(1994) Structure and function of gustatory neurons in the nucleus of the solitary tract. I. A classification of neurons based on morphological features. J. Comp. Neurol., 347, 531-544.Renehan WE, Jin Z, Zhang X, Schweitzer L. 1996. Struc- ture and function of gustatory neurons in the...
Automatic molecular classification of cancer based on DNA microarray has many advantages over conventional classification based on morphological appearance of the tumor. Using artificial neural networks is a general approach for automatic classification. In this paper, Direction-Basis-Function neuron and Prio...
The new WNN takes nonlinear mother wavelet as neuron instead of traditional nonlinear sigmoid function. It owns the merits of good generalization ability and high converging speed. In addition, multi-resolution and self-adaptation are also its advantages. Experimental results have shown that our ...
It not only overcomes the computational complexity, training inefficiency, and difficulty of the practical application of RNN but also avoids the problem of locally optimal solutions. ESN mimics the structure of recursively connected neuron circuits in the brain and consists of an input layer, an ...
The neuron identity problem: form meets function. Neuron 80, 602–612 (2013). Article CAS PubMed Google Scholar Markram, H. et al. Interneurons of the neocortical inhibitory system. Nat. Rev. Neurosci. 5, 793–807 (2004). Article CAS PubMed Google Scholar ...
Based on still simplified mathematical notation, it is proposed that nonlinear aggregating function of neural inputs should be understood as composition of synaptic as well as partial somatic neural operation also for static neural units. Thus it unravels novel, simplified, yet universal insight into ...
Multi-Valued Neuron (MVN) was proposed forpattern classification. It operates with complex-valued inputs, outputs, andweights, and its learning algorithm is based on error-correcting rule. Theactivation function of MVN is not differentiable. Therefore, we can not applybackpropagation when constructing...
QC improves the optimization of the underlying objective function. It reduces the training time of deep learning [42,43,44]. The convolutional neural network (CNN) is a classical machine learning model suitable to process the images. The CNN model is based on the idea of the convolutional ...
Based on the interaction of the CAE reconstruction process and the CNN classification process, the CAE regards the saturated features of the VICur as noise and removes them accurately. Consequently, it guides CNN to focus on the unsaturated features of the VICur. The unsaturated part of the VI...
Various patterns of neural activity are observed in dynamic cortical imaging data. Such patterns may reflect how neurons communicate using the underlying circuitry to perform appropriate functions; thus it is crucial to investigate the spatiotemporal cha