Based on this concept, in this work a novel way to analyze dendritic trees with high complexity is reported, using features obtained through splitting the 3D structure of the dendritic trees of traced neurons into time series. Digitally reconstructed neurons were separated into control and ...
Neurons have diverse molecular, morphological, connectional and functional properties. We believe that the only realistic way to manage this complexity — and thereby pave the way for understanding the structure, function and development of brain circuits — is to group neurons into types, which can...
Probabilistic Neural Network Structure Reduction for Medical Data Classification Probabilistic neural network (PNN) consists of the number of pattern neurons that equals the cardinality of the data set. The model design is therefore com... M Kusy,J Kluska - International Conference on Artificial Intell...
e, AT8-positive neurons in hippocampus. f, Hippocampus stained with antibody RD4 (specific for 4R tau). g, Gallyas-Braak silver-positive neurons and glial cells in hippocampus. h, Hippocampus stained with antibody RD3 (specific for 3R tau). i, Higher-power view of frontal cortex stained ...
Neural networks are software systems that loosely model biological neurons and synapses. Neural network classification is one of the most interesting and sophisticated topics in all of machine learning. One way to think of a neural network is as a complex mathematical function that accepts one or ...
4.1.1.2 Artificial neural network classifier-based methods Artificial neural network (ANN) classifier is a supervised multi-classifier composed of one input layer, one or more hidden layers and one output layer. Each layer contains several nodes named artificial neurons which are connected with other...
To develop a method for simulating background EEG based on the premise that the self-organized activity from synaptic interaction among populations of neurons creates sustained fluctuations that can be modeled with the filtered output of... WJ Freeman - 《Clinical Neurophysiology》 被引量: 47发表:...
Researchers have begun to optimize the connections between neurons and have launched a series of studies on ESN topologies. Fette et al.’s improved network structure [20] has little effect on the performance of traditional ESNs, but its idea of improving the structure is worthy of reference. ...
At the end of each block, a dropout layer randomly drops neurons from the network and further helps the network to overcome the overfitting problem by reducing the number of parameters. After the convolution-max-pooling blocks, there is a classifier block, which contains two dense layers and a...
Fig. 6a and b illustrate the computational times of training and a testing realization for both the heartbeat audio signals versus the number of neurons. The GPU and CPU comparison shows the benefit of using a GPU implementation, with significantly lower training times. Fig. 6a presented the ...