Furthermore, Hoc has turned out to be an orphan language limited to NEURON users. Though Hoc has served well, continuing development and maintenance of a general programming language steals significant time and effort from neurobiology domain-specific improvements. In the early 90's, Hoc syntax was again extended to provide some limited support for classes and objects, that is, data encapsulation and polymorphism, but not inheritance.

Sections are connected to form a tree shaped structure and their principle purpose is to allow the user to specify the physical properties of a neuron without regard for the purely numerical issue of how many compartments are used to represent each of the cable sections.

A fundamental extension to Hoc syntax was made in the late 80's in order to represent the notion of continuous cables, called sections. Hoc is not exactly an interpreted language since, analogous to Pascal, Java, or Python, Hoc statements are first dynamically compiled to an internal stack machine representation using a yacc parser and then the stack machine statements are executed. Hoc has a syntax for expressions and control flow vaguely similar to the C language. Since 1984, the NEURON simulation environment has used the Hoc interpreter (Kernighan and Pike, 1984) for setup and control of neural simulations. Instead of laborious piecemeal adoption of individual packages that requires intervention by a handful of experts, a better approach is to offer Python as an alternative interpreter so that a huge number of resources becomes available at the cost of only minimal interface code that most users can write for themselves. Few people, however, have the specialized expertise required to manually interface an existing software package and the creation of such interfaces is tedious. There are many more cases in which existing packages could potentially be employed by NEURON users. Some cases demand intimate knowledge of NEURON's internals and considerable effort examples include network parallelization with MPI, and adoption of Sundials for adaptive integration. The problem is one of interfacing an existing package with NEURON's interpreter. Experience shows that most of these needs have a software solution that has already been implemented elsewhere in the domain of scientific computing.

In large part this is because of its flexibility and the fact that it is continually being extended to meet the evolving research needs of its user community. The NEURON simulation environment has become widely used in the field of computational neuroscience, with more than 700 papers reporting work employing it as of April, 2008.

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