Figure 1. Panels A, B, and C from Figure 4 in the paper “Linking Macroscopic with Microscopic Neuroanatomy using Synthetic Neuronal Populations” published October 23, 2014 in PLoS Computational Biology). (A) Visualization of the dentate gyrus model highlighting 1,000 synthetic dendritic trees (dark purple structure scattered through model). (B) Rendering of the complete morphologies for all granule cells in a 20 µm transverse slice from the center of the model dentate gyrus. (C) Rendering of 48 granule cells from the crest of the slice in (B).

Most signal integration and transformations happening in your brain are happening in the neuropil. The neuropil is the tangle of extremely small processes connecting nerve cells together. Axons from nerve cell bodies touch dendrites of receiving neurons through an intervening tiny gap called the synapse through which chemicals from the transmitting cell address the receiving cell.

It’s been analytically surmised for many decades now that the shape, branching, and other properties of neuron processes influence signal processing and, although technically difficult, a number of experiments have shown this to be true. These experiments are performed on just one or a few neurons and show that, for example, a branch in an axon may cause a signal (an action potential) to slow down, or even stop, at the branch. The signal may continue down one branch but may cease to exist the other branch (this is called filtering).

If morphology has such large affects on signal processing in a single cell, what would be the effects of hundreds of thousands or millions of cells with diverse morphologies on the signal processing in a particular organ of the brain? The research team behind the article “Linking Macroscopic with Microscopic Neuroanatomy using Synthetic Neuronal Populations” (published October 23, 2014 in PLoS Computational Biology) doesn’t address this question directly but carried out work that lays a foundation for helping to answer it.

In this paper, the authors set out to build an anatomically and morphologically realistic model of a well studied organ of the brain known as the dentate gyrus located in the hippocampus. This area of our brains is particularly interesting for its central roll in forming memories and helping us to navigate through our environment. To study details of signal processing in the dentate gyrus we must take into account the significant variability in neuron morphology across this structure. Currently we can only hope to study details in signal processing across hundreds of thousands or millions of cells using simulation tools. Therefore, this team set out to construct an anatomically and morphologically realistic model of the dentate gyrus.

Note: The authors have posted the Matlab source code used to create this synthetic dentate gyrus at “Generation of granule cell dendritic morphology (Schneider et al. 2014)” record in the SenseLab ModelDB repository at . The paper doesn’t state the level of computer power necessary to generate the model but the research team did use a high performance computer cluster so it’s probably safe to say “a lot.” Please keep this in mind if you decide to download the code and play.

Rip a donut in half and squish that half circle of a donut on a countertop so that the arch almost completely collapses and each end is splayed out a bit. Now you have something resembling a rat’s dentate gyrus. A rat’s dentate gyrus is estimated to contain about 1.2 million granule cells (see Figure 1A above). Using mathematical and computational methods that capture both its overall anatomical shape (the squished half donut) and the morphology of each of over 1 million cells, the research team generated 1.19 million granular cells packed at the appropriate density into a three-dimensionally appropriate structure. They used regional statistical variation to capture experimentally observed variability in neuron processes across the dentate gyrus. The result is an impressive anatomical model that can be used to study the effect of anatomical and morphological heterogeneity on signal processing in the dentate gyrus.