# Heterogeneous quantization regularizes spiking neural network activity

arXiv: https://arxiv.org/abs/2409.18396 · HTML: https://mlsanity.com/paper/2409.18396/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 41 references that are in the map.
- First posted on arXiv 27 Sep 2024.

## Builds on (most cited first, 3 of 3)

- [Rapid online learning and robust recall in a neuromorphic olfactory circuit](https://mlsanity.com/paper/1906.07067/) (2019)
- [Signal Conditioning for Learning in the Wild](https://mlsanity.com/paper/1907.05827/) (2019)
- [A Geometric Framework for Odor Representation](https://mlsanity.com/paper/2208.07455/) (2022)

## Abstract (arXiv)

The learning and recognition of object features from unregulated input has been a longstanding challenge for artificial intelligence systems. Brains are adept at learning stable representations given small samples of noisy observations; across sensory modalities, this capacity is aided by a cascade of signal conditioning steps informed by domain knowledge. The olfactory system, in particular, solves a source separation and denoising problem compounded by concentration variability, environmental interference, and unpredictably correlated sensor affinities. To function optimally, its plastic network requires statistically well-behaved input. We present a data-blind neuromorphic signal conditioning strategy whereby analog data are normalized and quantized into spike phase representations. Input is delivered to a column of duplicated spiking principal neurons via heterogeneous synaptic weights; this regularizes layer utilization, yoking total activity to the network's operating range and rendering internal representations robust to uncontrolled open-set stimulus variance. We extend this mechanism by adding a data-aware calibration step whereby the range and density of the quantization weights adapt to accumulated input statistics, optimizing resource utilization by balancing activity regularization and information retention.