www.neuron.yale.edu

Okay, I believe I found it here: https://github.com/neurodroid/neurodroid/blob/master/nrn/squid.hoc Here is the code: /* Squid giant axon ap */ load_file("stdrun.hoc") /* Print v at every time step */ proc advance() { fadvance() print "ND ", v } create axon access axon insert hh ...

I am learning to write my own MOD files, and I'm starting with hh.mod as a learning tool. The comment section in hh.mod says to see squid.hoc as an example of the hh mechanism's implementation...but I cannot find squid.hoc anywhere. It's not in my installation of NEURON, and I can't find it anywhere...

Fair enough, thanks. I just wanted a sanity check to make sure I wasn't missing something.

Good question. I should have mentioned in my original post that yes, I have checked the source code you referenced (written in C++), and it incorporates this exact trap function. It's so bizarre that it makes the function discontinuous. I'll try emailing the authors one more time...

I am trying to code the Bazhenov 2002 sleep model in NEURON (ModelDB link: https://senselab.med.yale.edu/ModelDB/showmodel.cshtml?model=28189 ), and I have run across a very strange "trap" function for the fast sodium current in the cortical pyramidal cells. The details of this current are...

Good to know, thanks, Ted. I will use the same specific capacitance for the soma as for the dendrite, then compare to Bazhenov et al's results.

I am interested in coding Bazhenov's sleep model ("Sleep-wake transitions in corticothalamic system," 2002) in NEURON. (ModelDB link: https://senselab.med.yale.edu/ModelDB/showmodel.cshtml?model=28189#tabs-2 ) In getting started, however, I immediately stumbled upon a curious feature of th...

Okay, this post https://www.neuron.yale.edu/phpBB/viewtopic.php?f=2&t=3213&p=13373&hilit=subworlds#p13373 helped me figure out the solution: I need to include a destructor in my "Net" class definition. I just added def __del__(self): pc.gid_clear() to my Net class (which contai...

I have written some fairly complex distributed network code in which different groups of cells are hosted on different processors (i.e. NOT bulletin board style parallelization). My current simulations are not large enough to require parallel code (I currently run them with just one processor), but ...

All right, finally figured out the solution. Much thanks to Ted and Robert for their help with this. For some reason beforestep_py.mod messes with i_membrane_'s location in memory, and this problem can be avoided by instead using cvode's extra_scatter_gather method to call the function which compute...

Hmmm...I solved the reproducibility problem by commenting out from neuron import gui and replacing it with h('load_file("nrngui.hoc")') . Apparently these two statements are not identical? In any case, I still have the flat-line problem. Here is the result I consistently get: http://i.imgu...

Thanks, Ted. Good point on using modlunit. When I did so, I found that I needed to use 'nanoamps' instead of 'nA'. Also, I needed to change er = rx * im to er = (1000) * rx * im , which makes sense since er is in microvolts, rx is in megohms, and im is in nanoamps. (Although, weirdly, modlunit conti...

All right, I figured out what was causing the segmentation fault in h.finitialize: I wasn't linking the ex pointer within xtra.mod. This post was helpful: https://www.neuron.yale.edu/phpBB/viewtopic.php?f=16&t=3403&p=14429&hilit=segmentation+finitialize#p14429 xtra.mod defines two pointe...

Model setup is laying the groundwork for this to happen, because print statements show the program gets to h.run and then chokes with Segmentation fault (core dumped) . I'm having a hard time debugging from here, because usually when NEURON chokes within h.run, it gives an error message that indicat...

Hmmm...I see what you're getting at now. Upon further investigation, the simulation was not actually choking within setelec, but rather somewhere within h.run(). The error message associated with this is simply "Segmentation fault (core dumped)". So yes, it would seem I need to somehow deb...