Neural network (biology)

Your brain has many tiny parts.

These parts are called neurons. They connect to each other like a big web. One part can talk to many others. They use tiny signals to send messages. This helps your brain work and learn. Scientists study these webs to see how we think. They even use these ideas to make smart computers. It is amazing how these tiny parts work together!

Your brain uses a big web of tiny parts. These parts are called neurons. A group of neurons working together is a neural network.

Neurons connect to each other in many ways. They use parts called synapses to link up. One neuron can talk to many other neurons. They send signals using electricity. They also use chemicals to pass messages.

Scientists study these networks to learn how we think. They also use these ideas to make smart computers. These are called artificial neural networks. These computer models are inspired by our own brains. They help computers recognize speech or images. They even help robots move on their own.

Long ago, thinkers had different ideas about these networks. Alexander Bain thought that repeating an action makes connections stronger. He believed this was how we make memories. William James thought electrical currents moved through the neurons. Scientists still study these webs to understand how we learn and act every day.

A neural network is a large group of neurons working together. These neurons are tiny cells that connect to each other. You might think of them as a giant, living web. This web helps living things function and organize their systems. Scientists study these networks to learn how nervous systems work. They also look at how these systems process data.

These networks work through many different connections. Most connections happen at places called synapses. These links usually go from a part called an axon to a part called a dendrite. A single neuron can connect to many other neurons. This creates a very large and busy system. Neurons send signals using electricity. They can also use chemicals called neurotransmitters to pass messages.

People have studied these ideas for a long time. In 1873, Alexander Bain proposed a theory about these networks. He thought that repeating an activity makes connections stronger. He believed this repetition is how we form memories. In 1890, William James shared a similar idea. He suggested that electrical currents flow among the neurons. This helped explain how memories and actions happen.

Many scientists have added to this work over the years. In 1898, C. S. Sherrington tested electrical currents in rats. He discovered a concept called habituation during his tests. Later, in 1943, McCulloch and Pitts made a math model for networks. In 1956, a scientist named Svaetichin studied how retinal cells work. In 1986, Rumelhart and McClelland wrote about connectionism in computers. These researchers helped bridge the gap between biology and math.

We can see these ideas in our own technology today. Artificial neural networks are computer models inspired by our brains. They use math to act like real neural circuits. These models help computers recognize speech and analyze images. They are also used to make autonomous robots. They even help characters move in video games. These tools help us understand how biological systems learn.

A biological neural network is a large, interconnected group of neurons. These neurons work together as a population to form various neural circuits. Scientists study these networks to understand how nervous systems are organized and how they function. These biological systems are closely related to artificial neural networks. Artificial neural networks are machine learning models that use mathematical functions to mimic biological mechanisms. By studying these living networks, researchers can better understand how the brain processes information and manages complex tasks.

The functioning of a neural network depends on how neurons communicate. Most connections occur at specific points called synapses. These synapses usually form between an axon, which is a long part of a neuron, and a dendrite, which is a branching part. While these are the most common connections, other types like dendrodendritic synapses are also possible. Communication happens through electrical signaling between cells. Additionally, signaling can occur through the diffusion of neurotransmitters, which are chemical messengers. A single neuron can connect to many others, creating an extensive web of communication.

Researchers use different types of models to study these complex systems. These models exist at various levels of abstraction. Some models focus on the short-term behavior of a single individual neuron. Other models look at the dynamics of neural circuitry created by many interacting neurons. Some even represent complete subsystems through abstract neural modules. Scientists also create models to study plasticity, which refers to how neural systems change. This includes studying both short-term and long-term plasticity to understand how learning and memory work at the system level.

The history of neural network theory began in the late 19th century. In 1873, Alexander Bain proposed that thoughts and body activities come from neuron interactions. He argued that repeating an activity strengthens connections between neurons, which forms memories. In 1890, William James proposed a similar idea. He suggested that electrical currents flowing among neurons create memories and actions. James’ model was unique because it did not require a separate connection for every single memory. Instead, it focused on the continuous flow of electrical currents through the network.

In 1898, C. S. Sherrington tested James' theory by running electrical currents through the spinal cords of rats. He found that the electrical current strength actually decreased over time. This important experiment led to the discovery of habituation. Later, in 1943, McCulloch and Pitts developed a computational model called threshold logic. This model used mathematics and algorithms to simulate networks. By 1956, Svaetichin discovered how certain retinal cells, known as Horizontal Cells, use an opponency mechanism. This helped explain how the visual system processes its first layer of information.

In the mid-1980s, the concept of connectionism became popular. This approach involves using computers to simulate neural processes through parallel distributed processing. A text by Rumelhart and McClelland in 1986 provided a full explanation of this method. Today, artificial neural networks are used in many practical ways. They are applied to speech recognition, image analysis, and adaptive control. These technologies allow for the creation of software agents in video games and the development of autonomous robots. While these models are inspired by biology, scientists still debate how closely they actually mirror the brain's architecture.

Modern neuroscience uses theoretical and computational methods to analyze these systems. Neuroscientists try to link biological data with plausible mechanisms for learning. Recent research has also moved beyond just electrical characteristics. Scientists now explore how neuromodulators like dopamine, acetylcholine, and serotonin affect behavior and learning. In August 2020, researchers found that adding bi-directional or feedback connections can improve communication. These feedback connections help pulses move successfully through the cerebral cortex. Understanding these connections helps us grasp the deep relationship between biology, computing, and behavior.