Abstract
Recent advances in parallel computing, including the creation of the parallel version of the NEURON simulation environment, have allowed for a previously unattainable level of complexity and detail in neural network models. Previously, we published a functional NEURON model of the rat dentate gyrus with over 50,000 biophysically realistic, multicompartmental neurons, but network simulations could only utilize a single processor. By converting the model to take advantage of parallel NEURON, we are now able to utilize greater computational resources and are able to simulate the full-scale dentate gyrus, containing over a million neurons. This has eliminated the previous necessity for scaling adjustments and allowed for a more direct comparison to experimental techniques and results. The translation to parallel computing has provided a superlinear speedup of computation time and dramatically increased the overall computer memory available to the model. The incorporation of additional computational resources has allowed for more detail and elements to be included in the model, bringing the model closer to a more complete and accurate representation of the biological dentate gyrus. As an example of a major step toward an increasingly accurate representation of the biological dentate gyrus, we discuss the incorporation of realistic granule cell dendrites into the model. Our previous model contained simplified, two-dimensional dendritic morphologies that were identical for neurons of the same class. Using the software tools L-Neuron and L-Measure, we are able to introduce cell-to-cell variability by generating detailed, three-dimensional granule cell morphologies that are based on biological reconstructions. Through these and other improvements, we aim to construct a more complete full-scale model of the rat dentate gyrus, to provide a better tool to delineate the functional role of cell types within the dentate gyrus and their pathological changes observed in epilepsy.
Highlights
The dentate gyrus network model was developed to study the role of the circuit alterations present in the epileptic dentate gyrus (Santhakumar et al, 2005; Dyhrfjeld-Johnsen et al, 2007) and has been used to make predictions for the existence of nonrandom microcircuits in epilepsy (Morgan and Soltesz, 2008)
PARALLELIZATION OF THE 1:20 SCALE MODEL Previous implementations of the rat dentate gyrus network model were scaled down from the biological dentate gyrus because they were limited to the use of a single processor
Increasing the number of processors for the 1:2000 scale model has been shown to produce a superlinear speedup of the overall runtime when tested on several different parallel computing systems (Migliore et al, 2006)
Summary
The dentate gyrus network model was developed to study the role of the circuit alterations present in the epileptic dentate gyrus (Santhakumar et al, 2005; Dyhrfjeld-Johnsen et al, 2007) and has been used to make predictions for the existence of nonrandom microcircuits in epilepsy (Morgan and Soltesz, 2008). The creation of parallel NEURON and the speedup obtained during tests on the 1:2000 scale model (Migliore et al, 2006) have provided a means to make the recent model more accessible to researchers, but to remove the limitations on the size and complexity of the computational model. Through the cooperation of two software tools, the complexity of the model can be increased with the generation of variable and realistic dendritic morphology for the sample case of granule cells. These model improvements reflect a significant advancement toward a realistic full-scale computational model
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