The neocognitron is a hierarchical, multilayered artificial neural network proposed by Kunihiko Fukushima in 1979. [1] [2] It has been used for Japanese handwritten character recognition and other pattern recognition tasks, and served as the inspiration for convolutional neural networks. [3]
Previously in 1969, he published a similar architecture, but with hand-designed kernels inspired by convolutions in mammalian vision. [4] In 1975 he improved it to the Cognitron, [5] [6] and in 1979 he improved it to the neocognitron, which learns all convolutional kernels by unsupervised learning (in his terminology, "self-organized by 'learning without a teacher'"). [2]
The neocognitron was inspired by the model proposed by Hubel & Wiesel in 1959. They found two types of cells in the visual primary cortex called simple cell and complex cell , and also proposed a cascading model of these two types of cells for use in pattern recognition tasks. [7] [8]
The neocognitron is a natural extension of these cascading models. The neocognitron consists of multiple types of cells, the most important of which are called S-cells and C-cells. [9] The local features are extracted by S-cells, and these features' deformation, such as local shifts, are tolerated by C-cells. Local features in the input are integrated gradually and classified in the higher layers. [10] The idea of local feature integration is found in several other models, such as the Convolutional Neural Network model, the SIFT method, and the HoG method.
There are various kinds of neocognitron. [11] For example, some types of neocognitron can detect multiple patterns in the same input by using backward signals to achieve selective attention. [12]
In machine learning, a neural network is a model inspired by the structure and function of biological neural networks in animal brains.
Jürgen Schmidhuber is a German computer scientist noted for his work in the field of artificial intelligence, specifically artificial neural networks. He is a scientific director of the Dalle Molle Institute for Artificial Intelligence Research in Switzerland. He is also director of the Artificial Intelligence Initiative and professor of the Computer Science program in the Computer, Electrical, and Mathematical Sciences and Engineering (CEMSE) division at the King Abdullah University of Science and Technology (KAUST) in Saudi Arabia.
The year 1980 in science and technology involved some significant events, listed below.
David Hunter Hubel was an American Canadian neurophysiologist noted for his studies of the structure and function of the visual cortex. He was co-recipient with Torsten Wiesel of the 1981 Nobel Prize in Physiology or Medicine, for their discoveries concerning information processing in the visual system. For much of his career, Hubel worked as the Professor of Neurobiology at Johns Hopkins University and Harvard Medical School. In 1978, Hubel and Wiesel were awarded the Louisa Gross Horwitz Prize from Columbia University. In 1983, Hubel received the Golden Plate Award of the American Academy of Achievement.
The receptive field, or sensory space, is a delimited medium where some physiological stimuli can evoke a sensory neuronal response in specific organisms.
In neuroscience, neuronal tuning refers to the hypothesized property of brain cells by which they selectively represent a particular type of sensory, association, motor, or cognitive information. Some neuronal responses have been hypothesized to be optimally tuned to specific patterns through experience. Neuronal tuning can be strong and sharp, as observed in primary visual cortex, or weak and broad, as observed in neural ensembles. Single neurons are hypothesized to be simultaneously tuned to several modalities, such as visual, auditory, and olfactory. Neurons hypothesized to be tuned to different signals are often hypothesized to integrate information from the different sources. In computational models called neural networks, such integration is the major principle of operation. The best examples of neuronal tuning can be seen in the visual, auditory, olfactory, somatosensory, and memory systems, although due to the small number of stimuli tested the generality of neuronal tuning claims is still an open question.
A simple cell in the primary visual cortex is a cell that responds primarily to oriented edges and gratings. These cells were discovered by Torsten Wiesel and David Hubel in the late 1950s.
Complex cells can be found in the primary visual cortex (V1), the secondary visual cortex (V2), and Brodmann area 19 (V3).
Hierarchical temporal memory (HTM) is a biologically constrained machine intelligence technology developed by Numenta. Originally described in the 2004 book On Intelligence by Jeff Hawkins with Sandra Blakeslee, HTM is primarily used today for anomaly detection in streaming data. The technology is based on neuroscience and the physiology and interaction of pyramidal neurons in the neocortex of the mammalian brain.
Kunihiko Fukushima is a Japanese computer scientist, most noted for his work on artificial neural networks and deep learning. He is currently working part-time as a senior research scientist at the Fuzzy Logic Systems Institute in Fukuoka, Japan.
Time delay neural network (TDNN) is a multilayer artificial neural network architecture whose purpose is to 1) classify patterns with shift-invariance, and 2) model context at each layer of the network.
There are many types of artificial neural networks (ANN).
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A convolutional neural network (CNN) is a regularized type of feed-forward neural network that learns features by itself via filter optimization. This type of deep learning network has been applied to process and make predictions from many different types of data including text, images and audio. Convolution-based networks are the de-facto standard in deep learning-based approaches to computer vision and image processing, and have only recently have been replaced -- in some cases -- by newer deep learning architectures such as the transformer. Vanishing gradients and exploding gradients, seen during backpropagation in earlier neural networks, are prevented by using regularized weights over fewer connections. For example, for each neuron in the fully-connected layer, 10,000 weights would be required for processing an image sized 100 × 100 pixels. However, applying cascaded convolution kernels, only 25 neurons are required to process 5x5-sized tiles. Higher-layer features are extracted from wider context windows, compared to lower-layer features.
This page is a timeline of machine learning. Major discoveries, achievements, milestones and other major events in machine learning are included.
AlexNet is the name of a convolutional neural network (CNN) architecture, designed by Alex Krizhevsky in collaboration with Ilya Sutskever and Geoffrey Hinton, who was Krizhevsky's Ph.D. advisor at the University of Toronto. It had 60 million parameters and 650,000 neurons.
Machine learning in bioinformatics is the application of machine learning algorithms to bioinformatics, including genomics, proteomics, microarrays, systems biology, evolution, and text mining.
In artificial neural networks, a convolutional layer is a type of network layer that applies a convolution operation to the input. Convolutional layers are some of the primary building blocks of convolutional neural networks (CNNs), a class of neural network most commonly applied to images, video, audio, and other data that have the property of uniform translational symmetry.
Artificial neural networks (ANNs) are models created using machine learning to perform a number of tasks. Their creation was inspired by biological neural circuitry. While some of the computational implementations ANNs relate to earlier discoveries in mathematics, the first implementation of ANNs was by psychologist Frank Rosenblatt, who developed the perceptron. Little research was conducted on ANNs in the 1970s and 1980s, with the AAAI calling this period an "AI winter".
Juyang (John) Weng is a Chinese-American computer engineer, neuroscientist, author, and academic. He is a former professor at the Department of Computer Science and Engineering at Michigan State University and the President of Brain-Mind Institute and GENISAMA.