Simple model of spiking neurons
... Hoppensteadt and Izhikevich [1] and Wang [2] have proposed network models where the neural activity is described by differential equations. Both architectures can be used for pattern recognition via associative memory, which occurs when a group of neurons fires synchronously. These models were inspi ...
... Hoppensteadt and Izhikevich [1] and Wang [2] have proposed network models where the neural activity is described by differential equations. Both architectures can be used for pattern recognition via associative memory, which occurs when a group of neurons fires synchronously. These models were inspi ...
A neural model of hierarchical reinforcement learning
... The next question is how to organize the model into a hierarchy, so that higher level decisions (e.g., “go to the grocery store”) can control the lower level decisions. Given the structure laid out above, this can be accomplished by allowing high level systems to set the context in lower level syste ...
... The next question is how to organize the model into a hierarchy, so that higher level decisions (e.g., “go to the grocery store”) can control the lower level decisions. Given the structure laid out above, this can be accomplished by allowing high level systems to set the context in lower level syste ...
Discrete Modeling of Multi-Transmitter Neural Networks with Neuron
... technologies and the growth of their applications. In particular, the algorithms of deep learning of multilayer neural networks are developing rapidly (LeCun, Bengio, & Hinton, 2015), (Goodfellow, Bengio, & Courville, 2016), where "depth" means the number of layers of the network. The range of probl ...
... technologies and the growth of their applications. In particular, the algorithms of deep learning of multilayer neural networks are developing rapidly (LeCun, Bengio, & Hinton, 2015), (Goodfellow, Bengio, & Courville, 2016), where "depth" means the number of layers of the network. The range of probl ...
Imitating the Brain with Neurocomputer A New Way towards Artificial
... structure, that is, the same structures (with similar functional components) will generate similar functions. It is New just because it reverses the order of understanding intelligence and making intelligence. Understand intelligence (the functions of the brain) is instead by analyzing the its struc ...
... structure, that is, the same structures (with similar functional components) will generate similar functions. It is New just because it reverses the order of understanding intelligence and making intelligence. Understand intelligence (the functions of the brain) is instead by analyzing the its struc ...
Artificial neural network
In machine learning and cognitive science, artificial neural networks (ANNs) are a family of statistical learning models inspired by biological neural networks (the central nervous systems of animals, in particular the brain) and are used to estimate or approximate functions that can depend on a large number of inputs and are generally unknown. Artificial neural networks are generally presented as systems of interconnected ""neurons"" which exchange messages between each other. The connections have numeric weights that can be tuned based on experience, making neural nets adaptive to inputs and capable of learning.For example, a neural network for handwriting recognition is defined by a set of input neurons which may be activated by the pixels of an input image. After being weighted and transformed by a function (determined by the network's designer), the activations of these neurons are then passed on to other neurons. This process is repeated until finally, an output neuron is activated. This determines which character was read.Like other machine learning methods - systems that learn from data - neural networks have been used to solve a wide variety of tasks that are hard to solve using ordinary rule-based programming, including computer vision and speech recognition.