Spiking Neural Networks
Overview
Spiking Neural Networks (SNN) are a type of artificial neural network inspired by biological neurons. In SNN, when the membrane potential of a neuron exceeds a certain threshold, it generates a discrete signal known as a "spike," which is used to drive the network's computations. Unlike traditional neural networks, which process continuous signals, SNN convert input signals into binary spike trains (spiking or non-spiking events) and perform computations based on these discrete events. SNN offer two significant advantages over conventional artificial neural networks. The first advantage is that SNN model biological neural activity in a more detailed and biologically plausible manner. Traditional neural networks typically handle information as real-valued signals. However, the brain processes information through binary spike trains, which are electrical pulses transmitted between neurons. By utilizing binary signals, SNN more closely resemble the activity of neurons in the human brain, making them valuable for simulating brain function and conducting neuroscience experiments. The second advantage of SNN is their energy efficiency. Because SNNs rely on binary signals for computation, they can describe input-output relationships using only addition operations. In contrast to multiplication-based operations used in conventional networks, addition operations generally require fewer computational resources and lower power consumption, making SNNs more power-efficient.
Challenges of Spiking Neural Networks (SNNs)
SNN are known to pose significant challenges in network training. The back propagation algorithm, commonly used in traditional neural networks, cannot be directly applied to SNN due to the discrete nature of spikes. As a result, there is a need for the development of specialized algorithms tailored specifically for SNN. Additionally, in SNN, the timing of spikes and the occurrence or absence of firing events play a critical role, which makes the network more susceptible to noise and variations in input data. Furthermore, because the timing and sequence of spikes are crucial in SNN, computations that account for temporal dynamics are essential. Therefore, hardware capable of efficiently processing these temporal aspects is required.
Example of a Spiking Neural Network
The Leaky Integrate-and-Fire (LIF) Model
The Leaky Integrate-and-Fire (LIF) model is a commonly used spiking neuron model, which describes the dynamics of the membrane potential V(t) as follows: <math> \tau_m \frac{dV(t)}{dt} = (-V(t) + E_{\text{rest}}) + I(t) </math> where \(V(t)\) is the membrane potential, \(I(t)\) is the input current, \(E_{\text{rest}}\) is the resting membrane potential, and \(\tau_m\) is the membrane time constant.
