Project Jenkins: Turning Monkey Neural Data into Robotic Arm Movement, and Back

Synthetic neural data generation and neuroprosthetic devices are active areas of research, sparked by advances in neuroscience and robotics [22, 4, 2, 15]. These fields have significant implications for brain-computer interfaces, rehabilitation, and simulation of brain dynamics for downstream tasks or gaining new understanding of the underlying neural mechanisms. In this project, which we call "Project Jenkins," we explore such decoding and encoding of neural data from a macaque monkey named Jenkins. We used a publicly available dataset [5] containing neural firing patterns from Jenkins' motor and premotor cortical areas during a center-outreach task. Generating synthetic neural activity enables researchers to test and refine decoding models without requiring continuous access to live neural recordings [12, 16], while neuroprosthetic advancements [18, 20, 21, 9, 7, 3, 8, 17] rely on robust encoding techniques to translate brain signals into precise motor commands. Our aim was two-fold (Figure 1, 2): Decoding: Translate neural spiking data into predicted velocities for a robotic arm. Encoding: Generate synthetic neural activity corresponding to an intended robotic movement. With this paper, we publish the developed open-source tools for both synthetic neural data generation and neural decoding, enabling researchers to replicate our methods and build upon them. Our full codebase and additional resources, including demonstration videos, can be found on the project's website: https://www.808robots.com/