Real time controller for brain machine interface experiment

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We are requesting a system that is capable of ensuring deterministic behaviour of closed loop reach experiments for brain machine interfaces. Our approach to brain machine interfaces is unique as our goal is to teach the brain to use our algorithms rather than write algorithms that interpret the neural code. The experiments we are conducting require real time continuous feedback to induce plasticity in the parietal and premotor cortices. This paradigm is very sensitive to delays between the instruction and the feedback. We have trained a non human primate (NHP) to change the tuning of neurons rapidly and in real time. Each trial, the NHP has to think about reaching in such a way so that a target on the screen is inflated. The size of the target is a proxy for our ability to decode that particular trial. Once the NHP understood the task, there was rapid and considerable improvement in decoding up to 12 positions on a screen that faces the monkey. However, we are hindered by delays in the feedback signal. Conditioning is disrupted if delays become too big. We have seen a decrease in performance after a spike in the feedback delay time. This problem has frustrated and hindered our progress. The NetLinx system we are requesting eliminates the delay in the feedback by eliminating all secondary and tertiary computers in the pathway. This system transfers all the statistical and signal processing algorithms directly onto our behavioural controller which runs Labview Real Time. This system will be heavily utilized as it is integral for the continued success of our experiments.