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Found 4 papers, 2 papers with code
Harmonics of Learning: Universal Fourier Features Emerge in Invariant Networks
In this work, we formally prove that, under certain conditions, if a neural network is invariant to a finite group then its weights recover the Fourier transform on that group.
Bispectral Neural Networks
We present a neural network architecture, Bispectral Neural Networks (BNNs) for learning representations that are invariant to the actions of compact commutative groups on the space over which a signal is defined.
Biologically Plausible Sequence Learning with Spiking Neural Networks
Motivated by the celebrated discrete-time model of nervous activity outlined by McCulloch and Pitts in 1943, we propose a novel continuous-time model, the McCulloch-Pitts network (MPN), for sequence learning in spiking neural networks.
Deciphering subsampled data: adaptive compressive sampling as a principle of brain communication
A new algorithm is proposed for a) unsupervised learning of sparse representations from subsampled measurements and b) estimating the parameters required for linearly reconstructing signals from the sparse codes.
