Path Integration and Cognitive Mapping in a Continuous Attractor Neural Network Model
نویسندگان
چکیده
منابع مشابه
Path integration and cognitive mapping in a continuous attractor neural network model.
A minimal synaptic architecture is proposed for how the brain might perform path integration by computing the next internal representation of self-location from the current representation and from the perceived velocity of motion. In the model, a place-cell assembly called a "chart" contains a two-dimensional attractor set called an "attractor map" that can be used to represent coordinates in a...
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A key issue is how networks in the brain learn to perform path integration, that is update a represented position using a velocity signal. Using head direction cells as an example, we show that a competitive network could self-organize to learn to respond to combinations of head direction and angular head rotation velocity. These combination cells can then be used to drive a continuous attracto...
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Grid cells in the rat entorhinal cortex display strikingly regular firing responses to the animal's position in 2-D space and have been hypothesized to form the neural substrate for dead-reckoning. However, errors accumulate rapidly when velocity inputs are integrated in existing models of grid cell activity. To produce grid-cell-like responses, these models would require frequent resets trigge...
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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...
متن کاملDynamical properties of continuous attractor neural network with background tuning
Persistent activity holds the transient stimulus for up to many seconds even after the stimulus is gone. It has been implemented in a class of models known as continuous attractor neural networks, which have infinite stable states corresponding to persistent activity patterns. Continuous attractor neural network remains stable so does not change systematically in the absence of stimulus input. ...
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ژورنال
عنوان ژورنال: The Journal of Neuroscience
سال: 1997
ISSN: 0270-6474,1529-2401
DOI: 10.1523/jneurosci.17-15-05900.1997