# CHANI: Correlation-based Hawkes Aggregation of Neurons with bio-Inspiration

arXiv: https://arxiv.org/abs/2405.18828 · HTML: https://mlsanity.com/paper/2405.18828/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 50 references that are in the map.
- First posted on arXiv 29 May 2024.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [On the Relationship between Self-Attention and Convolutional Layers](https://mlsanity.com/paper/1911.03584/) (2019)
- [Deep Learning in Spiking Neural Networks](https://mlsanity.com/paper/1804.08150/) (2018)
- [Birth of a Transformer: A Memory Viewpoint](https://mlsanity.com/paper/2306.00802/) (2023)
- [On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning](https://mlsanity.com/paper/1704.00805/) (2017)
- [Causal Interpretation of Self-Attention in Pre-Trained Transformers](https://mlsanity.com/paper/2310.20307/) (2023)
- [Tracking Dynamic Point Processes on Networks](https://mlsanity.com/paper/1409.0031/) (2014)
- [Modeling networks of spiking neurons as interacting processes with memory of variable length](https://mlsanity.com/paper/1502.06446/) (2015)
- [Event-scheduling algorithms with Kalikow decomposition for simulating potentially infinite neuronal networks](https://mlsanity.com/paper/1910.10576/) (2019)
- [Hawkes Process Multi-armed Bandits for Disaster Search and Rescue](https://mlsanity.com/paper/2004.01580/) (2020)

## Built on by (most cited first, 1 of 1)

- [Chemical Reaction Networks Learn Better than Spiking Neural Networks](https://mlsanity.com/paper/2603.12060/) (2026)

## Abstract (arXiv)

The present work aims at proving mathematically that a neural network inspired by biology can learn a classification task thanks to local transformations only. In this purpose, we propose a spiking neural network named CHANI (Correlation-based Hawkes Aggregation of Neurons with bio-Inspiration), whose neurons activity is modeled by Hawkes processes. Synaptic weights are updated thanks to an expert aggregation algorithm, providing a local and simple learning rule. We were able to prove that our network can learn on average and asymptotically. Moreover, we demonstrated that it automatically produces neuronal assemblies in the sense that the network can encode several classes and that a same neuron in the intermediate layers might be activated by more than one class, and we provided numerical simulations on synthetic dataset. This theoretical approach contrasts with the traditional empirical validation of biologically inspired networks and paves the way for understanding how local learning rules enable neurons to form assemblies able to represent complex concepts.