hoc (programming language)

hoc is an interpreted, dynamically-typed programming language primarily associated with the NEURON simulation environment developed by Michael Hines at Yale University and subsequently at the NIH. It serves as the primary interface for defining and controlling simulations within NEURON, allowing users to specify neuronal morphology, biophysical properties, network connectivity, and simulation protocols.

The language is object-oriented, incorporating features such as classes, inheritance, and polymorphism. It allows users to create and manipulate objects representing biological entities like neurons, sections, and synaptic connections. It's not a general-purpose language and is heavily tailored towards the specific needs of computational neuroscience.

Key features of hoc include:

• Simulation Control: Provides commands and structures for initiating, controlling, and analyzing simulations. This includes functions for specifying time steps, simulation duration, and recording data.

• Morphology Definition: Allows users to define the three-dimensional structure of neurons using segments, sections, and points. Syntax supports specification of segment lengths, diameters, and spatial locations.

• Biophysical Modeling: Facilitates the definition and implementation of biophysical properties such as membrane conductances, ion channel kinetics, and synaptic mechanisms.

• Network Specification: Enables the creation of neuronal networks through the specification of connections between neurons, including synapse types, locations, and connection probabilities.

• Data Analysis and Visualization: Provides tools for analyzing and visualizing simulation results, including plotting membrane potential, current, and other variables. Data can be exported to other analysis software.

• Interpreter Environment: Hoc code is interpreted, meaning that commands are executed directly without a separate compilation step. The NEURON environment provides an interactive interpreter for running hoc code and examining simulation results.

Hoc is tightly integrated with the NEURON environment, and its use is largely confined to computational neuroscience research. Its syntax is somewhat idiosyncratic and distinct from more widely used programming languages like Python or C++. Users familiar with NEURON and computational neuroscience generally find hoc to be a powerful and flexible tool for building and simulating complex neuronal models.