What is a neuronal cell?

A biological, natural neuron is a nerve cell that can process information. The human brain has a particularly large number of such neurons. These cells are specialised in the conduction and transmission of excitation. All neurons together form the nervous system. The nerve cell has a cell body and cell processes, the dendrites and neurites with the axon. Dendrites can receive excitations from other cells. Voltage changes can be achieved by short ionic currents, through special channels allowed in the cell membrane. Axon terminals are located above synapses, which communicate chemically by means of messengers. The human brain consists of nearly 90,000,000,000 nerve cells.

What is an artificial neuron?

Artificial neuronal cells, on the other hand, form the basis for a model of the artificial neural networks. This model of neuroinformatics, which is based on biological networks, enables intelligent behaviour. An artificial neuron can process multiple inputs and react in a targeted manner via its activation. Weighted inputs are passed to an output function. This calculates the neuron activation. The behaviour is generally given by learning using a learning procedure.

The modelling of artificial neuron networks began with Warren McCulloch and Walter Pitts in 1943. It could be shown that logical and arithmetic functions can be calculated with a simplified model of such neuron networks. In 1949, Hebb's learning rule was described by Donald Hebb. During learning, active connections between neurons are repeatedly strengthened. The generalisation of such a rule can also be used in today's learning procedures.

The 1958 convergence theorem about the perceptron was also important. Frank Rosenblatt proved that with a given learning procedure it is indeed possible to learn all solutions that can be represented with the model. In 1985, John Hopfield showed that Hopfield nets are capable of solving optimisation problems. Thus even the problem of the travelling salesman could be handled. A boost in research was given to the development of the Backpropagation process. Today, neural networks are used in many research areas.

What is the goal of artificial neural networks?

Based on the understanding of biological neurons, artificial neural networks have been modelled since the 1950s. Thus, input signals into the artificial neuron are linearly summed in the neural network and certain values are output by a corresponding activation function.

Even individual neurons consist of very complex structures. These fulfil different local input-output functions. The goal of research on Artificial intelligence is to replicate these natural neurons in the brain. In this way, the electrical processes are to be simulated, with the aim of learning the meaning of language and developing artificial neural networks for object recognition. The big vision is this, Mimic the functionality, ability and diversity of the brain in all respects.