Biological Foundations

Explore the foundational biological concepts that inspire spiking neural networks, including neural anatomy, action potentials, synaptic transmission, and temporal coding in biological neural networks.

Learning Approaches

There are many approaches to implementing learning within spiking neural networks, determining an ideal categorisation will likely be an ongoing challenge. Here we describe the current categorisation of the approaches benchmarked on this site.

Neuron Models

Over the years many neuron models have been proposed, on this page various popular models are presented.

Network Architectures

Explore different SNN architectures including feed-forward networks, recurrent configurations, reservoir computing approaches, and hybrid architectures that combine multiple paradigms.

Implementation

Learn about practical aspects of implementing SNNs, including neuromorphic hardware platforms, simulation frameworks, optimization techniques, and benchmarking methodologies used to evaluate network performance.

Applications

Discover how SNNs are being applied in real-world scenarios, including computer vision, speech processing, robotics, and neuromorphic computing implementations.

Challenges and Future Directions

Explore current limitations in SNN development, open research questions in the field, and emerging trends that are shaping the future of neuromorphic computing and brain-inspired artificial intelligence.