odynn.neuron module¶

class odynn.neuron.BioNeuronTf(init_p=None, dt=0.1, fixed=(), constraints=None, groups=None, n_rand=None)[source]¶

Bases: odynn.models.celeg.CElegansNeuron, odynn.neuron.NeuronTf

Class representing a neuron, implemented using Tensorflow. This class allows simulation and optimization, alone and in a Circuit. It can contain several neurons at the same time. Which in turn can be optimized in parallel, or be used to represent the entire neurons in a Circuit.

Attributes:	`groups` list indicating the group of each neuron `hidden_init_state` For behavioral models eg LSTM `init_params` initial model parameters `init_state` ndarray, Initial state vector `num` int, Number of neurons being modeled in this object parameter_names `trainable` True if the object can be optimized `variables` Current variables of the models

Methods

`apply_constraints`(session)	Apply the constraints to the object variables
`build_graph`([batch])	Build a tensorflow graph for running the neuron(s) on a series of input :param batch: dimension of the batch :type batch: int
`calculate`(i)	Iterate over i (current) and return the state variables obtained after each step
`get_random`()	Returns a dictionnary of random parameters
`init`(batch)	Method to implement whe initialization is needed, will be called before reset
`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
`reset`()	rebuild tf variable graph
`settings`()	Returns(str): string describing the object
`step`(X, i_inj)	Integrate and update state variable (voltage and possibly others) after one time step

apply_init
boxplot_vars
predump
set_init_param
study_vars

__init__(init_p=None, dt=0.1, fixed=(), constraints=None, groups=None, n_rand=None)[source]¶

Initializer :param init_p: initial parameters of the neuron(s). If init_p is a list, then this object

will model n = len(init_p) neurons

Parameters:	dt (float) – time step fixed (set) – parameters that are fixed and will stay constant in case of optimization. if fixed == ‘all’, all parameters will be constant constraints (dict of ndarray) – keys as parameters name, and values as [lower_bound, upper_bound]

apply_constraints(session)[source]¶

Apply the constraints to the object variables

Parameters:	session – tensorflow session

build_graph(batch=None)[source]¶

Build a tensorflow graph for running the neuron(s) on a series of input :param batch: dimension of the batch :type batch: int

Returns:	input placeholder and results of the run
Return type:	tf.placeholder, tf.Tensor

calculate(i)[source]¶

Iterate over i (current) and return the state variables obtained after each step

Parameters:	i (ndarray) – input current
Returns:	state vectors concatenated [i.shape[0], len(self.init_state)(, i.shape[1]), self.num]
Return type:	ndarray

groups¶: list indicating the group of each neuron Neurons with the same group share the same parameters

init_params¶: initial model parameters

parallelize(n)[source]¶

Add a dimension of size n in the initial parameters and initial state

Parameters:	n (int) – size of the new dimension

reset()[source]¶: rebuild tf variable graph

set_init_param(name, value=None)[source]¶

settings()[source]¶: Returns(str): string describing the object

trainable¶: True if the object can be optimized

variables¶: Current variables of the models

class odynn.neuron.NeuronLSTM(nb_layer=1, layer_size=50, extra_ca=0, dt=0.1, vars_init=None)[source]¶

Bases: odynn.neuron.NeuronTf

Behavior model of a neuron using an LSTM network

Attributes:	`groups` list indicating the group of each neuron `hidden_init_state` Give the initial state needed for the LSTM network `init_params` Initial model parameters `init_state` ndarray, Initial state vector `num` Number of neurons contained in the object, always 1 here `trainable` boolean stating if the neuron can be optimized `variables` dict, current Tf variables

Methods

`apply_constraints`(session)	Apply necessary constraints to the optimized variables
`apply_init`(sess)	Initialize the variables if loaded object
`build_graph`([batch])	Build the tensorflow graph.
`calculate`(i)	Iterate over i (current) and return the state variables obtained after each step
`init`(batch)	Method to implement whe initialization is needed, will be called before reset
`plot_output`(ts, i_inj, states[, y_states, …])	Plot voltage and ion concentrations, potentially compared to a target model
`plot_vars`(var_dic, suffix, show, save)	A function to plot the variables of the optimized object
`settings`()	Returns(str): string describing the object
`step`(X, hprev, i_inj)	Update function

predump
reset
study_vars

apply_init(sess)[source]¶

Initialize the variables if loaded object

Parameters:	sess – tf.Session

build_graph(batch=1)[source]¶: Build the tensorflow graph. Take care of the loop and the initial state.

