www.neuron.yale.edu

Posted: **Wed Jun 21, 2017 7:55 am**

Hi,

I have 2 cells that share the same ECF. The currents through both the cells can change the ECF concentration of all the ions. I have enabled this sharing using pointers e.g. for ion X, the extracellular concentration [X]o can be changed by cell1 (del[X]o,1) and by cell2 (del[X]o,2) (The change in [X]o is the result of currents moving in/out of both the cells only). I am using these pointers and reading them in a density mechanism where total concentration change is calculated del[X]o = del[X]o,1 + del[X]o,2. This mechanism is inserted into one of the cells. The final [X]o is then also pointed to other cell which does not have the density mechanism. Similar pointers are used for other ions as well.

But, now if I have to increase the number of cells that share the same ECF, the number of pointers will increase and density mechanism for ECF change also becomes complex. So, the question is that whether this approach correct or there are other elegant ways to make such models?

Thanks,
Darshan

Posted: **Mon Jun 26, 2017 5:39 pm**

In a recent discussion, Robert McDougal said

With the current extracellular support: you can have as many segments as you want generating currents that affect and are affected by the same extracellular concentration. If it's all point cells in a single voxel of extracellular space, I suppose this could be quite simple. There's not even anything really to do besides adding a note that the species is dynamic in an extracellular region containing your cells, and that the region is a certain size and has a certain volume fraction.

Posted: **Mon Jun 26, 2017 10:16 pm**

Thank you, Ted.

I will try the model with RxD.

Posted: **Wed Jun 28, 2017 1:47 pm**

Hi,

We are extending the reaction-diffusion module (rxd) to support extracellular space, it’s not currently implement in the distributed version of NEURON, but the source code is available on github https://github.com/ramcdougal/nrn.git

Here is a simple example of two cells in the extracellular space defined by rxd.Extracellular, the volume fraction is the relative proportion of free space (typically 20%) and the tortuosity reduces extracellular diffusion due to obstacles (typically ~1.6) in brain tissue (See http://www.sciencedirect.com/science/ar ... 3698012612)
If your problem doesn’t require extracellular diffusion, you could make dx equal to the length of the grid, so it is all in one voxel.

The initial concentrations are taken from NEURON defaults (h.nai0_na_ion, h.nao0_na_ion, etc.) and the concentration can be accessed via the NEURON sections or the rxd Species objects.

Code: Select all

from neuron import rxd, h
import numpy
from matplotlib import pyplot
h.load_file('stdrun.hoc')

soma1 = h.Section(name='soma1')
soma2 = h.Section(name='soma2')

# Geometry 
soma1.nseg = 5
soma1.L = soma1.diam = 20


soma2.nseg = 5
soma2.L = soma2.diam = 20


# insert mechanisms into the cells
soma1.insert('hh')
soma2.insert('hh')

# stimulation 
stim1 = h.IClamp(soma1(0.5))
stim1.delay = 0 
stim1.dur = 50
stim1.amp = 0.015

stim2 = h.IClamp(soma2(0.5))
stim2.delay = 50 
stim2.dur = 50
stim2.amp = 0.03

# enable the extracellular space
rxd.options.enable.extracellular = True

# define the grid with the volume fraction (the proportion of free volume)
# and the tortuosity (the increase in path length due to obstacles)
extracellular = rxd.Extracellular(xlo=-25, ylo=-25, zlo=-100, xhi = 25, yhi = 25, zhi = 100, dx = 10, volume_fraction=0.2, tortuosity=1.6) 

# the intracellular region
cyt = rxd.Region(h.allsec(), nrn_region='i')

# the ion species
rxd_na = rxd.Species([extracellular,cyt], name = 'na', charge = 1, d = 1)
rxd_k = rxd.Species([extracellular, cyt], name = 'k', charge = 1, d = 1)

