Artificial neural networks (ANNs) are computational models inspired by biological neural networks, but they are much simpler and operate in a more abstract manner. ANNs consist of layers of artificial neurons connected by weights, and they process input data through these connections to produce an output.
Biological neural networks, on the other hand, consist of neurons in the human or animal brain that communicate with each other via electrical and chemical signals. These networks are highly complex, involve much more interconnection, and use biological processes such as synaptic plasticity for learning.
While ANNs are simplified models designed for specific tasks like pattern recognition or prediction, biological neural networks are capable of a wide range of cognitive functions, including perception, decision-making, and motor control. ANNs attempt to mimic the general structure and function of biological systems, but they are still far less complex and versatile.