# Neuromorphic Online Clustering and Classification

arXiv: https://arxiv.org/abs/2310.17797 · HTML: https://mlsanity.com/paper/2310.17797/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Oct 2023.

## Built on by (most cited first, 2 of 2)

- [Implementing Online Reinforcement Learning with Clustering Neural Networks](https://mlsanity.com/paper/2402.18472/) (2024)
- [Neuromorphic Online Clustering and Its Application to Spike Sorting](https://mlsanity.com/paper/2506.12555/) (2025)

## Abstract (arXiv)

The bottom two layers of a neuromorphic architecture are designed and shown to be capable of online clustering and supervised classification. An active spiking dendrite model is used, and a single dendritic segment performs essentially the same function as a classic integrate-and-fire point neuron. A single dendrite is then composed of multiple segments and is capable of online clustering. Although this work focuses primarily on dendrite functionality, a multi-point neuron can be formed by combining multiple dendrites. To demonstrate its clustering capability, a dendrite is applied to spike sorting, an important component of brain-computer interface applications. Supervised online classification is implemented as a network composed of multiple dendrites and a simple voting mechanism. The dendrites operate independently and in parallel. The network learns in an online fashion and can adapt to macro-level changes in the input stream. Achieving brain-like capabilities, efficiencies, and adaptability will require a significantly different approach than conventional deep networks that learn via compute-intensive back propagation. The model described herein may serve as the foundation for such an approach.