What Is Synaptic Plasticity?

In neuroscience, synaptic plasticity refers to the connection between nerve cells, that is, synapses, whose connection strength can be adjusted. A characteristic or phenomenon in which the morphology and function of a synapse can change over a longer period of time. Synapses will be strengthened and weakened as their activities strengthen and weaken. In artificial neural networks, synaptic plasticity refers to the use of synaptic plasticity-related theories in neuroscience combined with mathematical models to construct connections between neurons.

Synaptic plasticity mainly includes short-term synaptic plasticity and long-term synaptic plasticity. Short-term synaptic plasticity mainly includes facilitation, depression, and potentiation. The main manifestations of long-term synaptic plasticity are Long-term potentiation and

Overview of Synaptic Plasticity

In the field of machine learning and cognitive science, artificial neural network (ANN), or neural network (NN for short) or neural network-like, is a model that mimics biological neural networks A mathematical or computational model of the structure and function of a system, especially the brain), used to estimate or approximate functions. The neural network is calculated by a large number of artificial neurons. In most cases, the artificial neural network can change the internal structure based on external information and is an adaptive system. [Source request] Modern neural network is a non-linear statistical data modeling tool. A typical neural network has the following three parts:

Architecture specifies the variables in the network and their topological relationships. For example, the variables in a neural network can be the weights of neuron connections and the activities of the neurons.

Stimulation function (Activity Rule) Most neural network models have a short-term dynamics rule to define how neurons change their own stimulation values based on the activities of other neurons. The general incentive function depends on the weights in the network (that is, the parameters of the network).

Learning Rules Learning rules specify how weights in the network adjust over time. This is generally seen as a long-term dynamics rule. In general, learning rules depend on the stimulus value of the neuron. It may also depend on the target value provided by the supervisor and the value of the current weight.

Synaptic Plasticity

The characteristics and advantages of artificial neural networks are mainly manifested in three aspects:

First, it has a self-learning function. For example, when image recognition is implemented, only many different image templates and corresponding recognition results should be input into the artificial neural network first, and the network will slowly learn to recognize similar images through the self-learning function. The self-learning function is particularly important for prediction. It is expected that artificial neural network computers in the future will provide economic forecasts, market forecasts, and profit forecasts for human beings, and their application prospects are very promising.

Second, it has Lenovo memory function. This kind of association can be realized with the feedback network of artificial neural network.

Third, it has the ability to find optimal solutions at high speed. Finding an optimal solution to a complex problem often requires a large amount of calculation. Using a feedback-type artificial neural network designed for a certain problem and using the computer's high-speed computing capabilities, it may quickly find an optimal solution.