![]() These are trained alongside the HCU’s, and, as training continues, these are refined to capture different properties from the receptive fields. Each HCU comprises several minicolumn units (MCUs), which are trained to offer various interpretations of the HCU’s receptive field. We can note that three receptive fields complement each other, and there is little-to-no overlap between the fields. Finally, after several training epochs, the three HCUs have learned precisely which parts of the image to look at to maximize information transfer. As we begin to train the network, it learns that most of the information about the digits is located in the middle of the images, and each HCU gradually re-evaluates and changes the connectivity (the structure) between itself and the input image. Initially, when we start our training, each HCUs is initiated with a sparse and random receptive field that samples the input image, as represented by the white pixels in the figure. For simplicity, our input set is MNIST, comprising 28x28 pixel images of handwritten digits. Here we see three hypercolumn units (HCUs, the main computational agent in BCPNN), which we are supposed to train so that each HCUs learn a particular feature from the dataset. To illustrate the property visually, consider the series of images presented in Fig. Gradually, as we train, the HCUs’ learn to look (their receptive fields) to extract maximal information from the input image (e.g., the number ’5’) and ignores patches with little-to-no information at the fringes. 1: Three different HCUs, which initially looks at random places in the input image dataset (in this example, the number five). In BCPNN, structural plasticity is a core feature and is used to maximize information extraction from a dataset given a fixed number of connections – the network learns to look at the most interesting aspects of the input set. However, in the human brain, the structure continuously changes, with synapses being formed and removed between neurons. Common deep learning models such as CNNs neglect learning the structure of the network and force fixed spatial filters on the data. Learning of features in the BCPNN model encompasses two separate processes: one is learning the weights of the connections, and the other process called structural plasticity learns a sparse connectivity structure. The BCPNN model for pattern recognition extracts a set of feature representations of the data in a purely unsupervised manner using simple biologically plausible local learning rules. We end this section by presenting a simplified view of the BCPNN model designed for pattern recognition, giving readers an intuitive feeling behind the algorithm Finally, BCPNN supports supervised, semi-supervised, and – perhaps most importantly – unsupervised forms of training, which allows bringing order even to unlabeled (the majority) of data. Structural plasticity is particularly important, as it can reveal new knowledge about the data-set (by inspecting the receptive fields) as well as give opportunity for sparse representations-a theme that is becoming more and more important overall in ML. Furthermore, BCPNN feature structural plasticity-the network does not only learn to interpret what it is seeing (the receptive field) but also learns where it should look to extract most of the information. The basic units for computation are hyper- and mini-columns, which are structures to be fundamental in the human neocortex. BCPNN – unlike DL – is brain-inspired and has an architecture that closely mimics that of the human brain. Has emerged as an alternative to Deep Learning (DL). Recently, however, the Bayesian Confidence Propagation Neural Network (BCPNN) (DNNs) and are based on backpropagation, which has been motivated by the super-human level of performance they can achieve in image recognition tasks Many of these are so-called Deep Neural Networks Today, Machine Learning (ML) as a method for data exploration and understanding has permeated nearly all scientific fields and disciplines. ![]()
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