Biological neural network mainly refers to the neural network of human brain, which is the technical prototype of artificial neural network. The human brain is the material basis of human thinking, and the function of thinking is located in the cerebral cortex, which contains about101neurons, and each neuron is connected with about 103 other neurons through synapses, forming a highly complex and flexible dynamic network. As a discipline, biological neural network mainly studies the structure, function and working mechanism of human brain neural network, aiming at exploring the laws of human brain thinking and intelligent activities.
Artificial neural network is a technical representation of biological neural network in a simplified sense. As a discipline, its main task is to establish a practical artificial neural network model according to the principle of biological neural network and the needs of practical application, design corresponding learning algorithm, simulate some intelligent activities of human brain, and then realize it technically to solve practical problems. Therefore, biological neural network mainly studies the mechanism of intelligence; Artificial neural network mainly studies the realization of intelligent mechanism, and the two complement each other.
Extended data:
The research content of neural network is quite extensive, which embodies the characteristics of interdisciplinary technology field. The main research work focuses on the following aspects:
1, biological prototype
The biological prototype structure and functional mechanism of nerve cells, neural networks and nervous systems are studied from physiology, psychology, anatomy, brain science and pathology.
Step 2 build a model
Through the study of biological prototype, the theoretical models of neurons and neural networks are established. It includes conceptual model, knowledge model, physical and chemical model, mathematical model and so on.
3. Algorithm
On the basis of theoretical model research, a concrete neural network model is constructed to realize computer simulation or prepare hardware, including the research of network learning algorithm. This work is also called technology model research.
The algorithm used in neural networks is vector multiplication, and symbolic functions and their approximations are widely used. Parallelism, fault tolerance, hardware implementation and self-learning are several basic advantages of neural network, and they are also the differences between neural network calculation methods and traditional methods.
References:
Baidu Encyclopedia-Neural Network (Communication Definition)