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So the question was why do we use a feedforward model instead of a recurrent model?
We propose a new recurrent model that can form a broader range of stable population activity patterns.
The outcome VLSI functional blocks combine both current and voltage to represent the variables in the recurrent model.
This analog network belongs to an early recurrent model, which is electrically redesigned to take into account memristive arrays but keeping its original convergence properties.
We assessed the behavior and neuropathology of tumor-bearing animals treated with the combined gene therapy, 3 days after treatment, in long-term survivors, and in a recurrent model of glioma.
A comparative study between the proposed recurrent architecture, with the more popular feedforward neural network, the Jordan recurrent model, and some traditional parametric software reliability growth models are carried out.
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I work with canonical feed-forward models as well as recurrent models to compare different types of neuron non-linearities.
However, existing recurrent models are unable to veridically encode more than one orientation at a time.
In sum, our approach provides key architectural as well as image-level behavioral and neural constraints that will guide the next-generation recurrent models of the visual system.
However, recurrent models are able to store information of prior system states internally and could be therefore more appropriate for the analysis of dynamic systems.
Several recurrent models exist in the literature, especially as extensions to the Cox proportional hazard approach.
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