# Neuromorphic Artificial Intelligence Systems

arXiv: https://arxiv.org/abs/2205.13037 · HTML: https://mlsanity.com/paper/2205.13037/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2022.

## Built on by (most cited first, 12 of 12)

- [PLEIADES: Building Temporal Kernels with Orthogonal Polynomials](https://mlsanity.com/paper/2405.12179/) (2024)
- [Neural Network Compression for Reinforcement Learning Tasks](https://mlsanity.com/paper/2405.07748/) (2024)
- [Temporal-Difference Learning Using Distributed Error Signals](https://mlsanity.com/paper/2411.03604/) (2024)
- [Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning](https://mlsanity.com/paper/2412.09379/) (2024)
- [Three-Factor Learning in Spiking Neural Networks: An Overview of Methods and Trends from a Machine Learning Perspective](https://mlsanity.com/paper/2504.05341/) (2025)
- [Augmenting learning in neuro-embodied systems through neurobiological first principles](https://mlsanity.com/paper/2407.04525/) (2024)
- [Improving the adaptive and continuous learning capabilities of artificial neural networks: Lessons from multi-neuromodulatory dynamics](https://mlsanity.com/paper/2501.06762/) (2025)
- [A Theory of Synaptic Neural Balance: From Local to Global Order](https://mlsanity.com/paper/2405.09688/) (2024)
- [Towards efficient keyword spotting using spike-based time difference encoders](https://mlsanity.com/paper/2503.15402/) (2025)
- [Spiffy: Efficient Implementation of CoLaNET for Raspberry Pi](https://mlsanity.com/paper/2506.18306/) (2025)
- [NeuroTrain: Surveying Local Learning Rules for Spiking Neural Networks with an Open Benchmarking Framework](https://mlsanity.com/paper/2605.15058/) (2026)
- [Hybrid ANN-SNN Pipeline with Local Plasticity](https://mlsanity.com/paper/2606.20151/) (2026)

## Abstract (arXiv)

Modern AI systems, based on von Neumann architecture and classical neural networks, have a number of fundamental limitations in comparison with the brain. This article discusses such limitations and the ways they can be mitigated. Next, it presents an overview of currently available neuromorphic AI projects in which these limitations are overcame by bringing some brain features into the functioning and organization of computing systems (TrueNorth, Loihi, Tianjic, SpiNNaker, BrainScaleS, NeuronFlow, DYNAP, Akida). Also, the article presents the principle of classifying neuromorphic AI systems by the brain features they use (neural networks, parallelism and asynchrony, impulse nature of information transfer, local learning, sparsity, analog and in-memory computing). In addition to new architectural approaches used in neuromorphic devices based on existing silicon microelectronics technologies, the article also discusses the prospects of using new memristor element base. Examples of recent advances in the use of memristors in euromorphic applications are also given.