Improved system identification using artificial neural networks and analysis of individual differences in responses of an identified neuron.
Improved system identification using artificial neural networks and analysis of individual differences in responses of an identified neuron.
复制标题
使用人工神经网络改进系统识别并分析已识别神经元响应的个体差异。
DOI：
10.1016/j.neunet.2015.12.002
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Costalago Meruelo A
中科院分区：
文献类型：
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作者：
Costalago Meruelo A
数学模型通常被用来理解神经元和神经网络响应的编码特性和动力学。在这里，我们分析了人工神经网络(ANN)作为运动神经元反应建模工具的有效性。我们使用神经网络来模拟沙漠蝗虫的一种已识别的运动神经元-快速伸展运动神经元-的突触反应，以响应感觉器官-股骨脉络膜器官的移位，该感觉器官监测胫骨相对于腿部股骨的运动。这项研究的目的有三个：第一，确定神经网络作为建模和研究神经网络的工具的潜在价值；第二，了解神经网络在不同个体和不同输入信号之间的泛化特性；第三，了解已识别神经元的反应的个体差异。提出了一种设计人工神经网络体系结构的元启发式算法。将神经网络生成的模型与同一神经元先前的数学模型生成的模型的性能进行了比较。结果表明，人工神经网络在预测高斯白噪声下的特定神经反应方面明显优于LNL和Wiener模型，但在正弦输入下的预测结果没有显著差异。他们还能够预测同一神经元在不同个体中的反应，无论是哪种动物被用来建立模型，尽管一些个体之间的显著差异是明显的。
Mathematical modelling is used routinely to understand the coding properties and dynamics of responses of neurons and neural networks. Here we analyse the effectiveness of Artificial Neural Networks (ANNs) as a modelling tool for motor neuron responses. We used ANNs to model the synaptic responses of an identified motor neuron, the fast extensor motor neuron, of the desert locust in response to displacement of a sensory organ, the femoral chordotonal organ, which monitors movements of the tibia relative to the femur of the leg. The aim of the study was threefold: first to determine the potential value of ANNs as tools to model and investigate neural networks, second to understand the generalisation properties of ANNs across individuals and to different input signals and third, to understand individual differences in responses of an identified neuron. A metaheuristic algorithm was developed to design the ANN architectures. The performance of the models generated by the ANNs was compared with those generated through previous mathematical models of the same neuron. The results suggest that ANNs are significantly better than LNL and Wiener models in predicting specific neural responses to Gaussian White Noise, but not significantly different when tested with sinusoidal inputs. They are also able to predict responses of the same neuron in different individuals irrespective of which animal was used to develop the model, although notable differences between some individuals were evident.