Honeypots are powerful security tools, which are developed to shield commercial and industrial networks from malicious activity. Honeypots act as passive and interactive decoys in a network by attracting malicious activity away from critical network devices. Given that the security incidents against industrial and critical infrastructure are getting sophisticated and persistent, advanced security systems are needed. In this paper, a novel industrial honeypot implementation is presented, which is based on the Modbus protocol, entitled NeuralPot. The presented NeuralPot honeypot is able to emulate industrial Modbus entities in order to actively confuse the intruders. It achieves this by introducing two distinct deep neural networks, a Generative Adversarial Network and an Autoencoder Network, which learn Modbus device behavior and generate realistic-looking traffic behavior. Based on the evaluation results, the proposed industrial honeypot performs well in terms of accuracy, similarity, and elapsed time of data generation.