Deep Randomized Neural Networks
Randomized Neural Networks explore the behavior of neural systems where the majority of connections are fixed, either in a stochastic or a deterministic fashion. Typical examples of such systems consist of multi-layered neural network architectures where the connections to the hidden layer(s) are left untrained after initialization. Limiting the training algorithms to operate on a reduced set of w
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Fudan CCDC model (time delay dynamical system): $$ \begin{aligned} \frac{\mathrm{d} I}{\mathrm{d} t} &= r I_{0}(t) \\ \frac{\mathrm{d} J}{\mathrm{d} t} &= r \int_{-\infty}^{t} f_{4}(t-s) I_{0}(s) \mathrm{d} s \\ \frac{\mathrm{d} G}{\mathrm{d} t} &= \ell(t) \left[ \int_{-\infty}^{t} f_{2}(t-s) I_{0}(s) d s - \int_{-\infty}^{t} f_{4}(t-s) I_{0}(s) \mathrm{d} s \right] \\ \end{aligned} $$ $I(t)$: the
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