A spiking neural network (SNN) is a type of artificial neural network that mimics the behavior of biological neurons more closely than traditional neural networks. Unlike standard artificial neural networks (ANNs), which process data using continuous numerical values (like activation levels), SNNs communicate through discrete electrical events called “spikes.” These spikes occur at specific times, allowing SNNs to encode information in both the timing and frequency of signals. This approach is inspired by how biological neurons fire action potentials, making SNNs particularly suited for tasks that involve temporal data or require energy-efficient processing.
SNNs consist of neurons connected by synapses, similar to ANNs, but with key differences in how they operate. Each neuron in an SNN accumulates input spikes over time, and when its internal state reaches a threshold, it fires a spike to downstream neurons. This mechanism is often modeled using equations like the Leaky Integrate-and-Fire (LIF) model, which accounts for the neuron’s membrane potential and its gradual decay. Synapses in SNNs can also adapt their strengths through rules like Spike-Timing-Dependent Plasticity (STDP), where the timing of pre- and post-synaptic spikes determines whether a connection strengthens or weakens. For example, if a neuron fires just before another, the connection between them may strengthen, reinforcing causal relationships in the data.
SNNs are used in applications where timing and energy efficiency matter, such as robotics, neuromorphic hardware, and real-time sensory processing. Neuromorphic chips like Intel’s Loihi or IBM’s TrueNorth are designed to run SNNs efficiently, leveraging their event-driven nature to reduce power consumption. However, training SNNs remains challenging because traditional backpropagation isn’t easily applicable to spike-based signals. Developers often use surrogate gradients or convert SNNs into equivalent ANN models for training before deploying them on neuromorphic systems. Despite these hurdles, SNNs offer unique advantages for low-power, real-time tasks, such as processing data from event-based cameras or building adaptive control systems in robotics. Research continues to improve training methods and integrate SNNs with conventional machine learning frameworks.