Yang Shih-Hung, Wang Han-Lin, Lo Yu-Chun, Lai Hsin-Yi, Chen Kuan-Yu, Lan Yu-Hao, Kao Ching-Chia, Chou Chin, Lin Sheng-Huang, Huang Jyun-We, Wang Ching-Fu, Kuo Chao-Hung, Chen You-Yin
Department of Mechanical Engineering, National Cheng Kung University, Tainan, Taiwan.
Department of Biomedical Engineering, National Yang Ming University, Taipei, Taiwan.
Front Comput Neurosci. 2020 Mar 31;14:22. doi: 10.3389/fncom.2020.00022. eCollection 2020.
In brain machine interfaces (BMIs), the functional mapping between neural activities and kinematic parameters varied over time owing to changes in neural recording conditions. The variability in neural recording conditions might result in unstable long-term decoding performance. Relevant studies trained decoders with several days of training data to make them inherently robust to changes in neural recording conditions. However, these decoders might not be robust to changes in neural recording conditions when only a few days of training data are available. In time-series prediction and feedback control system, an error feedback was commonly adopted to reduce the effects of model uncertainty. This motivated us to introduce an error feedback to a neural decoder for dealing with the variability in neural recording conditions. We proposed an evolutionary constructive and pruning neural network with error feedback (ECPNN-EF) as a neural decoder. The ECPNN-EF with partially connected topology decoded the instantaneous firing rates of each sorted unit into forelimb movement of a rat. Furthermore, an error feedback was adopted as an additional input to provide kinematic information and thus compensate for changes in functional mapping. The proposed neural decoder was trained on data collected from a water reward-related lever-pressing task for a rat. The first 2 days of data were used to train the decoder, and the subsequent 10 days of data were used to test the decoder. The ECPNN-EF under different settings was evaluated to better understand the impact of the error feedback and partially connected topology. The experimental results demonstrated that the ECPNN-EF achieved significantly higher daily decoding performance with smaller daily variability when using the error feedback and partially connected topology. These results suggested that the ECPNN-EF with partially connected topology could cope with both within- and across-day changes in neural recording conditions. The error feedback in the ECPNN-EF compensated for decreases in decoding performance when neural recording conditions changed. This mechanism made the ECPNN-EF robust against changes in functional mappings and thus improved the long-term decoding stability when only a few days of training data were available.
在脑机接口（BMI）中，由于神经记录条件的变化，神经活动与运动学参数之间的功能映射会随时间而变化。神经记录条件的变异性可能导致长期解码性能不稳定。相关研究使用几天的训练数据来训练解码器，使其对神经记录条件的变化具有内在的鲁棒性。然而，当只有几天的训练数据可用时，这些解码器可能对神经记录条件的变化并不鲁棒。在时间序列预测和反馈控制系统中，通常采用误差反馈来减少模型不确定性的影响。这促使我们将误差反馈引入神经解码器，以应对神经记录条件的变异性。我们提出了一种带有误差反馈的进化构造与剪枝神经网络（ECPNN-EF）作为神经解码器。具有部分连接拓扑结构的ECPNN-EF将每个分类单元的瞬时放电率解码为大鼠的前肢运动。此外，采用误差反馈作为额外输入，以提供运动学信息，从而补偿功能映射的变化。所提出的神经解码器是根据从大鼠水奖励相关杠杆按压任务收集的数据进行训练的。前2天的数据用于训练解码器，随后10天的数据用于测试解码器。对不同设置下的ECPNN-EF进行评估，以更好地理解误差反馈和部分连接拓扑结构的影响。实验结果表明，当使用误差反馈和部分连接拓扑结构时，ECPNN-EF在每日解码性能上显著更高，且每日变异性更小。这些结果表明，具有部分连接拓扑结构的ECPNN-EF可以应对神经记录条件在日内和日间的变化。ECPNN-EF中的误差反馈在神经记录条件改变时补偿了解码性能的下降。这种机制使ECPNN-EF对功能映射的变化具有鲁棒性，从而在只有几天训练数据可用时提高了长期解码稳定性。
