Large-scale recordings of neural activity are providing new opportunities to study neural population dynamics. A powerful method for analyzing such high-dimensional measurements is to deploy an algorithm to learn the low-dimensional latent dynamics. LFADS (Latent Factor Analysis via Dynamical Systems) is a deep learning method for inferring latent dynamics from high-dimensional neural spiking data recorded simultaneously in single trials. This method has shown a remarkable performance in modeling complex brain signals with an average inference latency in milliseconds. As our capacity of simultaneously recording many neurons is increasing exponentially, it is becoming crucial to build capacity for deploying low-latency inference of the computing algorithms. To improve the real-time processing ability of LFADS, we introduce an efficient implementation of the LFADS models onto Field Programmable Gate Arrays (FPGA). Our implementation shows an inference latency of 41.97 $\mu$s for processing the data in a single trial on a Xilinx U55C.

翻译：大规模神经活动记录为研究神经群体动态提供了新机遇。分析此类高维测量数据的一种有效方法是部署算法以学习低维潜在动态。LFADS（基于动力系统的潜在因子分析）是一种深度学习算法，可单次试验中从高维神经脉冲数据推断潜在动态。该方法在建模复杂脑信号方面表现卓越，平均推理延迟仅为毫秒级。随着同时记录大量神经元的能力呈指数级增长，构建低延迟推理计算算法的部署能力变得至关重要。为提升LFADS的实时处理能力，我们提出了一种在FPGA（现场可编程门阵列）上高效实现LFADS模型的方法。实验表明，该实现方案在Xilinx U55C上处理单次试验数据的推理延迟为41.97微秒。