Spiking Neural Networks

Introduction Neurons are composed of three main parts: somatic cells (cell bodies), dendrites and axons. Somatic cells contain nuclei, dendrites accept synaptic inputs (neurotransmitters), and axons release neurotransmitters. The large branching structure at the end of dendrites and axons allows each neuron to connect to thousands of other neurons to form a large communication network. Neurons communicate via action potentials. The action potential is an electrical pulse traveling along the axon until it reaches the synapse where the synapse causes the release of the neurotransmitter.

A third-generation neural network, a pointed neural network, aims to fill the gap between neuroscience and machine learning using a biologically realistic neuron model for computation. The spike neural network (SNN) is fundamentally different from the neural network known to the machine learning community. SNN works with spikes. Spikes are not continuous values, but are individual events occurring at some point. The appearance of spikes is determined by differential equations representing various biological processes, the most important of which is the membrane potential of neurons. Essentially, when a neuron reaches a certain potential, it jumps up and the potential of the neuron is reset. The most common model is the Leaky integrated ignition (LIF) model. In addition, SNN is usually loosely coupled and uses a special network topology.

Spike neural network (SNN) is a third-generation neural network model that improves true level neural simulation. In addition to the state of neurons and the state of synapses, SNN incorporates the concept of time into its operational model. The idea is that SNN neurons are not released in all propagation cycles (as occurs in a typical multilayer perceptron network), but in the membrane potential - the intrinsic quality of the neurons associated with their membrane charges - It is only released. - When sending - reaches a specific value. When a neuron is released it generates a signal propagating to other neurons, which in turn increases or decreases their potential based on this signal.