# Neuromorphic Online Clustering and Its Application to Spike Sorting

arXiv: https://arxiv.org/abs/2506.12555 · HTML: https://mlsanity.com/paper/2506.12555/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 15 references that are in the map.
- First posted on arXiv 14 Jun 2025.

## Builds on (most cited first, 4 of 4)

- [Why Neurons Have Thousands of Synapses, A Theory of Sequence Memory in Neocortex](https://mlsanity.com/paper/1511.00083/) (2015)
- [Implementing Online Reinforcement Learning with Temporal Neural Networks](https://mlsanity.com/paper/2204.05437/) (2022)
- [Neuromorphic Online Clustering and Classification](https://mlsanity.com/paper/2310.17797/) (2023)
- [A Macrocolumn Architecture Implemented with Spiking Neurons](https://mlsanity.com/paper/2207.05081/) (2022)

## Abstract (arXiv)

Active dendrites are the basis for biologically plausible neural networks possessing many desirable features of the biological brain including flexibility, dynamic adaptability, and energy efficiency. A formulation for active dendrites using the notational language of conventional machine learning is put forward as an alternative to a spiking neuron formulation. Based on this formulation, neuromorphic dendrites are developed as basic neural building blocks capable of dynamic online clustering. Features and capabilities of neuromorphic dendrites are demonstrated via a benchmark drawn from experimental neuroscience: spike sorting. Spike sorting takes inputs from electrical probes implanted in neural tissue, detects voltage spikes (action potentials) emitted by neurons, and attempts to sort the spikes according to the neuron that emitted them. Many spike sorting methods form clusters based on the shapes of action potential waveforms, under the assumption that spikes emitted by a given neuron have similar shapes and will therefore map to the same cluster. Using a stream of synthetic spike shapes, the accuracy of the proposed dendrite is compared with the more compute-intensive, offline k-means clustering approach. Overall, the dendrite outperforms k-means and has the advantage of requiring only a single pass through the input stream, learning as it goes. The capabilities of the neuromorphic dendrite are demonstrated for a number of scenarios including dynamic changes in the input stream, differing neuron spike rates, and varying neuron counts.