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Our understanding of the neuronal mechanisms behind epilepsy dynamics has recently advanced due to the application of novel technologies, monitoring hundreds of neurons with single cell resolution. These developments have provided new theories on the relationship between physiological and pathological states, as well as common motifs for the propagation of paroxysmal activity. Although traditional electroencephalogram (EEG) recordings continue to describe normal network oscillations and abnormal epileptic events within and outside of the seizure focus, analysis of epilepsy dynamics at the microscale has found variability in the composition of macroscopically repetitive epileptiform events. These novel results point to heterogeneity in the underlying dynamics of the disorder, highlighting both the need and potential for more specific and targeted therapies.

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High-energy protons constitute at least 85% of the fluence of energetic ions in interplanetary space. Although protons are only sparsely ionizing compared to higher atomic mass ions, they nevertheless significantly contribute to the delivered dose received by astronauts that can potentially affect central nervous system function at high fluence, especially during prolonged deep space missions such as to Mars. Here we report on the long-term effects of 1 Gy proton irradiation on electrophysiological properties of CA1 pyramidal neurons in the mouse hippocampus. The hippocampus is a key structure for the formation of long-term episodic memory, for spatial orientation and for information processing in a number of other cognitive tasks. CA1 pyramidal neurons form the last and critical relay point in the trisynaptic circuit of the hippocampal principal neurons through which information is processed before being transferred to other brain areas. Proper functioning of CA1 pyramidal neurons is crucial for hippocampus-dependent tasks. Using the patch-clamp technique to evaluate chronic effects of 1 Gy proton irradiation on CA1 pyramidal neurons, we found that the intrinsic membrane properties of CA1 pyramidal neurons were chronically altered at 3 months postirradiation, resulting in a hyperpolarization of the resting membrane potential (VRMP) and a decrease in input resistance (Rin). These small but significant alterations in intrinsic properties decreased the excitability of CA1 pyramidal neurons, and had a dramatic impact on network function in a computational model of the CA1 microcircuit. We also found that proton-radiation exposure upregulated the persistent Na(+) current (INaP) and increased the rate of miniature excitatory postsynaptic currents (mEPSCs). Both the INaP and the heightened rate of mEPSCs contribute to neuronal depolarization and excitation, and at least in part, could compensate for the reduced excitability resulting from the radiation effects on the VRMP and the Rin. These results show long-term alterations in the intrinsic properties of CA1 pyramidal cells after realistic, low-dose proton irradiation.

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Dendritic morphology has been shown to have a dramatic impact on neuronal function. However, population features such as the inherent variability in dendritic morphology between cells belonging to the same neuronal type are often overlooked when studying computation in neural networks. While detailed models for morphology and electrophysiology exist for many types of single neurons, the role of detailed single cell morphology in the population has not been studied quantitatively or computationally. Here we use the structural context of the neural tissue in which dendritic trees exist to drive their generation in silico. We synthesize the entire population of dentate gyrus granule cells, the most numerous cell type in the hippocampus, by growing their dendritic trees within their characteristic dendritic fields bounded by the realistic structural context of (1) the granule cell layer that contains all somata and (2) the molecular layer that contains the dendritic forest. This process enables branching statistics to be linked to larger scale neuroanatomical features. We find large differences in dendritic total length and individual path length measures as a function of location in the dentate gyrus and of somatic depth in the granule cell layer. We also predict the number of unique granule cell dendrites invading a given volume in the molecular layer. This work enables the complete population-level study of morphological properties and provides a framework to develop complex and realistic neural network models.

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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.