Systems and methods for modeling and decoding neural activities

A system comprising:

• a computing device, the computing device comprising a processor and a memory device in communication with the processor;
• and
• a brain sensing device configured to measure activities of a neural population and send signals corresponding to the measured activities to the computing device,
• wherein the processor, when executing instructions stored in the memory device, is configured to:
• receive a first plurality of signals corresponding to the activities of the neural population from the brain sensing device;
• generate, by applying a neural dynamics model to the first plurality of signals, a second plurality of signals corresponding to a de-noised state of the activities of the neural population;
• generate, by applying a BMI decoding model to the second plurality of signals, a control signal;
• generate, by applying a BMI plant model to the control signal, a movement vector;
• and
• move an object according to a predetermined path, in response to the movement vector and a current state of the object,
• wherein a first plurality of coefficients of the neural dynamics model and a second plurality of coefficients of the BMI decoding model are iteratively updated, by the processor executing a learning algorithm, in response to the brain sensing device's continuous measurement of the activities of the neural population.

A method of simulating neural activities, comprising:

• generating, by a processor applying a feedback control model, an input signal;
• generating, by a processor applying a simulated neural dynamics model, a state signal;
• combining, by a neural activity simulation program, the input signal and the state signal;
• in response to combining the input signal and the state signal, generating a simulated neural activity signal;
• and
• sending the simulated neural activity signal to a BMI system, the BMI system configured to be controlled by the neural activity simulation program.