# Tailored ensembles of neural networks optimize sensitivity to stimulus statistics

arXiv: https://arxiv.org/abs/1905.10401 · HTML: https://mlsanity.com/paper/1905.10401/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 May 2019.

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

- [Modular Growth of Hierarchical Networks: Efficient, General, and Robust Curriculum Learning](https://mlsanity.com/paper/2406.06262/) (2024)

## Abstract (arXiv)

The dynamic range of stimulus processing in living organisms is much larger than a single neural network can explain. For a generic, tunable spiking network we derive that while the dynamic range is maximal at criticality, the interval of discriminable intensities is very similar for any network tuning due to coalescence. Compensating coalescence enables adaptation of discriminable intervals. Thus, we can tailor an ensemble of networks optimized to the distribution of stimulus intensities, e.g., extending the dynamic range arbitrarily. We discuss potential applications in machine learning.