Neural Encoding and Decoding in Brain-Machine Interfaces for Learning and Memory Applications

The Brain-Machine Interfaces (BMI) offer an engineering model to record and decode neural responses. This paper will discuss the issue of neural activity of memory manifestation in the circuits between hippocampal and cortical neurons; the computational decoding algorithms, which transform the population-level allocation of neurons into intelligible cognitive terminologies; and the uses of the algorithms in creating closed-loop BMI systems in restoring memory. Cognitive BMIs are neural state estimation with machine learning and adaptive stimulation paradigms to help improve or repair mnemonic functioning. Hippocampal prototest and temporally directed stimulation models depict some of the potentials of such systems. To gain a clearer insight into the potential offered by BMIs in the field of cognitive neurotechnology, the last part of the paper will discuss the numerous technical issues surrounding the creation of such systems (signal stability, model generalization, stimulation precision) and their effect on the field of ethics.