def _legion_state(self, inputs, t, argv):
"""!
@brief Returns new values of excitatory and inhibitory parts of oscillator and potential of oscillator.
@param[in] inputs (list): Initial values (current) of oscillator [excitatory, inhibitory, potential].
@param[in] t (double): Current time of simulation.
@param[in] argv (uint): Extra arguments that are not used for integration - index of oscillator.
@return (list) New values of excitatoty and inhibitory part of oscillator and new value of potential (not assign).
"""
index = argv;
x = inputs[0]; # excitatory
y = inputs[1]; # inhibitory
p = inputs[2]; # potential
potential_influence = heaviside(p + math.exp(-self._params.alpha * t) - self._params.teta);
dx = 3.0 * x - x ** 3.0 + 2.0 - y + self._stimulus[index] * potential_influence + self._coupling_term[index] + self._noise[index];
dy = self._params.eps * (self._params.gamma * (1.0 + math.tanh(x / self._params.betta)) - y);
neighbors = self.get_neighbors(index);
potential = 0.0;
for index_neighbor in neighbors:
potential += self._params.T * heaviside(self._excitatory[index_neighbor] - self._params.teta_x);
dp = self._params.lamda * (1.0 - p) * heaviside(potential - self._params.teta_p) - self._params.mu * p;
return [dx, dy, dp];
def _legion_state(self, inputs, t, argv):
"""!
@brief Returns new values of excitatory and inhibitory parts of oscillator and potential of oscillator.
@param[in] inputs (list): Initial values (current) of oscillator [excitatory, inhibitory, potential].
@param[in] t (double): Current time of simulation.
@param[in] argv (uint): Extra arguments that are not used for integration - index of oscillator.
@return (list) New values of excitatoty and inhibitory part of oscillator and new value of potential (not assign).
"""
index = argv;
x = inputs[0]; # excitatory
y = inputs[1]; # inhibitory
p = inputs[2]; # potential
potential_influence = heaviside(p + math.exp(-self._params.alpha * t) - self._params.teta);
dx = 3.0 * x - x ** 3.0 + 2.0 - y + self._stimulus[index] * potential_influence + self._coupling_term[index] + self._noise[index];
dy = self._params.eps * (self._params.gamma * (1.0 + math.tanh(x / self._params.betta)) - y);
neighbors = self.get_neighbors(index);
potential = 0.0;
for index_neighbor in neighbors:
potential += self._params.T * heaviside(self._excitatory[index_neighbor] - self._params.teta_x);
dp = self._params.lamda * (1.0 - p) * heaviside(potential - self._params.teta_p) - self._params.mu * p;
return [dx, dy, dp];
def _calculate_states(self, solution, t, step, int_step):
"""!
@brief Calculates new state of each oscillator in the network.
@param[in] solution (solve_type): Type solver of the differential equation.
@param[in] t (double): Current time of simulation.
@param[in] step (double): Step of solution at the end of which states of oscillators should be calculated.
@param[in] int_step (double): Step differentiation that is used for solving differential equation.
"""
next_excitatory = [0.0] * self._num_osc;
next_inhibitory = [0.0] * self._num_osc;
next_potential = [];
if (self._params.ENABLE_POTENTIONAL is True):
next_potential = [0.0] * self._num_osc;
# Update states of oscillators
for index in range (0, self._num_osc, 1):
if (self._params.ENABLE_POTENTIONAL is True):
result = odeint(self._legion_state, [self._excitatory[index], self._inhibitory[index], self._potential[index]], numpy.arange(t - step, t, int_step), (index , ));
[ next_excitatory[index], next_inhibitory[index], next_potential[index] ] = result[len(result) - 1][0:3];
else:
result = odeint(self._legion_state_simplify, [self._excitatory[index], self._inhibitory[index] ], numpy.arange(t - step, t, int_step), (index , ));
[ next_excitatory[index], next_inhibitory[index] ] = result[len(result) - 1][0:2];
# Update coupling term
neighbors = self.get_neighbors(index);
coupling = 0
for index_neighbor in neighbors:
coupling += self._dynamic_coupling[index][index_neighbor] * heaviside(self._excitatory[index_neighbor] - self._params.teta_x);
self._buffer_coupling_term[index] = coupling - self._params.Wz * heaviside(self._global_inhibitor - self._params.teta_xz);
# Update state of global inhibitory
result = odeint(self._global_inhibitor_state, self._global_inhibitor, numpy.arange(t - step, t, int_step), (None, ));
self._global_inhibitor = result[len(result) - 1][0];
self._noise = [random.random() * self._params.ro for i in range(self._num_osc)];
self._coupling_term = self._buffer_coupling_term[:];
self._inhibitory = next_inhibitory[:];
self._excitatory = next_excitatory[:];
if (self._params.ENABLE_POTENTIONAL is True):
self._potential = next_potential[:];
def _calculate_states(self, solution, t, step, int_step):
"""!
