Alexander Fuchs, Tina Trachsel, Roland Weiger, Florin Eggmann
ChatGPT's performance in dentistry and allergyimmunology assessments: a comparative study
Swiss dental journal
deep neural networks dnns widely adopted decode motor states noninvasively invasively recorded neural signals eg realizing braincomputer interfaces however neurophysiological interpretation dnns make decision based input neural activity limitedly addressed especially applied invasively recorded data reduces decoder reliability transparency prevents exploitation decoders better comprehend motor neural encoding adopted explainable artificial intelligence approach based convolutional neural network explanation technique reveal spatial temporal neural properties reachtograsping singleneuron recordings posterior parietal area v6a network able accurately decode 5 different grip types explanation technique automatically identified cells temporal samples influenced network prediction grip encoding v6a neurons already started movement preparation peaking movement execution difference found within v6a dorsal v6a neurons progressively encoded increasingly advanced grips ventral v6a neurons increasingly rudimentary grips subareas following linear trend amount grip encoding level grip skills revealing elements neural activity relevant grip priori assumptions approach supports advances current knowledge reachtograsp encoding v6a may represent general tool able investigate neural correlates motor cognitive tasks eg attention memory tasks singleneuron recordings