Brain-machine interfaces in rat motor cortex: neuronal operant conditioning to perform a sensory detection task

A chief concern in the pursuit of controlling a neuroprosthetic device using direct brain signals is the question of how many bits of information are achievable through a direct brain-machine interface (BMI) via implantable microelectrode devices. This experiment begins to address this issue with implementation of a simple, software based decoding algorithm that allows the brain to adapt to the rules imposed upon it, To test this algorithm, two chronic 16-channel Michigan silicon microelectrode arrays were implanted into the primary motor cortex of two rats to record simultaneous unit spike activity. The animals were trained to perform an auditory detection task by modulating the recorded cortical spike activity in a prescribed manner. Both non-adaptive and adaptive neural decoding algorithms were evaluated. With the implementation of a non-adaptive decoding algorithm, the rats´ behavioral (cortical) responses plateaued at approximately 75% correct; however, with the implementation of an adaptive algorithm, the rats´ behavioral responses relatively quickly increased to 91% correct. The neural recordings suggest that the brain is able to modulate detailed cortical responses in accordance with the prescribed operant conditioning rules.