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.

使用 LFADS 模型和 FPGA 实现的高效实时数据处理方法，能够从高维度的神经尖峰数据中推断出潜在的动态，为研究神经人群动力学和提高计算算法的实时处理能力提供了新的机会。