NSS, a two-layer LCA-based spiking neural network with 2-bit graded spikes, sorts tetrode spikes unsupervised and runs inference on Loihi 2, improving drift F1 over a LIF version at modest power cost.
The Sparse Manifold Transform
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abstract
We present a signal representation framework called the sparse manifold transform that combines key ideas from sparse coding, manifold learning, and slow feature analysis. It turns non-linear transformations in the primary sensory signal space into linear interpolations in a representational embedding space while maintaining approximate invertibility. The sparse manifold transform is an unsupervised and generative framework that explicitly and simultaneously models the sparse discreteness and low-dimensional manifold structure found in natural scenes. When stacked, it also models hierarchical composition. We provide a theoretical description of the transform and demonstrate properties of the learned representation on both synthetic data and natural videos.
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cs.NE 1years
2025 1verdicts
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Unsupervised Sparse Coding-based Spiking Neural Network for Real-time Spike Sorting
NSS, a two-layer LCA-based spiking neural network with 2-bit graded spikes, sorts tetrode spikes unsupervised and runs inference on Loihi 2, improving drift F1 over a LIF version at modest power cost.