Continuous Attractor Neural Networks: Candidate of a Canonical Model for Neural Information Representation
نویسندگان
چکیده
منابع مشابه
Continuous Attractor Neural Networks: Candidate of a Canonical Model for Neural Information Representation.
Owing to its many computationally desirable properties, the model of continuous attractor neural networks (CANNs) has been successfully applied to describe the encoding of simple continuous features in neural systems, such as orientation, moving direction, head direction, and spatial location of objects. Recent experimental and computational studies revealed that complex features of external in...
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In this chapter a brief review is given of computational systems that are motivated by information processing in the brain, an area that is often called neurocomputing or artificial neural networks. While this is now a well studied and documented area, specific emphasis is given to a subclass of such models, called continuous attractor neural networks, which are beginning to emerge in a wide co...
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In this lecture I will present some models of neural networks that have been developed in the recent years. The aim is to construct neural networks which work as associative memories. Different attractors of the network will be identified as different internal representations of different objects. At the end of the lecture I will present a comparison among the theoretical results and some of th...
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An associative memory model and a neural network model with a Mexican-hat type interaction are the two most typical attractor networks used in the artificial neural network models. The associative memory model has discretely distributed fixed-point attractors, and achieves a discrete information representation. On the other hand, a neural network model with a Mexican-hat type interaction uses a...
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ژورنال
عنوان ژورنال: F1000Research
سال: 2016
ISSN: 2046-1402
DOI: 10.12688/f1000research.7387.1