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Background and Related Work
Modeling the lobster gastric mill circuit
Hopf Bifurcation and HopfHopping during Recurrent Learning
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activation function analysis approximation ARTISTE asymptotic backpropagation behavior biological BPTT center manifold chapter complex computation connected converge Cottrell curve derived desired trajectory differential equations discrete network discuss dynamical systems eigenvalues embedding error gradient example feedforward network Figure finite difference framework gap junctions gastric mill GM circuit gradient descent Hopf bifurcation Hopf-hopping Hopfield input INT1 iterated prediction network layer learned oscillation learning rate limit sets linear method network learns network trained neuromodulator neuron nonlinear nullclines orbit oscillation output parameter periodic attractors phase plane phase relationship phase space phase space reconstruction phase-space learning Pol oscillator possible prediction training reciprocal inhibition recurrent hidden units recurrent nets recurrent network recurrent neural networks recurrent training RTRL algorithm self-recurrent sigmoidal units simulations sine waves stable fixed point stable limit cycle subnetworks synapses tasks teacher forcing teacher signals train the network training set Tsung two-unit network values vector field visible units waveform weights