A biological neural network (BNN) is the complex, interconnected system of nerve cells found in all living organisms. These networks are the fundamental architecture for sensing the environment, controlling movement, and generating thought. Composed of billions of specialized cells, BNNs communicate through both electrical and chemical signals. This intricate wiring allows the brain and nervous system to process information with speed and efficiency, managing everything from basic reflexes to complex problem-solving and learning.
The Cellular Components of Biological Networks
The core processing unit of the biological neural network is the neuron, or nerve cell, specialized for transmitting signals. Each neuron consists of a cell body, the soma, which houses the nucleus. Extending from the soma are the dendrites, which act as the primary antennae for receiving incoming signals from other cells.
The neuron’s output structure is the axon, a single projection that transmits signals to downstream cells. Specialized axon terminals at the end of the axon release the signal to another neuron. Communication occurs at the synapse, a specialized junction where the axon terminal meets the dendrite or cell body of the receiving cell.
While neurons are the main information carriers, glial cells also play a supporting role. These non-neuronal cells provide insulation for the axons, maintain the necessary chemical environment, and contribute to the overall health and function of the neural tissue. The entire network relies on the structural integrity and precise positioning of all these components to ensure accurate communication.
Signal Transmission and Information Processing
Information movement begins with an electrical signal generated within the neuron called an action potential. This all-or-nothing electrochemical pulse travels rapidly down the axon once the neuron’s internal voltage reaches a specific threshold. The action potential is a temporary shift in the membrane potential caused by the flow of charged particles, such as sodium and potassium ions, across the cell membrane.
When this electrical impulse reaches the axon terminal, signal transmission switches from electrical to chemical. The action potential triggers the release of chemical messengers, neurotransmitters, into the synaptic cleft—the small gap separating the two cells. These neurotransmitters diffuse across the cleft and bind to receptor sites on the receiving, or postsynaptic, neuron.
The binding of neurotransmitters determines the nature of the signal: it can be either excitatory or inhibitory. Excitatory signals encourage the receiving cell to fire an action potential, while inhibitory signals reduce the likelihood of the cell firing. Information processing occurs as the neuron integrates the incoming excitatory and inhibitory signals received across its dendritic branches. Only if the sum of these incoming signals is strong enough to depolarize the cell body above its firing threshold will the neuron generate a new action potential and pass the information along.
Adaptability and the Changing Network
A defining characteristic of biological neural networks is their ability to change structure and function over time, known as neural plasticity. This capacity allows the network to adapt to new experiences, form memories, and acquire new skills. The most profound changes occur at the synapse, modifying the strength of the connection between two neurons.
One mechanism for strengthening connections is long-term potentiation (LTP), where rapid and repeated activation of a synapse leads to a long-lasting increase in signal transmission efficacy. This strengthening involves changes in the amount of neurotransmitter released by the presynaptic cell or an increase in the number of receptors on the postsynaptic cell. LTP is considered a primary cellular mechanism for memory storage and learning.
Conversely, connections that are rarely used can become weaker or eliminated through long-term depression or synaptic pruning. This dynamic modification ensures the network remains efficient by strengthening useful pathways and removing unnecessary ones. The continuous sculpting of these connections based on activity patterns allows the network to store information and reorganize itself.