odynn.models.celeg module

class odynn.models.celeg.CElegansNeuron(init_p=None, tensors=False, dt=0.1)[source]

Bases: odynn.models.model.BioNeuron

Full Hodgkin-Huxley Model implemented for C. elegans

Attributes:
init_state

ndarray, Initial state vector

num

int, Number of neurons being modeled in this object

parameter_names

Methods

calculate(i_inj) Simulate the neuron with input current i_inj and return the state vectors
get_random() Returns a dictionnary of random parameters
parallelize(n) Add a dimension of size n in the initial parameters and initial state
plot_output(ts, i_inj, states[, y_states, …]) Plot voltage and ion concentrations, potentially compared to a target model
plot_results(ts, i_inj_values, results[, …]) plot all dynamics
plot_vars(var_dic[, suffix, show, save, func]) plot variation/comparison/boxplots of all variables organized by categories
plot_vars_gate(name, mdp, scale, tau, fig, …) plot the gates variables
step(X, i_inj) Integrate and update state variable (voltage and possibly others) after one time step
boxplot_vars  
study_vars  
REST_CA = 0.0
static boxplot_vars(var_dic, suffix='', show=False, save=True)[source]
default_init_state = array([-6.0e+01, 0.0e+00, 9.5e-01, 0.0e+00, 0.0e+00, 1.0e+00, 1.0e-07])

initial state for neurons – voltage, rates and $[Ca^{2+}]$

default_params = {'C_m': 20.0, 'E_Ca': 20.0, 'E_K': -60.0, 'E_L': -60.0, 'decay_ca': 110.0, 'e__mdp': -3.36, 'e__scale': 6.75, 'e__tau': 10.0, 'f__mdp': 25.2, 'f__scale': -5.03, 'f__tau': 151.0, 'g_Ca': 3.0, 'g_Kf': 0.07, 'g_Ks': 10.0, 'g_L': 0.005, 'h__alpha': 0.282, 'h__mdp': 6.42, 'h__scale': -1.0, 'n__mdp': 19.9, 'n__scale': 15.9, 'n__tau': 25.0, 'p__mdp': -8.05, 'p__scale': 7.43, 'p__tau': 100.0, 'q__mdp': -15.6, 'q__scale': -9.97, 'q__tau': 150.0, 'rho_ca': 0.23}

default parameters as a dictionnary

static get_random()[source]

Returns a dictionnary of random parameters

plot_results(ts, i_inj_values, results, ca_true=None, suffix='', show=True, save=False)[source]

plot all dynamics

Parameters:
  • ts –
  • i_inj_values –
  • results –
  • ca_true – (Default value = None)
  • suffix – (Default value = “”)
  • show (bool) – If True, show the figure (Default value = True)
  • save (bool) – If True, save the figure (Default value = False)

Returns:

classmethod plot_vars(var_dic, suffix='evolution', show=False, save=True, func=<function plot>)[source]

plot variation/comparison/boxplots of all variables organized by categories

Parameters:
  • var_dic –
  • suffix – (Default value = “”)
  • show (bool) – If True, show the figure (Default value = True)
  • save (bool) – If True, save the figure (Default value = False)
  • func – (Default value = plot)

Returns:

static plot_vars_gate(name, mdp, scale, tau, fig, pos, labs, func=<function plot>)[source]

plot the gates variables

Parameters:
  • name –
  • mdp –
  • scale –
  • tau –
  • fig –
  • pos –
  • labs –
  • func – (Default value = plot)

Returns:

step(X, i_inj)[source]

Integrate and update state variable (voltage and possibly others) after one time step

Parameters:
  • X (ndarray) – State variables
  • i (float) – Input current
Returns:

updated state vector
Return type:

ndarray
classmethod study_vars(p, suffix='', target=None, show=False, save=True)[source]
odynn.models.celeg.give_rand()[source]