calculate(i)[source]¶

Iterate over i (current) and return the state variables obtained after each step

Parameters:	i (ndarray) – input current
Returns:	state vectors concatenated [i.shape[0], len(self.init_state)(, i.shape[1]), self.num]
Return type:	ndarray

hidden_init_state¶: Give the initial state needed for the LSTM network

init(batch)[source]¶

Method to implement whe initialization is needed, will be called before reset

Parameters:	batch (int) – number of batches

init_params¶: Initial model parameters

num¶: Number of neurons contained in the object, always 1 here

predump(sess)[source]¶

reset()[source]¶

settings()[source]¶: Returns(str): string describing the object

step(X, hprev, i_inj)[source]¶

Update function

Parameters:	X (Tensor) – not used here, classical state hprev (tuple of LSTMStateTuple) – previous LSTM state i_inj (Tensor) – array of input currents, dimension [batch]
Returns:	Tensor containing the voltages in the first position
Return type:	Tensor

class odynn.neuron.NeuronTf(dt=0.1)[source]¶

Bases: odynn.models.model.Neuron, odynn.optim.Optimized

Abstract class whose implementation allow single optimization as well as in a Circuit

Attributes:	`groups` list indicating the group of each neuron `hidden_init_state` For behavioral models eg LSTM `init_params` dict, initial parameters `init_state` ndarray, Initial state vector `num` int, Number of neurons being modeled in this object `trainable` boolean stating if the neuron can be optimized `variables` dict, current Tf variables

Methods

`apply_constraints`(session)	Apply necessary constraints to the optimized variables
`build_graph`([batch])	Build the tensorflow graph.
`calculate`(i)	Iterate over i (current) and return the state variables obtained after each step
`init`(batch)	Method to implement whe initialization is needed, will be called before reset
`plot_output`(ts, i_inj, states[, y_states, …])	Plot voltage and ion concentrations, potentially compared to a target model
`plot_vars`(var_dic, suffix, show, save)	A function to plot the variables of the optimized object
`settings`()	Give a string describing the settings Returns(str): description
`step`(X, i)	Integrate and update state variable (voltage and possibly others) after one time step

apply_init
predump
study_vars

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])¶

groups¶: list indicating the group of each neuron Neurons with the same group share the same parameters

hidden_init_state¶: For behavioral models eg LSTM

init(batch)[source]¶

Method to implement whe initialization is needed, will be called before reset

Parameters:	batch (int) – number of batches

nb = 0¶

trainable¶: boolean stating if the neuron can be optimized

class odynn.neuron.Neurons(neurons)[source]¶

Bases: odynn.neuron.NeuronTf

This class allow to use neurons from different classes inheriting NeuronTf in a same Circuit

Attributes:	`groups` list indicating the group of each neuron `hidden_init_state` For behavioral models eg LSTM `init_params` dict, initial parameters `init_state` ndarray, Initial state vector `num` int, Number of neurons being modeled in this object `trainable` boolean stating if the neuron can be optimized `variables` dict, current Tf variables

Methods

`apply_constraints`(session)	Apply the constraints to the object variables
`build_graph`()	Build the tensorflow graph.
`calculate`(i)	Iterate over i (current) and return the state variables obtained after each step
`init`(batch)	call init method for all contained neuron objects
`plot_output`(ts, i_inj, states[, y_states, …])	Plot voltage and ion concentrations, potentially compared to a target model
`plot_vars`(var_dic, suffix, show, save)	A function to plot the variables of the optimized object
`settings`()	Returns(str): string describing the object
`step`(X, hidden, i)	Share the state and the input current into its embedded neurons

apply_init
predump
reset
study_vars

__init__(neurons)[source]¶

Parameters:	neurons (list) – list of NeuronTf objects
Raises:	`AttributeError` – If all neurons don’t share the same dt

apply_constraints(session)[source]¶

Apply the constraints to the object variables

Parameters:	session – tensorflow session

apply_init(session)[source]¶

build_graph()[source]¶: Build the tensorflow graph. Take care of the loop and the initial state.

calculate(i)[source]¶

Iterate over i (current) and return the state variables obtained after each step

Parameters:	i (ndarray) – input current
Returns:	state vectors concatenated [i.shape[0], len(self.init_state)(, i.shape[1]), self.num]
Return type:	ndarray

hidden_init_state¶: For behavioral models eg LSTM

init(batch)[source]¶: call init method for all contained neuron objects

predump(sess)[source]¶

reset()[source]¶

settings()[source]¶: Returns(str): string describing the object

step(X, hidden, i)[source]¶

Share the state and the input current into its embedded neurons

Parameters:	X (tf.Tensor) – precedent state vector i (tf.Tensor) – input current
Returns:	next state vector
Return type:	ndarray

class odynn.neuron.PyBioNeuron(init_p=None, dt=0.1)[source]¶

Bases: odynn.models.celeg.CElegansNeuron

Class representing a neuron, implemented only in Python This class allows simulation but not optimization

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