Abstract Objective. Artificial neural networks (ANNs) are state-of-the-art tools for modeling and decoding neural activity, but deploying them in closed-loop experiments with tight timing constraints is challenging due to their limited support in existing real-time frameworks. Researchers need a platform that fully supports high-level languages for running ANNs (e.g. Python and Julia) while maintaining support for languages that are critical for low-latency data acquisition and processing (e.g. C and C++). Approac…

Abstract Objective. Current intracortical brain-computer interfaces (iBCIs) rely predominantly on threshold crossings (‘spikes’) for decoding neural activity into a control signal for an external device. Spiking data can yield high accuracy online control during complex behaviors; however, its dependence on high-sampling-rate data collection can pose challenges. An alternative signal for iBCI decoding is the local field potential (LFP), a continuous-valued signal that can be acquired simultaneously with spiking ac…

This is the supporting data for "BRAND: A platform for closed-loop experiments with deep network models". It can be analyzed using the code at github.com/brandbci/paper-figures. This dataset contains: Latency measurements from computers running the BRAND software under several different conditions Example data from a real-time neural data simulator made using BRAND Cursor kinematics from a participant using BRAND to perform cursor control with an intracortical brain-computer interface in the BrainGate2 clinical tr…

This is the supporting data for "BRAND: A platform for closed-loop experiments with deep network models". It can be analyzed using the code at github.com/brandbci/paper-figures. This dataset contains: Latency measurements from computers running the BRAND software under several different conditions Example data from a real-time neural data simulator made using BRAND Cursor kinematics from a participant using BRAND to perform cursor control with an intracortical brain-computer interface in the BrainGate2 clinical tr…

Abstract Artificial neural networks (ANNs) are state-of-the-art tools for modeling and decoding neural activity, but deploying them in closed-loop experiments with tight timing constraints is challenging due to their limited support in existing real-time frameworks. Researchers need a platform that fully supports high-level languages for running ANNs (e.g., Python and Julia) while maintaining support for languages that are critical for low-latency data acquisition and processing (e.g., C and C++). To address these…