Overview#

This is a short introduction to sinaps for new users.

The primary data structures of sinaps are:

  • Section wich aims to represent a segment of a neuron with uniform physicals values

  • Neuron wich aims to represent a complete neuron is a directed graph whose edges are of Section type.

  • Channels wich aim to represent ionic channels with various dynamics. Several channels are already implemented, such as Hodgkin-Huxley channels, Pulse currents or AMPA receptors.

The outputs of a simulation are:

  • Electric potential, at each position in the neuron, for each time point

  • Electric currents computed from channels defined in the neuron, each postion, each time point

  • Concentration of species, at each position in the neuron, for each time point

The package can be imported as follows:

[1]:

import sinaps as sn

Object creation#

Creating an empty Neuron:

[2]:

nrn = sn.Neuron()

Creating a Section, letting sinaps setting default attribute:

[3]:

sec = sn.Section()

Adding HodgkinHuxley channels (sodium, potassium and leak) to the newly created section:

[4]:

sec.add_channel(sn.channels.Hodgkin_Huxley())

Adding a Hodgkin-Huxley type calcium channel:

[5]:

sec.add_channel(sn.channels.Hodgkin_Huxley_Ca())

Adding a PulseCurrent channel with a current of 200 pA, between t =2 and t=5 ms, at the beginning of the section (position 0):

[6]:

sec.add_channel(sn.channels.PulseCurrent(200,2,5),0)

Adding the section s to the neuron nrn as an edge between nodes 0 and 1 :

[7]:

nrn.add_section(sec,0,1)

Adding two new sections * sec2 with a radius of 2 μm * sec3 with a length of 200 μm

[8]:

sec2 = sn.Section(a = 2)
sec3 = sn.Section(L = 200)
nrn.add_sections_from_dict({
    sec2:(1,2),
    sec3:(1,3)
})

Plotting the neuron structure:

[9]:

nrn.plot()

Calculating layout...[OK]

[9]:

Adding calcium ions in the model:

[10]:

nrn.add_species(sn.Species.Ca,C0=2E-4, D=100) # For the sake of the example, we increase the calcium diffusion coefficient to speed-up its dynamics, in order to observe variations within 50 ms.

Running simulation#

Creating a Simulation of neuron nrn with spatial resolution 10 μm:

[11]:

sim = sn.Simulation(nrn,dx=10)

Running the simulation for timespan 0 to 50 ms.

[12]:

sim.run((0,50))

100%|██████████| 50.0/50 [00:02<00:00, 20.39ms/s]

Results of the simulation are stored as pandas Dataframe:

[13]:

sim.V

[13]:
Section Section0000 ... Section0002
Position (μm) 5.0 15.0 25.0 35.0 45.0 55.0 65.0 75.0 85.0 95.0 ... 105.0 115.0 125.0 135.0 145.0 155.0 165.0 175.0 185.0 195.0
Time
0.000000 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 ... 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
0.000009 -2.666092e-07 -2.666092e-07 -2.666092e-07 -2.666092e-07 -2.666092e-07 -2.666092e-07 -2.666092e-07 -2.666092e-07 -2.666066e-07 -2.655348e-07 ... -1.518839e-36 -3.683640e-39 -8.933931e-42 -2.166746e-44 -5.255010e-47 -1.274497e-49 -3.091039e-52 -7.496696e-55 -1.818174e-57 -4.420311e-60
0.000017 -5.332102e-07 -5.332102e-07 -5.332102e-07 -5.332102e-07 -5.332102e-07 -5.332102e-07 -5.332102e-07 -5.332102e-07 -5.331990e-07 -5.298292e-07 ... -2.226100e-35 -5.822772e-38 -1.514984e-40 -3.923580e-43 -1.012046e-45 -2.601149e-48 -6.664197e-51 -1.702519e-53 -4.338305e-56 -1.105701e-58
0.000104 -3.198502e-06 -3.198502e-06 -3.198502e-06 -3.198502e-06 -3.198502e-06 -3.198502e-06 -3.198500e-06 -3.198433e-06 -3.195528e-06 -3.071596e-06 ... -1.223602e-24 -2.844991e-26 -6.614874e-28 -1.538021e-29 -3.576043e-31 -8.314639e-33 -1.933232e-34 -4.494947e-36 -1.045131e-37 -2.486497e-39
0.000191 -5.862975e-06 -5.862975e-06 -5.862975e-06 -5.862975e-06 -5.862975e-06 -5.862975e-06 -5.862966e-06 -5.862630e-06 -5.851409e-06 -5.506585e-06 ... -1.694736e-23 -4.254388e-25 -1.062186e-26 -2.639415e-28 -6.531533e-30 -1.610402e-31 -3.957683e-33 -9.698040e-35 -2.370257e-36 -5.919767e-38
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
44.995915 1.457756e-01 1.462924e-01 1.473259e-01 1.488769e-01 1.509466e-01 1.535368e-01 1.566495e-01 1.602876e-01 1.644538e-01 1.691516e-01 ... 1.923908e-01 1.935948e-01 1.946656e-01 1.956030e-01 1.964068e-01 1.970769e-01 1.976131e-01 1.980154e-01 1.982836e-01 1.984177e-01
46.496669 8.945049e-02 8.989337e-02 9.077948e-02 9.210991e-02 9.388649e-02 9.611170e-02 9.878869e-02 1.019212e-01 1.055137e-01 1.095709e-01 ... 1.298739e-01 1.309308e-01 1.318710e-01 1.326944e-01 1.334007e-01 1.339895e-01 1.344609e-01 1.348145e-01 1.350503e-01 1.351683e-01
47.997422 4.379940e-02 4.415709e-02 4.487296e-02 4.594831e-02 4.738519e-02 4.918644e-02 5.135559e-02 5.389689e-02 5.681528e-02 6.011636e-02 ... 7.681423e-02 7.768715e-02 7.846403e-02 7.914455e-02 7.972841e-02 8.021534e-02 8.060514e-02 8.089764e-02 8.109271e-02 8.119027e-02
49.875495 1.901671e-03 2.152505e-03 2.654713e-03 3.409594e-03 4.419191e-03 5.686271e-03 7.214318e-03 9.007517e-03 1.107074e-02 1.340954e-02 ... 2.542159e-02 2.605335e-02 2.661587e-02 2.710881e-02 2.753187e-02 2.788481e-02 2.816741e-02 2.837951e-02 2.852098e-02 2.859173e-02
50.000000 -3.092591e-04 -6.524872e-05 4.233110e-04 1.157715e-03 2.139995e-03 3.372907e-03 4.859916e-03 6.605188e-03 8.613578e-03 1.089061e-02 ... 2.259949e-02 2.321560e-02 2.376419e-02 2.424495e-02 2.465757e-02 2.500181e-02 2.527745e-02 2.548432e-02 2.562231e-02 2.569133e-02

261 rows × 40 columns

Viewing results#

Plotting up to 10 curves distributed evenly on the neuron:

[14]:

sim.plot()

[14]:

Plotting the Hodgkin-Huxley currents:

[15]:

sim.plot.I(sn.channels.Hodgkin_Huxley)

[15]:

Getting a field view of the potential:

[16]:

sim.plot.V_field()

[16]:

Running the electrodiffusion part:

[17]:

sim.run_diff(max_step=1)

100%|██████████| 50.0/50 [00:01<00:00, 40.02ms/s]

Plotting the calcium concentration dynamics

[18]:

sim.plot.C(sn.Species.Ca)

[18]:

[19]:

sim.plot.C_field(sn.Species.Ca)

[19]: