Mirfakhraei K, Horch K
IEEE Trans Biomed Eng. 1994 Jan;41(1):89-91. doi: 10.1109/10.277276.
Neural network pattern-recognition techniques were applied to the problem of identifying the sources of action potentials in multi-unit neural recordings made from intrafascicular electrodes implanted in cats. The network was a three-layer connectionist machine that used digitized action potentials as input. On average, the network was able to reliably separate 6 or 7 units per recording. As the number of units present in the recording increased beyond this limit, the number separable by the network remained roughly constant. The results demonstrate the utility of neural networks for classifying neural activity in multi-unit recordings.
神经网络模式识别技术被应用于识别植入猫体内的束内电极所记录的多单元神经活动中动作电位来源的问题。该网络是一个三层连接主义机器，它将数字化的动作电位作为输入。平均而言，该网络能够可靠地将每次记录中的6或7个单元分开。当记录中存在的单元数量超过这个限制时，网络能够分开的单元数量大致保持不变。结果证明了神经网络在多单元记录中对神经活动进行分类的实用性。
