The domain specimen for which the brain serves as an example is characterized by two main properties. One of them being the position dependent 'aggregate characteristics' (distribution of neurons within the specimen), the other position invariant 'single cell characteristics' (structural properties of...
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 then be analysed systematically and reproducibly. However, neuronal classification has...
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 ...
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 nodes in adjacent layers. Each connection has a weight ...
Compared with other common pre-trained CNNs, AlexNet has the following privileges: (i) The overfitting issue is alleviated with the use of a dropout layer that discards randomly some neurons in the training process; (ii) the richness of features is improved with the use of max-pooling layers...
ESN mimics the structure of recursively connected neuron circuits in the brain and consists of an input layer, an implicit layer (or called reservoir), and an output layer. Among them, the hidden layer is a reservoir composed of large-scale random, sparsely connected neurons, which maps the ...
Classification Bimodal distribution Clustering Sigmoid non-linearity V1 neurons 1. Introduction In their early work on primary visual cortex, Hubel and Wiesel described the existence of two classes of cells: simple and complex (Hubel and Wiesel, 1962, Hubel and Wiesel, 1968). A simple cell was ...
With the wide application of Light Detection and Ranging (LiDAR) in the collection of high-precision environmental point cloud information, three-dimensional (3D) object classification from point clouds has become an important research topic. However, th
The resulting classification accuracy was found to be above chance several seconds before the time point of conscious decision2,3 both in the frontopo- lar (BA10) and in the parietal cortex. Analogous results were obtained also in the case of complex free-decisions4 and supported by ...
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