neurodynex.cable_equation package

Submodules

neurodynex.cable_equation.passive_cable module

Implements compartmental model of a passive cable. See Neuronal Dynamics Chapter 3 Section 2

neurodynex.cable_equation.passive_cable.getting_started()[source]

A simple code example to get started.

neurodynex.cable_equation.passive_cable.simulate_passive_cable(current_injection_location=[166.66666667 * umetre], input_current=<brian2.input.timedarray.TimedArray object>, length=0.5 * mmetre, diameter=2. * umetre, r_longitudinal=0.5 * metre ** 3 * kilogram * second ** -3 * amp ** -2, r_transversal=1.25 * metre ** 4 * kilogram * second ** -3 * amp ** -2, e_leak=-70. * mvolt, initial_voltage=-70. * mvolt, capacitance=0.008 * metre ** -4 * kilogram ** -1 * second ** 4 * amp ** 2, nr_compartments=200, simulation_time=5. * msecond)[source]

Builds a multicompartment cable and numerically approximates the cable equation.

Parameters:
  • t_spikes (int) – list of spike times
  • current_injection_location (list) – List [] of input locations (Quantity, Length): [123.*b2.um]
  • input_current (TimedArray) – TimedArray of current amplitudes. One column per current_injection_location.
  • length (Quantity) – Length of the cable: 0.8*b2.mm
  • diameter (Quantity) – Diameter of the cable: 0.2*b2.um
  • r_longitudinal (Quantity) – The longitudinal (axial) resistance of the cable: 0.5*b2.kohm*b2.mm
  • r_transversal (Quantity) – The transversal resistance (=membrane resistance): 1.25*b2.Mohm*b2.mm**2
  • e_leak (Quantity) – The reversal potential of the leak current (=resting potential): -70.*b2.mV
  • initial_voltage (Quantity) – Value of the potential at t=0: -70.*b2.mV
  • capacitance (Quantity) – Membrane capacitance: 0.8*b2.uF/b2.cm**2
  • nr_compartments (int) – Number of compartments. Spatial discretization: 200
  • simulation_time (Quantity) – Time for which the dynamics are simulated: 5*b2.ms
Returns:

The state monitor contains the membrane voltage in a Time x Location matrix. The SpatialNeuron object specifies the simulated neuron model and gives access to the morphology. You may want to use those objects for spatial indexing: myVoltageStateMonitor[mySpatialNeuron.morphology[0.123*b2.um]].v
Return type:

(StateMonitor, SpatialNeuron)

Module contents