Dendritic Signal Processing Simulated Using NEURON

In my blog post “NEURON, SenseLab ModelDB, NeuroMorpho.org, and Signal Processing in Brain Microcircuitry” the computational neuroscience paper under review “Local Control of Postinhibitory Rebound Spiking in CA1 Pyramidal Neuron Dendrites” (published May 5, 2010 in the Journal of Neuroscience) referenced two NEURON models in the ModelDB online data repository. We looked at the model directly associated with the paper in the blog post “A Taste of Neuroscience Papers in the Future.” In this post we’ll look at the foundational model of hippocampus CA1 pyramidal cells (and associated paper) that they referenced.

Figure 1. Model traces for neuron 5038804 in NEURON using the “CA1 pyramidal neuron: signal propagation in oblique dendrites (Migliore et al 2005)” model downloaded from ModelDB. The traces are the same as in Figure 2a of the paper being reviewed. Please see text below for more details.

The paper “Signal Propagation in Oblique Dendrites of CA1 Pyramidal Cells” (published December 2005 in the Journal of Neurophysiology) reports on investigations into how and to what extent the shape of dendrites and the distribution of A-type potassium channels (KA) across the dendrites might regulate the backward and forward propagation of action potentials. They focused on the dendritic branches beyond the primary trunks attached to the cell body (soma). Current limits on experimental techniques makes it impossible to directly test a living cell so the research team used realistic computational models of a CA1 pyramidal cells using NEURON.

Note: The NEURON model associated with the paper being discussed is “CA1 pyramidal neuron: signal propagation in oblique dendrites (Migliore et al 2005)” and may be downloaded from the SenseLab ModelDB repository.

They used 27 three-dimensional (3D) digital reconstructions of rat CA1 pyramidal cells from four different laboratories, which are publicly available from NeuroMorpho.org. The reference (URL) to these data in the paper and in the ModelDB record is out of date. I wondered how I’d figure out which neurons were used in this study until I finally noticed that the cell identifier numbers used by NeuroMorpho.org were listed in figure 1 under the bars representing input resistance values. However, only 26 neurons were listed there. However, as I began looking at the records on NeuroMorpho.org I noticed the following. The listing 9068802 does not exist in the repository but 9068802a and 9068802b do exist (which would account for missing neuron number 27). This raises the question of the input resistance values for these two neurons. Neuron 8228804 is not in repository but 8228804a does exist. Kr1 and Kr2 do not exist but there is an NM1 and an NM2. I couldn’t find c8076 but there is a c8076e.

Note: Those interested may find the complete list of reconstructed neurons used in this study in the table at the end of this post.

This points out a huge issue that is super important for the Semantic Web. Things on the Semantic Web, like neurons, each have their unique identifier that is technically a Uniform Resource Identifier (URI) but often in practice is a Uniform Resource Locator (URL), which is a kind of URI. URIs and URLs used to identify things must remain valid across time to be useful. A published account of neuron n125 is compromised if its identifier http://neuromorpho.org/neuroMorpho/neuron_info.jsp?neuron_name=n125 (the way NeuroMorpho.org identifies their cells) is no longer valid. How do you find it? Ways to solve the problem have been proposed and even put into practice but the Semantic Web community hasn’t come to a consensus yet.

The study demonstrated that both back and forward propagation of action potentials in the dendrites can be effectively and independently modulated in individual dendritic branches of the same neuron. They found that the distribution of A-type potassium channels (KA) appears to be pivotal in gating back propagation whereas local morphology plays the lead role in regulating forward propagation. They found that spikes back propagating from the soma continuously decreased in size in the apical trunk but tended only to invade branches off the trunk in an all-or-none fashion. They also found that isolated spiking activity in a dendritic branch usually had negligible effects on the rest of the neuron.

You can play with these anatomical reconstructions and physiological simulations due to the remarkable tools and data freely available to anyone with access to the Internet. For instance, reproduce the paper’s figure 2a by running the “CA1 pyramidal neuron: signal propagation in oblique dendrites (Migliore et al 2005)” model downloaded from ModelDB in NEURON.

Note: Those with more than a passing interested in using NEURON may find the book or e-book by its creators useful “The NEURON Book.”

FIrst, if you don’t already have it, download NEURON from the NEURON Download and install web page.

Note: NEURON runs under most computing environments. Details on setup and trouble shooting vary by platform but are well documented at the NEURON website.

Next download the “CA1 pyramidal neuron: signal propagation in oblique dendrites (Migliore et al 2005)” model from ModelDB.

Note: Don’t forget to compile the files in the project folder. On the Macintosh computer you drag the project folder to NEURON’s mknrndll program icon.

Load the model’s fig2A.hoc file.

The NEURON application should be running and displaying some windows. One window should contain an image of the hippocampus CA1 pyramidal neuron that you’ll use in the simulation (neuron 5038804). Another window titled “Neuron” shows two empty graphs and a button. The top graph will show a trace of the somatic membrane potential. The bottom graph will show the distribution of the average peak depolarization of each dendritic branch in the neuron.

Click on the “runm” button to run the model.

The result should look like Figure 1 above and the paper’s figure 2a.

Don’t forget to play around with these models. You have first rate scientific tools at your disposal to investigate one of the biggest questions we may ask about ourselves. How does our brain work?

Note: Would you like me to take you deeper into NEURON? Please let me know!

The following table identifies all 27 neurons used in the study described in the paper under review. I’ve done the best I can to identify the cells (see text above) but there could be errors. Each neuron is identified by its NeuroMorpho.org number and is linked to its record in the repository.

5038801	9138801	c73162	cd2351
9068802a	NM1	cd0351	c80761
9068802b	NM2	c91665	c70963
6028801	n400	c62564	c30465
5038804	c9236e	c8076e	c72965
n125	c91662	c81462	c70863
8228804a	cd1152	c20466