In the domain of neural computation, Spiking Neural Networks (SNNs) are distinguished by their unique, biologically informed architecture which closely mirrors the brain's neuronal activity through discrete spike-based communication. This advanced capability positions SNNs at the forefront of the neural network evolution, providing a platform that significantly betters the efficiency and adaptability of information processing systems. Recent studies have highlighted the critical roles of synaptic plasticity, along with structural and temporal modifications, in enhancing the network's capacity for task-specific adaptation such as sequential learning and sensory processing. However, despite considerable progress, there remains a substantial gap in fully understanding and harnessing these properties to improve SNNs' overall performance and reliability in complex learning environments.

This Research Topic aims to elevate the comprehension and application of learning and adaptation in spiking neural networks. It intends to:

o Dissect the complex mechanisms of synaptic plasticity and adaptation, emphasizing the optimization of synaptic parameters like delays, weights, and adaptive strategies.
o Probe into neuron architecture competitions and their influence in enhancing supervised learning methodologies, with a special interest in spike-timing dependent plasticity and new learning protocols.
o Investigate the intertwined effects of structural and functional plasticity on the networks' adaptability and operational efficacy, focusing on applications in sequence learning and pattern recognition.
o Showcase practical implementations of SNNs in real-world contexts such as object recognition and feature extraction by utilizing adaptive timing methods and mixed network configurations.

To further enrich our understanding in this field, we welcome contributions that delve into topics including, but not limited to:

o Research advancing co-learning of synaptic delays, weights, and adaptations to boost SNN learning.
o New developments in STDP-based learning protocols like the Bi-Sigmoid model integrated with cutting-edge technologies such as magnetic tunnel junctions.
o Studies on how competing neuronal systems enhance STDP and supervised learning, aiming to improve the efficiency and precision of computations.
o Examination of how integrating structural plasticity into dynamic self-organizing networks enhances capabilities in sequence learning.
o Investigations into adaptive-time feature extraction techniques for comprehensive object tracking, showcasing SNNs' capabilities in handling complex sensory data.

This Research Topic calls for collaborative efforts across disciplines such as neural computation, artificial intelligence, and cognitive science to push the boundaries of neuro-inspired computational models.

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Frontiers in Neuroscience

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