Real-time Decoding of Neurally-encoded Signals
Developing algorithms for real-time decoding of neurally-encoded signals, that ensure stability in closed loop.
DEVELOPING ALGORITHMS FOR REAL-TIME DECODING OF NEURALLY-ENCODED SIGNALS, THAT ENSURE STABILITY IN CLOSED LOOP Decoding the digital signal that is transmitted by the neurons in the motor cortex to the muscles benefits the design of reliable prosthetics for rapid movements in uncertain environments. To that end, we developed a non-linear decoder for the neuronal ‘intent of movement’ signal which converges to the desired movement in the presence of neuronal encoding [1]. We also derived conditions for the stability of the movement generation in the presence of feedback as a function of the bandwidth of the movement and biophysical parameters of the neurons and the muscle [2]. The research can be applied towards a Brain-Machine Interface to drive a prosthetic or a compensatory device in a closed loop system involving visual and sensory feedback.
[1] Saxena S., Dahleh M. “Real-Time Decoding of an Integrate and Fire Encoder.” Advances in Neural Information Processing Systems (NIPS), 2014.
[2] Saxena S., Dahleh M. “Analyzing the Effect of an Integrate and Fire Encoder and Decoder in Feedback.” Proceedings of 53rd IEEE Conference on Decision and Control (CDC), 2014.