# Intrinsic Numerical Robustness and Fault Tolerance in a Neuromorphic Algorithm for Scientific Computing

arXiv: https://arxiv.org/abs/2603.10246 · HTML: https://mlsanity.com/paper/2603.10246/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 27 references that are in the map.
- First posted on arXiv 10 Mar 2026.

## Builds on (most cited first, 6 of 6)

- [The SpiNNaker 2 Processing Element Architecture for Hybrid Digital Neuromorphic Computing](https://mlsanity.com/paper/2103.08392/) (2021)
- [Solving Sparse Finite Element Problems on Neuromorphic Hardware](https://mlsanity.com/paper/2501.10526/) (2025)
- [Solving a steady-state PDE using spiking networks and neuromorphic hardware](https://mlsanity.com/paper/2005.10904/) (2020)
- [Neuromorphic scaling advantages for energy-efficient random walk computation](https://mlsanity.com/paper/2107.13057/) (2021)
- [Evaluating the Impact of SDC on the GMRES Iterative Solver](https://mlsanity.com/paper/1311.6505/) (2013)
- [Evaluating complexity and resilience trade-offs in emerging memory inference machines](https://mlsanity.com/paper/2003.10396/) (2020)

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

- [NeuroAI and Beyond: Bridging Between Advances in Neuroscience and Artificial Intelligence](https://mlsanity.com/paper/2604.18637/) (2026)

## Abstract (arXiv)

The potential for neuromorphic computing to provide intrinsic fault tolerance has long been speculated, but the brain's robustness in neuromorphic applications has yet to be demonstrated. Here, we show that a previously described, natively spiking neuromorphic algorithm for solving partial differential equations is intrinsically tolerant to structural perturbations in the form of ablated neurons and dropped spikes. The tolerance band for these perturbations is large: we find that as many as 32 percent of the neurons and up to 90 percent of the spikes may be entirely dropped before a significant degradation in the accuracy results. Furthermore, this robustness is tunable through structural hyperparameters. This work demonstrates that the specific brain-like inspiration behind the algorithm contributes to a significant degree of robustness expected from brain-like neuromorphic algorithms.