of dendritic spines (as well as other morphologic features) may be related to a given neuron's most effective stimulus, indicating that it will indeed be possible to use the criteria established in the present
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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 ...
Machine learning algorithms can be used for the prediction of nonnative sound classification based on crosslinguistic acoustic similarity. To date, very few linguistic studies have compared the classification accuracy of different algorithms. This study aims to assess how well machines align with human ...
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...
Internal state of neuron Uj is the result of such combination, and this internal state and the threshold help the last part pulse generator to generate the pulse. Lo et al. [23] introduce PCNN in image processing area and the mathematics modelling is defined below. The Table 2 explains the...
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 ...
The standard way to model a neuron’s outputfas a function of its inputxis withf(x) = tanh(x)orf(x) = (1 + e−x)−1. In terms of training time with gradient descent, these saturating nonlinearities are much slower than the non-saturating nonlinearityf(x) = max(0,x). Followin...
The classification techniques used to classify the PQ disturbances are based on regression algorithms. These techniques are multiple linear regression (MLR), adaptive linear neuron (LIN), multilayer ANNs (BP1 and BP2), radial basis function (RBF) network, exact radial basis (ERB), and generalized...
In each neuron, linear computation is performed optically and the non-linear activation function is realized opto-electronically, allowing a classification time of under 570 ps, which is comparable with a single clock cycle of state-of-the-art digital platforms. A uniformly distributed supply ...