Rare Events, Real Signals: Functional Ensembles as Units of Computation in Deep Spiking Networks
arXiv:2606.00073v1 Announce Type: cross Abstract: We investigate how internal representations emerge across hierarchical processing systems by introducing a neuroscience-inspired framework for analyzing deep spiking neural networks (SNN) through the lens of functional connectivity. Drawing on concepts from systems neuroscience and information theory, we form the first-order functionally-connected (1FC) group of a neuron based on its statistically significant pairwise correlations with neurons from the previous layer of a trained SNN architecture. We then track its response properties during in
The continuous evolution of AI research, particularly in biologically-inspired architectures like SNNs, drives ongoing exploration into more efficient and robust computational models.
This research explores fundamental principles of emergent representations in deep spiking neural networks, which could lead to advancements in AI efficiency and brain-computer interfaces.
Understanding how representations emerge in SNNs could enable more biologically plausible and potentially energy-efficient AI systems, diversifying optimization strategies beyond current deep learning paradigms.
- · AI researchers
- · Hardware developers for neuromorphic computing
- · Cognitive science
- · Traditional deep learning optimization methods (potentially, long-term)
Improved understanding of how information is processed and represented in advanced neural networks.
Development of more energy-efficient and robust AI models that mimic brain functions more closely.
New classes of AI applications in areas requiring low-power, event-driven computation, such as edge AI or advanced robotics.
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Read at arXiv cs.LG