NeuronC: a computational language for investigating functional architecture of neural circuits

Abstract

A computational language was developed to simulate neural circuits. A model of a neural circuit with up to 50,000 compartments is constructed from predefined parts of neurons, called “neural elements”. A 2-dimensional (2-D) light stimulus and a photoreceptor model allow simulating a visual physiology experiment. Circuit function is computed by integrating difference equations according to standard methods. Large-scale structure in the neural circuit, such as whole neurons, their synaptic connections, and arrays of neurons, are constructed with procedural rules. The language was evaluated with a simulation of the receptive field of a single cone in cat retina, which required a model of cone-horizontal cell network on the order of 1000 neurons. The model was calibrated by adjusting biophysical parameters to match known physiological data. Eliminating specific synaptic connections from the circuit suggested the influence of individual neuron types on the receptive field of a single cone. An advantage of using neural elements in such a model is to simplify the description of a neuron's structure. An advantage of using procedural rules to define connections between neurons is to simplify the network definition.