System and Circuit Design for Biologically-Inspired Intelligent Learning

Algorithmically and energetically efficient computational architectures that operate in real time are essential for clinically useful neural prosthetic devices. Such architectures decode raw neural data to obtain direct motor control signals for external devices. They can also perform data compression and vastly reduce the bandwidth and consequently power expended in wireless transmission of raw data from implantable brain–machine interfaces. We describe a biomimetic algorithm and micropower analog circuit architecture for decoding neural cell ensemble signals. The decoding algorithm implements a continuous-time artificial neural network, using a bank of adaptive linear filters with kernels that emulate synaptic dynamics. The filters transform neural signal inputs into control-parameter outputs, and can be tuned automatically in an on-line learning process. We demonstrate that the algorithm is suitable for decoding both local field potentials and mean spike rates. We also provide experimental validation of our system, decoding discrete reaching decisions from neuronal activity in the macaque parietal cortex, and decoding continuous head direction trajectories from cell ensemble activity in the rat thalamus. We further describe a method of mapping the algorithm to a highly parallel circuit architecture capable of continuous learning and real-time operation. Circuit simulations of a subthreshold analog CMOS instantiation of the architecture reveal that its performance is comparable to the predicted performance of our decoding algorithm for a system decoding three control parameters from 100 neural input channels at microwatt levels of power consumption. While the algorithm and decoding architecture are suitable for analog or digital implementation, we indicate how a micropower analog system trades some algorithmic programmability for reductions in power and area consumption that could facilitate implantation of a