Semiannual Publication
Document Type : Original Article
Abstract
In this paper, we develop a novel approach for bidirectional brain-machine interface (BMI). First, we propose a neural network model for the sensory cortex (S1) connected to the neural network model of the motor cortex (M1) considering the topographic mapping between S1 and M1. We use the 4-box model in S1 and 4-box in M1 so that each box contains 500 neurons. Individual boxes are composed of two neural populations: inhibitory interneurons and pyramidal neurons. Next, we develop a new BMI algorithm based on neural firing. The main concept of this BMI algorithm is to close the loop between two components: the sensory interface and the motor interface. The sensory interface encodes some of the state parameters of the external device into an electrical stimulus delivered to the S1 model. The motor interface takes neural recordings from the M1 model and decodes them into a force applied to the object. We present the simulation results for the online BMI which means that there is a real-time information exchange between the S1-M1 network model and the external device.
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