Connectionism is the name for the computer modeling approach to information processing based on the design or architecture of the brain

Connectionism is the name for the computer modeling approach to information processing based on the design or architecture of the brain. Not the architecture of the whole brain. Rather, because neurons are the basic information processing structures in the brain, and every sort of information the brain processes occurs in networks of interconnected neurons, connectionist computer models are based on how computation occurs in neural networks. Basically, connectionism can be thought of as the brain model of cognition.Connectionism are also sometimes referred to as ‘neural networks’ or ‘artificial neural networks’. The central connectionist principle is that mental phenomena can be described by interconnected networks of simple and often uniform units. The form of the connections and the units can vary from model to model. For example, units in the network could represent neurons and the connections could represent synapses, as in the human brain. Often, these come in the form of highly interconnected, neuron-like processing units. There is no sharp dividing line between connectionism and computational neuroscience, but connectionists tend more often to abstract away from the specific details of neural functioning to focus on high-level cognitive processes.
Connectionism was based on association, mostly claiming that elements or ideas become associated with one another through experience and that complex ideas can be explained through a set of simple rules. But connectionism further expanded these assumptions and introduced ideas like distributed representation and supervised learning and should not be confused with associationism. Further, it gave rise to Connectionist Models and Artificial Neural Network. Connectionist models, also known as Parallel Distributed Processing (PDP) models, are a class of computational models often used to model aspects of human perception, cognition, and behaviour, the learning processes underlying such behaviour, and the storage and retrieval of information from memory.The approach embodies a particular perspective in cognitive science, one that is based on the idea that our understanding of behaviour and of mental states should be informed and constrained by our knowledge of the neural processes that underpin cognition. Connectionist models take inspiration from the manner in which information processing occurs in the brain. Processing involves the propagation of activation among simple units (artificial neurons) organized in networks, that is, linked to each other through weighted connections representing synapses or groups thereof. Each unit then transmits its activation level to other units in the network by means of its connections to those units. The activation function, that is, the function that describes how each unit computes its activation based on its inputs, may be a simple linear function.
Whereas Artificial neural networks is a computational model based on the structure and functions of biological neural networks. Information that flows through the network affects the structure of the ANN because a neural network changes – or learns, in a sense – based on that input and output. ANNs are considered nonlinear statistical data modeling tools where the complex relationships between inputs and outputs are modeled or patterns are found. They are the foundations of AI and solve problems that would prove impossible or difficult by human or statistical standards. ANN have self-learning capabilities that enable them to produce better results as more data become available. Artificial neural networks are built like the human brain, with neuron node interconnected like a web. The human brain has hundreds of billions of cells called neurons. Each neuron is made up of a cell body that is responsible for processing information by carrying information towards inputs and away outputs from the brain.
Every aspect of these three parameters are connected to each other and always be compared with the human brains. As we talked about connectionism and the concepts of it gave rise to the connectionist model with almost the same concept whereas artificial neural network have different properties to deal with.