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Sparse Deep Learning for Time Series Data: Theory and Applications
Specifically, we lay the foundation within the Bayesian framework.
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Learning and Processing the Ordinal Information of Temporal Sequences in Recurrent Neural Circuits
Temporal sequence processing is fundamental in brain cognitive functions.
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Beyond Geometry: Comparing the Temporal Structure of Computation in Neural Circuits with Dynamical Similarity Analysis Mitchell Ostrow, Adam Eisen, Leo Kozachkov, Ila Fiete
Y et in recurrent networks, computations are implemented at the level of dynamics, and two networks performing the same computation with equivalent dynamics need not exhibit the same geometry.
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Training biologically plausible recurrent neural networks on cognitive tasks with long-term dependencies Supplementary material A Demand for biological RNNs with long time-scale dependencies
Emergence of grid-like representations by training recurrent neural networks to perform spatial localization.
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Learning better with Dale's Law: A Spectral Perspective
Dale's Law is generally absent from RNNs because simply
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