How Does a Biological Neural Network Work?

A biological neural network is the web of nerve cells, known as neurons, that forms the foundation of the brain and nervous system. This network is responsible for processing information, enabling thought, learning, and memory. It functions as a living computational machine where billions of neurons communicate to control bodily functions and conscious reasoning. This network operates through electrical and chemical signaling and constantly adapts based on new experiences, which allows for lifelong learning.

The Fundamental Components of the Network

The primary information-processing unit of the nervous system is the neuron. Each neuron is a specialized cell designed to transmit information to other nerve cells, muscles, or glands. While neurons vary in shape and size depending on their location and function, most share three parts that allow them to perform their communication role.

The core of the neuron is the cell body, or soma. The soma contains the nucleus, which houses the cell’s genetic material, and other organelles responsible for producing the energy and proteins the neuron needs to function. Think of the soma as the neuron’s command center, maintaining its structure and providing the resources to drive its activities. It integrates the signals received from other cells before deciding whether to pass the information along.

Branching out from the cell body are dendrites, which act as the primary receivers of incoming signals. These fibrous, tree-like extensions are covered in tiny protrusions called spines, which increase the surface area available for connecting with other neurons. Dendrites collect chemical signals from neighboring nerve cells and convert them into electrical impulses that travel toward the soma. A single neuron can have thousands of these dendritic branches, allowing it to receive information from a multitude of sources simultaneously.

Extending from the cell body is a long, cable-like structure called the axon. The axon’s job is to transmit signals away from the soma to other neurons. At the end of the axon are several terminal buttons. These terminals don’t physically touch the next neuron but are separated by a microscopic gap called a synapse. The synapse is the junction where information is chemically transferred from one neuron to the next, completing the communication circuit.

Information Processing and Transmission

The journey of information through a neural network begins with the dendrites receiving chemical signals from the axons of other neurons. These signals, in the form of molecules called neurotransmitters, bind to receptors on the dendritic surface. This binding event opens or closes ion channels, altering the electrical voltage across the neuron’s membrane. A single neuron can receive thousands of these inputs at once, some of which are excitatory (encouraging it to fire) and others inhibitory (discouraging it from firing).

All of these incoming electrical signals are funneled toward the cell body, where they are integrated. The soma continuously sums up the total of these excitatory and inhibitory inputs. If the combined signal strength crosses a specific electrical threshold, the neuron initiates an action potential. This ensures the neuron only fires when there is a significant amount of input.

The action potential is a brief, all-or-nothing electrical impulse that travels down the length of the axon. Once the threshold is reached, a rapid exchange of ions across the axon’s membrane generates a consistent electrical spike that propagates from the cell body to the axon terminals without losing strength. This mechanism allows the signal to be transmitted reliably over what can be very long distances within the body.

When the action potential reaches the axon terminals, it triggers the next phase of communication at the synapse. The electrical impulse causes small vesicles, sacks containing neurotransmitters, to fuse with the terminal’s membrane and release their chemical contents into the synaptic cleft. These neurotransmitter molecules then travel across the tiny gap and bind to the dendrites of the adjacent neuron. This chemical transmission starts the process over again in the next cell.

Network Plasticity and Learning

A biological neural network is not a fixed or static structure; it continuously reorganizes itself in response to experience, a capability known as neuroplasticity. This adaptability is the foundation of learning and memory. The brain can form new connections between neurons, strengthen or weaken existing ones, and even eliminate connections that are no longer used.

The strength of a synapse, the connection point between two neurons, can change depending on how frequently it is used. This principle is often summarized by the phrase “neurons that fire together, wire together.” When two neurons are repeatedly activated at the same time, the connection between them can become stronger, a process known as Long-Term Potentiation (LTP). Conversely, if a synapse is used infrequently, its connection can weaken, a process called Long-Term Depression (LTD).

LTP is achieved through molecular changes that make the postsynaptic neuron more responsive to the presynaptic neuron’s signals. For example, with repeated stimulation, more receptors for neurotransmitters may be inserted into the postsynaptic membrane, or the presynaptic neuron may be modified to release more neurotransmitters with each signal. These changes can last for hours, days, or even longer, effectively creating a physical trace of a memory in the neural circuit.

The brain also refines its circuitry through a process called synaptic pruning. During development and throughout life, the nervous system eliminates unused or redundant synaptic connections. This process streamlines neural pathways, making the network more efficient by dedicating resources to the most robust and frequently used circuits.

Key Differences from Artificial Neural Networks

While artificial neural networks (ANNs) are inspired by their biological counterparts, they differ in several key ways:

• Biological neural networks (BNNs) employ a combination of analog chemical signals and digital electrical spikes for communication. In contrast, ANNs operate on simplified, purely numerical values, where the signal is just a number passed between nodes.
• BNNs learn continuously through neuroplasticity, strengthening or weakening connections based on activity, often without direct supervision. Many ANNs require a distinct training phase using large, labeled datasets and rely on algorithms like backpropagation to adjust their internal weights.
• The human brain operates on approximately 20 watts of power, an amount comparable to a dim light bulb. Running large-scale ANNs requires vast amounts of electrical power and sophisticated cooling systems housed in data centers.
• The brain features a complex, three-dimensional, and sparsely connected wiring system. In contrast, many ANNs are structured in rigid, distinct layers and are often fully connected, a simplification of the interconnectivity found in a biological brain.