Biological neural network

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Biological neural network refers to the interconnected web of neurons in the nervous system of an organism. It is the fundamental unit of the nervous system, responsible for receiving sensory input from the external world, processing and integrating the incoming signals, and transmitting the information to the muscles and glands to execute an appropriate response.

Structure[edit | edit source]

A biological neural network consists of billions of neurons, each connected to thousands of other neurons. The connections, known as synapses, allow for the transmission of signals from one neuron to another. Each neuron consists of a cell body, dendrites, and an axon. The cell body contains the nucleus and other organelles, while the dendrites receive incoming signals and the axon transmits them.

Function[edit | edit source]

The primary function of a biological neural network is to transmit signals throughout the body. These signals are electrical impulses generated by the neurons, which are triggered by either sensory input or other neurons. The network is capable of learning and memory due to the plasticity of synapses, which can strengthen or weaken over time based on the activity of the neurons.

Types[edit | edit source]

There are several types of biological neural networks, including the central nervous system, the peripheral nervous system, and the enteric nervous system. The central nervous system consists of the brain and spinal cord, the peripheral nervous system includes all the nerves outside the central nervous system, and the enteric nervous system is responsible for controlling the gastrointestinal system.

Research and Applications[edit | edit source]

Research into biological neural networks has led to significant advancements in various fields, including neuroscience, psychology, medicine, and artificial intelligence. Understanding the workings of these networks can help in the treatment of neurological disorders, the development of artificial neural networks, and the creation of more effective learning systems.

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Contributors: Prab R. Tumpati, MD