Spiking neural networks (SNNs) are a type of artificial neural network that more closely mimic the way biological neurons communicate by generating discrete spikes or action potentials. This form of communication allows for temporal coding of information, making SNNs particularly effective for processing time-dependent data. The connectivity patterns and network topologies within SNNs play a crucial role in their learning and functioning, enabling more complex processing compared to traditional neural networks.
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