Spiking Neural Networks and Online Learning: An Overview and Perspectives (1908.08019v1)

Abstract: Applications that generate huge amounts of data in the form of fast streams are becoming increasingly prevalent, being therefore necessary to learn in an online manner. These conditions usually impose memory and processing time restrictions, and they often turn into evolving environments where a change may affect the input data distribution. Such a change causes that predictive models trained over these stream data become obsolete and do not adapt suitably to new distributions. Specially in these non-stationary scenarios, there is a pressing need for new algorithms that adapt to these changes as fast as possible, while maintaining good performance scores. Unfortunately, most off-the-shelf classification models need to be retrained if they are used in changing environments, and fail to scale properly. Spiking Neural Networks have revealed themselves as one of the most successful approaches to model the behavior and learning potential of the brain, and exploit them to undertake practical online learning tasks. Besides, some specific flavors of Spiking Neural Networks can overcome the necessity of retraining after a drift occurs. This work intends to merge both fields by serving as a comprehensive overview, motivating further developments that embrace Spiking Neural Networks for online learning scenarios, and being a friendly entry point for non-experts.

• The paper provides an overview of integrating Spiking Neural Networks (SNNs) with Online Learning (OL), highlighting their potential for processing dynamic data streams in evolving environments.
• It argues that SNNs are well-suited for OL due to their intrinsic temporal processing capabilities and computational efficiency.
• The review discusses applications in areas like IoT and Green AI, outlining challenges and future directions such as lifelong learning and efficient information encoding.

Spiking Neural Networks and Online Learning: An Overview and Perspectives

The paper, titled "Spiking Neural Networks and Online Learning: An Overview and Perspectives," authored by Lobo et al., systematically explores the integration of Spiking Neural Networks (SNNs) within Online Learning (OL) frameworks. SNNs, often considered the third generation of neural networks, possess a unique biological plausibility, reflecting more intricate dynamics of information processing observed in mammalian brains. This integrative approach holds substantial potential for addressing evolving data streams in various applications.

Theoretical Framework and Core Concepts

The authors initiate the discussion by elucidating the vital need for OL methods, especially as data generation becomes ubiquitous with applications spanning mobile technologies, sensor networks, and industrial processes, among others. In such non-stationary environments, models that do not adapt to changing data distributions—the phenomena known as concept drift—become quickly obsolete. A pivotal aspect of OL in these scenarios is the ability to process incoming data sequentially with minimum latency and resource constraints.

The paper argues for the value of SNNs within OL due to their intrinsic temporal processing capabilities and efficiency in neural computation, primarily leveraging spike-based dynamics. SNNs emulate the spike-timing dependent plasticity observed in biological neurons, which is inherently suitable for modeling temporal data patterns and adapting to environmental changes.

Numerical Results and Key Findings

While the paper provides a comprehensive conceptual overview rather than extensive empirical results, it emphasizes particularly on the adaptability and efficiency of SNNs compared to traditional Artificial Neural Networks (ANNs) in OL settings. SNNs, through architectures such as the eSNN, offer distinct benefits in evolving environments, showcasing capabilities in efficiently accumulating and adapting knowledge in real-time.

Applications and Implications

OL's capacity to adapt dynamics is crucial in applications like the Internet of Things (IoT), where continuous data flow necessitates rapid processing and learning. By implementing SNNs, IoT systems and similar applications can potentially harness a robust and adaptive learning paradigm that emulates human neurological processes in managing and responding to streaming data.

Furthermore, the paper suggests the potential of SNNs for applications demanding reduced computational resources, thus contributing to the Green AI paradigm. Given their architecture and functionality, SNNs may facilitate improved energy efficiencies in learning systems by reducing computational overhead and accelerating neural processing.

Future Directions and Challenges

The intricate relationship between SNNs and OL poses exciting research prospects. Future efforts could focus on extending learning within SNN architectures, particularly utilizing deep learning concepts to enhance their processing capability of spatio-temporal patterns. Additionally, realizing effective information encoding mechanisms within SNN frameworks remains a crucial area for innovation, given its direct impact on learning efficacy.

Moreover, the authors foresee the integration of SNNs in Lifelong Machine Learning (LML), enabling models to dynamically amend their knowledge base across various tasks and datasets throughout extended timelines. Overcoming the challenges related to the heterogeneity and sparse communication within SNNs could further bolster their utility across broader applications, including real-time human-computer interaction.

Conclusion

Through a comprehensive analysis, this paper lays a foundational pathway for coupling SNNs with OL frameworks. While numerous challenges persist—ranging from model standardization to efficient learning rule development—the integration promises enhanced model adaptability in dynamically shifting environments. The pursuit of merging these biologically inspired networks with advanced OL strategies holds the promise of not only resolving contemporary computational challenges but also advancing intelligent data-driven decision-making.