Spiking Neural Networks and Their Applications: A Review

ArticleKashu Yamazaki, Viet-Khoa Vo-Ho, Darshan Bulsara, Ngan LeResearch

A comprehensive review examines Spiking Neural Networks (SNNs) as the third generation of artificial neural networks. SNNs communicate via biologically inspired discrete spikes in continuous time, offering substantial energy and computational efficiency gains over standard deep neural networks for autonomous robotics and edge deployment.

Key points

  1. Standard deep neural networks are computationally heavy and power-hungry, limiting real-time deployment on power-constrained platforms like autonomous vehicles and drones.
  2. SNNs replicate biological neural mechanics, passing information using discrete spike events instead of scalar continuous activations.
  3. Deploying SNNs on neuromorphic hardware such as Loihi, TrueNorth, or SpiNNaker delivers substantial power efficiency due to event-driven sparsity.
  4. Training SNNs remains challenging due to the non-differentiable nature of spiking events and complex temporal neuron dynamics.
  5. The paper systematically details biological neuron and synapse models, mathematical spike dynamics, training methods, software toolkits, and applications across robotics and computer vision.

Why it matters

Real-time autonomy on resource-constrained platforms demands compute paradigms far more efficient than conventional GPUs running dense deep learning models. Neuromorphic architectures running SNNs provide an alternative pathway towards low-power, high-speed intelligent processing at the edge.

Analysis

The authors systematically bridge the gap between biological neuroscience and modern machine learning. Whereas conventional compression methods like pruning and quantization merely optimise standard continuous-valued networks, SNNs rethink computation fundamentally around temporal sparsity. However, until standard surrogate gradient frameworks and developer toolkits mature sufficiently to match the ease of use of standard deep learning ecosystems, broad industrial adoption will remain closely tied to specialised neuromorphic hardware deployments.

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Summary generated by AI (Gemini 3.8 Flash, 27 September 2026). It may contain errors – the original source is authoritative.

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