Recent study shows that the rat’s position on linear track can be decoded from the calcium images recorded from the hippocampal CA1 region of the rat’s brain. However, the real-time position decoding remains a challenge considering the complex decoding process and the strict requirements on hardware and energy cost for closed-loop feedback applications. In this paper, we propose two neural network based methods and corresponding hardware designs for realizing real-time position decoding from calcium images. Our methods are based on: 1) a simple convolutional neural network (CNN), and 2) a rate-based spiking neural network (SNN) converted from the CNN. We quantized the CNN and the SNN into 8-bit fixed point and implemented them on a low-power FPGA. Evaluation results show that the CNN and the SNN methods achieve 56.3%/83.1% and 56.0%/82.8% Hit-1/Hit-3 accuracy for the position decoding across different rats, respectively. We also observed an accuracy-latency tradeoff of the SNN method in decoding positions under various time steps. Finally, we present our SNN implementation on the neuromorphic chip Loihi.