Neural network, as its name indicates, is a model inspired by a neural structure of human—especially visual and auditory cortex. The underlying mechanism of the neural network is similar with that of human cognition, as it creates several layers, puts cells into them, and connects these cells with each other; each cell receives a signal and transmits it to the next neuron. In depth, however, there are a number of characteristics that make an artificial neural network and a biological brain disparate. A human cerebral consists of more than a hundred billion cells, and of course, current technology cannot simulate this number of neurons. Although the model of an artificial neural network itself began by simulating the structure of a biological brain, there are differences in terms of their structures other than the difference between the numbers of neurons of each system.
A biological cell has an all-or-none characteristic; stimulated under its threshold, it will not show any response at all and will react only if the stimulation is above its threshold. Stronger stimulation does not increase the size of its response, but rather increases the frequency of its response, which in fact can be considered somewhat similar to step function, or so-called Dirac Delta function. One problem is that, since it is discrete and thus non-differentiable at x = 0, a model based on the unit step function cannot intelligize itself through deep learning. That is why engineers in the past used other step functions such as sigmoid function and hyperbolic tangent function, which are smooth and differentiable. The sigmoid function is, arguably, similar with a step function; and yet, it is very different from a biological brain, which cannot function without the passage of time. In the case of artificial neural networks, the output value is set regardless of the passage of time, given a certain input value.
The question of whether artificial neural network is similar with human nerve system, in my opinion, should not be stuck to one-to-one comparison between them. The reason why scientists developed the neural network is to do tasks that human brain finds difficult to process. From the evolutionary point of view, human brain has evolved for the purpose of survival and preservation of the species; not for memorizing a hundred pages of text in a glance or performing highly complicated math operations. Artificial intelligence—optimized for these quantitative tasks—will lead us to overcome human limitations and present a new angle on the issues we are dealing with, even to the realm of art.