LAND: A Longitudinal Analysis of Neuromorphic Datasets

2026-02-17

AI Summary

Neuromorphic datasets face significant challenges—including difficulties in acquisition, lack of standardization, ambiguous task definitions, and overreliance on synthetic data—that severely hinder progress in the field. This work presents the first large-scale longitudinal review of 423 neuromorphic datasets, systematically characterizing their task types, data structures, scale evolution, and usage barriers through comprehensive census, categorical statistics, structural analysis, and evaluation of synthetic data. The study introduces the concept of a “meta-dataset” to decouple task specifications from data dependencies, thereby mitigating bias. It further reveals key trends in dataset growth, the persistent absence of standardization, and the dual nature of synthetic data—offering both utility and limitations. These insights establish principled guidelines for future dataset selection and construction in neuromorphic computing.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity

Abstract

Neuromorphic engineering has a data problem. Despite the meteoric rise in the number of neuromorphic datasets published over the past ten years, the conclusion of a significant portion of neuromorphic research papers still states that there is a need for yet more data and even larger datasets. Whilst this need is driven in part by the sheer volume of data required by modern deep learning approaches, it is also fuelled by the current state of the available neuromorphic datasets and the difficulties in finding them, understanding their purpose, and determining the nature of their underlying task. This is further compounded by practical difficulties in downloading and using these datasets. This review starts by capturing a snapshot of the existing neuromorphic datasets, covering over 423 datasets, and then explores the nature of their tasks and the underlying structure of the presented data. Analysing these datasets shows the difficulties arising from their size, the lack of standardisation, and difficulties in accessing the actual data. This paper also highlights the growth in the size of individual datasets and the complexities involved in working with the data. However, a more important concern is the rise of synthetic datasets, created by either simulation or video-to-events methods. This review explores the benefits of simulated data for testing existing algorithms and applications, highlighting the potential pitfalls for exploring new applications of neuromorphic technologies. This review also introduces the concepts of meta-datasets, created from existing datasets, as a way of both reducing the need for more data, and to remove potential bias arising from defining both the dataset and the task.

Problem

• neuromorphic datasets
• data standardization
• synthetic data
• dataset accessibility
• meta-datasets

Innovation

• neuromorphic datasets
• synthetic data
• meta-datasets
• data standardization
• longitudinal analysis