Cell body (Soma): The core part of a neuron, including the nucleus and organelles, is the metabolic and genetic control center of the cell.

Dendrites: Branched structures extending from the cell body. Their surface is covered with synapses. They are mainly responsible for receiving signals from other neurons.

Axon: A long, thin fiber that extends from the cell body and is responsible for transmitting electrical signals to other neurons or muscle cells.

Synapse: The connection point between neurons, consisting of the presynaptic membrane (the axon terminal of the sending neuron), the synaptic cleft (the tiny space between two neurons), and the postsynaptic membrane (receiving The dendrites or cell bodies of neurons).

Resting potential: When a neuron is not stimulated, there is a potential difference between the inside and outside of the cell membrane, which is usually negative (about -70 millivolts) and is mainly maintained by the outflow of potassium ions.

Action potential: When the dendrite receives an excitatory signal of sufficient strength, the sodium ion channel opens and sodium ions flow in, causing the membrane potential to rise to the threshold (about -55 millivolts), triggering the action potential.

Electrical conduction: Action potentials propagate along axons at speeds ranging from tens to hundreds of meters per second, determined by the myelin insulation of the axon and the sequential activation of ion channels.

Neurotransmitter release: When the action potential reaches the axon terminal, it causes calcium ions to enter the cell, triggers the fusion of synaptic vesicles and the presynaptic membrane, and releases neurotransmitters into the synaptic cleft.

Synaptic cleft: Neurotransmitters diffuse through the synaptic cleft to the postsynaptic membrane. This process is very rapid, usually on the order of milliseconds.

Receptor binding: Neurotransmitters bind to specific receptors on the postsynaptic membrane, causing ion channels to open or close, changing the membrane potential, and generating excitatory or inhibitory postsynaptic potentials.

Signal integration: The dendrite of a postsynaptic neuron may receive signals from multiple axons. These signals are integrated at the cell body to determine whether the threshold is reached to generate a new action potential.

Long-term potentiation (LTP): The efficiency of synaptic transmission is enhanced through repeated excitatory stimulation, which is one of the key mechanisms of learning and memory. LTP involves an increase in the number of postsynaptic membrane receptors, an increase in receptor sensitivity, and an increase in the efficiency of neurotransmitter release from the presynaptic membrane.

Long-term depression (LTD): The efficiency of synaptic transmission is weakened by repeated inhibitory stimulation, opposite to LTP. LTD involves a decrease in the number or sensitivity of postsynaptic membrane receptors.

Synaptic pruning: During development, inactive synapses are eliminated and active synapses are strengthened as a way for the brain to adapt to changes in the environment. Synaptic pruning helps optimize the structure of neural networks and improve the efficiency of information processing.

Neuron model: Artificial neurons in ANNs (artificial neural networks) usually contain inputs (dendrites), weights (strength of synapses), activation functions (simulating the action of neurotransmitters) and outputs (axon terminals).

Weight update: In ANNs, weights are adjusted by a learning algorithm (such as backpropagation) to minimize prediction error, similar to synaptic plasticity. The purpose of weight update is to enhance the network's ability to recognize input data.

Activation function: The activation function in ANNs simulates the nonlinear response of neurons, such as ReLU (Rectified Linear Unit) or Sigmoid function, which determines whether the neuron "activates" and transmits signals. The choice of activation function has an important impact on the learning ability and performance of the network.

Network structure: ANNs can have multiple layers of neurons to form complex network structures, similar to the hierarchical organization of neurons in the brain. Deep learning networks, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), simulate different levels of information processing and time series processing in the brain.