The phrase "a recurrent model of" is correct and usable in written English.
It can be used in contexts related to theories, frameworks, or systems that repeat or cycle through certain processes or patterns.
Example: "The researchers developed a recurrent model of economic behavior to better understand market fluctuations."
Alternatives: "a repetitive framework of" or "a cyclic model of".
We propose a new recurrent model that can form a broader range of stable population activity patterns.
Wolfrum et al. suggested a recurrent network model for processing of both detection and identification of a face on an input image [22].
We cast our observations in terms of a recurrent neural network model of choice, providing formal links to mechanistic dynamical accounts of decision-making.
It turns out that a deep layering of recurrent models allows an effective diversification of temporal representations in the layers of the hierarchy, by amplifying the effects of the factors influencing the time-scales and the richness of the dynamics, measured as the entropy of recurrent units activations.
Similarly to Linster and colleagues, Getz & Lutz [16] used a detailed recurrent network model of LN and PN populations to explain the emergence of odour specific response patterns that allowed odour quality recognition for varying concentrations.
This suggest for a significantly better detection performance of our recurrent model compared to feedforward processing-based junction detection schemes.
Herzog & Fahle [49] put forward a recurrent neural network model of perceptual learning that empahsizes the role of plasticity in the top-down connections as an enabling process for perceptual learning.
The approach is based on a recurrent NARX model obtained by linear combination of a recurrent neural network (RNN) output and the real data output.
With these extensions, the resulting model corresponds to an integration of a hierarchical and recurrent models as advocated by Martinez and Alonso (2003).
