Enabling Model-based Design for closed-loop applications in neuroengineering

Abstract

This study addresses the inherent difficulties in the creation of neuroengineering devices for closed-loop stimulation, a task typically characterized by intricate and technically demanding processes. Beneath the substantial hardware advancements in neurotechnology, there is often rather complex low-level code that poses challenges in terms of development, documentation, and long-term maintenance. To overcome these obstacles, we adopted an alternative strategy centered on Model-based Design (MBD) as a means to simplify the creation of closed-loop systems and reduce the entry barriers. MBD offers distinct advantages by streamlining the development workflow and facilitating the implementation of intricate systems. In this study, we applied MBD techniques to implement a spike detection algorithm on a Field-Programmable Gate Array (FPGA) using commercially available hardware that combines neural probe electronics with programmable FPGA-based hardware. The entire process of data handling and data processing was designed within the Simulink(R) environment, with subsequent generation of HDL code tailored to the FPGA hardware. The validation of our approach was conducted through in vivo experiments involving six animals. We have made all project code files open-source, thereby providing free access to fellow scientists interested in the development of closed-loop systems.