Simulating Spiking Neural Networks with 8-Bit Floating-Point Numbers

Performing brain simulations that match the size and dynamic nature of real brains is arduous but essential for understanding neural mechanisms underlying animal behaviour. To address this challenge, this paper proposes an 8-bit floating-point format (minifloat) for the purpose of efficient simulation of biological spiking neural networks in digital hardware. We present models employing minifloat variables, as well as multiplication and addition arithmetics. Other low-precision data types are considered to elucidate the feasibility and advantages of minifloat. Despite the inherent floating-point errors, minifloat models effectively simulate balanced networks that reproduce activity patterns observed in cortical networks. Our results suggest that low-precision floating-point data types are a viable alternative for spiking neural network simulations that could also improve scalability and data throughput.