The phrase "continuous attractor neural network" is correct and usable in written English.
You can use it when referring to a neural network system that uses attractor states to continually learn and update information. For example, "The continuous attractor neural network allows for more dynamically updated and accurate data processing than previous systems."
This includes not only continuous attractor neural networks, but also discrete attractor neural networks such as Hopfield networks with graded neuronal responses [2].
A common approach used to analyze continuous attractor neural networks is to approximate the N-dimensional system of ordinary differential equations (Eq. (1)) by a partial differential equation by taking the limit as (Nrightarrowinfty).
In relatively small environments, the megamap behaves similarly to standard continuous attractor neural networks, operating in the winner-take-all (WTA) mode whereby the equilibrium state fully represents one input while effectively ignoring the second input.
In this model, the system shows a sharp transition from the winner-take-all mode, which is characteristic of standard continuous attractor neural networks, to a combinatorial mode in which the equilibrium activity pattern combines embedded attractor states in response to conflicting external inputs.
While we focus on a particular attractor neural network, the results apply to a broad class of attractor network models.
This link is modeled as a time delay attractor neural network.
In the current study, we analyze an attractor neural network's response to conflicting external inputs that effectively create a competition between embedded attractor states.
In this paper we analyze the behavior of an attractor neural network driven by two conflicting external inputs.
The reduced 2-unit model applies to any attractor neural network in which the four approximations outlined in Sect.
We present a mathematical analysis of the properties of the megamap attractor neural network that emerge when the network represents a sufficiently large spatial environment [10].
Relatively little is known about how an attractor neural network responds to external inputs, which often carry conflicting information about a stimulus.
