extracellular recording

[ppt]

I have been going through the extracellular_stim_and_rec.zip files, and while I think I have a handle on how it works, I'm wondering what modifications would need to be made in order to incorporate a capacitive reactance. Obviously the transfer resistances will become transfer impedances, but I'm not really sure how to proceed in making the appropriate changes. Any pointers in the right direction?

[ted]

One approach would be to define the transfer impedances that couple segments to the recording electrodes, express these as DEs, and solve them numerically in the course of the simulation. A more efficient method would be to use a digital signal processing approach similar to that used by these authors for a different purpose:
Brette, R., Piwkowska, Z., Rudolph, M., Bal, T., and Destexhe, A. A non-parametric electrode model for intracellular recording. Neurocomputing 70:1597-1601, 2007.
Brette, R., Piwkowska, Z., Monier, C., Rudolph-Lilith, M., Fournier, J., Levy, M., Fregnac, Y., Bal, T., and Destexhe, A. High-resolution intracellular recordings using a real-time computational model of the electrode. Neuron 59:379-391, 2008.

You might also want to read this material and the papers that it cites: "Models of local field potentials" at http://cns.iaf.cnrs-gif.fr/outline_lfp.html

[Wolfgang]

I have a question about the same package.

I’m a student working to obtain a simulation of extracellular recordings from cortical layer V using silicon MEA probes.
So I’ve extended this package to have the possibility to create more than one electrode and one cell.
I have implemented a GUI to define the number of neurons and their spatial density; the neurons are then created in random positions. A GUI is also used to define the electrode arrays.

The problem is that in the original package (one cell/one electrode) the cell fires once because of the injection of a current pulse in the soma. In my implementation I need to obtain continuous recordings with more spikes at random times. So I’ve tried to inject Gaussian noise in each of my unconnected cells, and this actually allows me to get them fire at random times. The problem is that, whatever is the shape of the stimulus, the injected current induces extracellular potential: in my recordings I have also a signal proportional to the stimulus, that in my implementation completely covers the spikes.

So the question is: there is a way to avoid that the current stimulus is recorded by the extracellular electrode? If not, as I think, what do you think is the best approach to make my neurons fire randomly at different times, without going through the same trouble?

[ted]

there is a way to avoid that the current stimulus is recorded by the extracellular electrode?

Nope. You might try attaching an ExpSyn to each cell, and driving each ExpSyn with a spike train generated by a different NetStim with its random parameter set to 1. Or you could use the ChannelBuilder to create a point processor class that implements the HH sodium channel with stochastic gating (use a small number of channels and a large channel conductance, and you'll get very nice excitatory noise), and attach one of these to each of your cells near its spike trigger zone.

The synaptic or sodium channel current seems just as likely to contribute to the extracellular field as a current that is injected by a patch electrode, but there's only one way to be sure--try it and see what happens.

[Wolfgang]

there is a way to avoid that the current stimulus is recorded by the extracellular electrode?

Nope. You might try attaching an ExpSyn to each cell, and driving each ExpSyn with a spike train generated by a different NetStim with its random parameter set to 1.

Thanks for your help, this works fine for my purposes.

[ted]

Excellent! I wasn't sure it would give you a sufficiently high signal/artifact ratio; the only way to tell was by trying.

Please be sure to let me know when you publish anything that reports work done with NEURON, so we can cite it on the Bibliography page http://www.neuron.yale.edu/neuron/bib/usednrn.html