#record into vectors
#intracellular (i) and extracellular (o) concentrations
nai_vec1 = h.Vector()
nai_vec1.record(soma1(0.5)._ref_nai)
nao_vec1 = h.Vector()
nao_vec1.record(soma1(0.5)._ref_nao)
ki_vec1 = h.Vector()
ki_vec1.record(soma1(0.5)._ref_ki)
ko_vec1 = h.Vector()
ko_vec1.record(soma1(0.5)._ref_ko)

nai_vec2 = h.Vector()
nai_vec2.record(soma2(0.5)._ref_nai)
nao_vec2 = h.Vector()
nao_vec2.record(soma2(0.5)._ref_nao)
ki_vec2 = h.Vector()
ki_vec2.record(soma2(0.5)._ref_ki)
ko_vec2 = h.Vector()
ko_vec2.record(soma2(0.5)._ref_ko)

# membrane potential 
v_vec1 = h.Vector()
v_vec2 = h.Vector() 
v_vec1.record(soma1(0.5)._ref_v)
v_vec2.record(soma2(0.5)._ref_v)

t_vec = h.Vector() 
t_vec.record(h._ref_t)


h.finitialize()

# access the intracellular and extracellular species
na_ics = rxd_na[cyt]
k_ics = rxd_k[cyt]

na_ecs = rxd_na[extracellular]
k_ecs = rxd_k[extracellular]

print "Initial concentrations\t\tintracellular\t\textracellular"
print "Na+\t\t\t\t%g\t\t\t%g" % (numpy.mean(na_ics.nodes.concentration), na_ecs.states3d.mean())
print "K+\t\t\t\t%g\t\t\t%g" % (numpy.mean(k_ics.nodes.concentration), k_ecs.states3d.mean())

h.tstop=100
h.run()

print "Final concentrations\t\tintracellular\t\textracellular"
print "Na+\t\t\t\t%g\t\t\t%g" % (numpy.mean(na_ics.nodes.concentration), na_ecs.states3d.mean())
print "K+\t\t\t\t%g\t\t\t%g" % (numpy.mean(k_ics.nodes.concentration), k_ecs.states3d.mean())


pyplot.figure(figsize=(11,6))
pyplot.subplot(3,2,(1,2))
pyplot.plot(t_vec, v_vec1)
pyplot.plot(t_vec, v_vec2)
pyplot.xlabel('time (ms)')
pyplot.ylabel('mV')
pyplot.xlim(0,100)

pyplot.subplot(3,2,3)
pyplot.plot(t_vec, ki_vec1)
pyplot.plot(t_vec, ki_vec2)
pyplot.xlabel('time (ms)')
pyplot.ylabel('intracellular K+ mM')
pyplot.xlim(0,100)

pyplot.subplot(3,2,4)
pyplot.plot(t_vec, nai_vec1)
pyplot.plot(t_vec, nai_vec2)
pyplot.xlabel('time (ms)')
pyplot.ylabel('intracellular Na+ mM')
pyplot.xlim(0,100)

pyplot.subplot(3,2,5)
pyplot.plot(t_vec, ko_vec1)
pyplot.plot(t_vec, ko_vec2)
pyplot.xlabel('time (ms)')
pyplot.ylabel('extracellular K+ mM')
pyplot.xlim(0,100)

pyplot.subplot(3,2,6)
pyplot.plot(t_vec, nao_vec1)
pyplot.plot(t_vec, nao_vec2)
pyplot.xlabel('time (ms)')
pyplot.ylabel('extracellular Na+ mM')
pyplot.xlim(0,100)

pyplot.show()

https://slack-files.com/T0DM7EUDV-F5XJXSNGH-2aa6646349

Best wishes,

Adam.

Posted: **Wed Jun 28, 2017 3:31 pm**

Thanks Adam! I will test this out and if I have any doubts I will get back to you.

Regards,
Darshan

www.neuron.yale.edu

2 Cells Sharing and Changing Same ECF Concentrations

2 Cells Sharing and Changing Same ECF Concentrations

Re: 2 Cells Sharing and Changing Same ECF Concentrations

Re: 2 Cells Sharing and Changing Same ECF Concentrations

Re: 2 Cells Sharing and Changing Same ECF Concentrations

Re: 2 Cells Sharing and Changing Same ECF Concentrations