC affects how well these computers work with different types of data, like pictures and biosignals. They found that the LTC really matters. For pictures, a middle-range LTC worked best. It gave better results and kept the computer's connections stable. For biosignals, the best LTC helped the computer remember important details over time. But if the LTC was too high or too low, the computer's performance dropped. This is because too much LTC makes the computer forget things too slowly, and too little makes it forget too fast. The study also looked at how the LTC affects the computer's energy use. It found that balancing the LTC is key for good performance and efficiency. This research gives practical tips for making these brain-inspired computers work better. It shows that tweaking the LTC can help these computers work well for different tasks. This could lead to better, low-power computers for real-time tasks in the future.