Multiplicative Recurrent Neural Network for Fast and Robust Intracortical Brain Machine Interface Decoders

A brain-machine interface system, comprising:

• at least one multi-electrode array implanted into a user's brain;
• a prosthetic device;
• and
• a neural signal decoder implemented using a computing device in communication with the at least one multi-electrode array and the prosthetic device, where the neural signal decoder comprises a recurrent neural network trained by:
• obtaining a plurality of neural signals from a user using the at least one multi-electrode array, where the plurality of neural signals are obtained over the course of at least two days;
• instantiating the recurrent neural network, where the recurrent neural network is configured to map neural signals to kinematic functions of a prosthetic device;
• and;
• training the recurrent neural network using the plurality of neural signals;
• and
• where the neural signal decoder is configured to:
• obtain a unique neural signal describing an intended movement of the prosthetic device using the at least one multi-electrode array;
• move the prosthetic device to approximate the intended movement.