@brief Calculates new state of each oscillator in the network.
@param[in] solution (solve_type): Type solver of the differential equation.
@param[in] t (double): Current time of simulation.
@param[in] step (double): Step of solution at the end of which states of oscillators should be calculated.
@param[in] int_step (double): Step differentiation that is used for solving differential equation.
"""
next_excitatory = [0.0] * self._num_osc;
next_inhibitory = [0.0] * self._num_osc;
next_potential = [];
if (self._params.ENABLE_POTENTIONAL is True):
next_potential = [0.0] * self._num_osc;
# Update states of oscillators
for index in range (0, self._num_osc, 1):
if (self._params.ENABLE_POTENTIONAL is True):
result = odeint(self._legion_state, [self._excitatory[index], self._inhibitory[index], self._potential[index]], numpy.arange(t - step, t, int_step), (index , ));
[ next_excitatory[index], next_inhibitory[index], next_potential[index] ] = result[len(result) - 1][0:3];
else:
result = odeint(self._legion_state_simplify, [self._excitatory[index], self._inhibitory[index] ], numpy.arange(t - step, t, int_step), (index , ));
[ next_excitatory[index], next_inhibitory[index] ] = result[len(result) - 1][0:2];
# Update coupling term
neighbors = self.get_neighbors(index);
coupling = 0
for index_neighbor in neighbors:
coupling += self._dynamic_coupling[index][index_neighbor] * heaviside(self._excitatory[index_neighbor] - self._params.teta_x);
self._buffer_coupling_term[index] = coupling - self._params.Wz * heaviside(self._global_inhibitor - self._params.teta_xz);
# Update state of global inhibitory
result = odeint(self._global_inhibitor_state, self._global_inhibitor, numpy.arange(t - step, t, int_step), (None, ));
self._global_inhibitor = result[len(result) - 1][0];
self._noise = [random.random() * self._params.ro for i in range(self._num_osc)];
self._coupling_term = self._buffer_coupling_term[:];
self._inhibitory = next_inhibitory[:];
self._excitatory = next_excitatory[:];
if (self._params.ENABLE_POTENTIONAL is True):
self._potential = next_potential[:];
def _legion_state_simplify(self, inputs, t, argv):
"""!
@brief Returns new values of excitatory and inhibitory parts of oscillator of oscillator.
@details Simplify model doesn't consider oscillator potential.
@param[in] inputs (list): Initial values (current) of oscillator [excitatory, inhibitory].
@param[in] t (double): Current time of simulation.
@param[in] argv (uint): Extra arguments that are not used for integration - index of oscillator.
@return (list) New values of excitatoty and inhibitory part of oscillator (not assign).
"""
index = argv
x = inputs[0]
# excitatory
y = inputs[1]
# inhibitory
dx = 3.0 * x - x**3.0 + 2.0 - y + self._stimulus[
index] + self._coupling_term[index] + self._noise[index]
dy = self._params.eps * (self._params.gamma *
(1.0 + math.tanh(x / self._params.betta)) - y)
neighbors = self.get_neighbors(index)
potential = 0.0
for index_neighbor in neighbors:
potential += self._params.T * heaviside(
self._excitatory[index_neighbor] - self._params.teta_x)
return [dx, dy]
def _legion_state_simplify(self, inputs, t, argv):
"""!
@brief Returns new values of excitatory and inhibitory parts of oscillator of oscillator.
@details Simplify model doesn't consider oscillator potential.
@param[in] inputs (list): Initial values (current) of oscillator [excitatory, inhibitory].
@param[in] t (double): Current time of simulation.
@param[in] argv (uint): Extra arguments that are not used for integration - index of oscillator.
@return (list) New values of excitatoty and inhibitory part of oscillator (not assign).
"""
index = argv;
x = inputs[0]; # excitatory
y = inputs[1]; # inhibitory
dx = 3.0 * x - x ** 3.0 + 2.0 - y + self._stimulus[index] + self._coupling_term[index] + self._noise[index];
dy = self._params.eps * (self._params.gamma * (1.0 + math.tanh(x / self._params.betta)) - y);
neighbors = self.get_neighbors(index);
potential = 0.0;
for index_neighbor in neighbors:
potential += self._params.T * heaviside(self._excitatory[index_neighbor] - self._params.teta_x);
return [dx, dy];
def __allocate_neuron_patterns(self, start_iteration, stop_iteration):
"""!
@brief Allocates observation transposed matrix of neurons that is limited by specified periods of simulation.
@details Matrix where state of each neuron is denoted by zero/one in line with Heaviside function on each iteration.
@return (list) Transposed observation matrix that is limited by specified periods of simulation.
"""
pattern_matrix = []
for index_neuron in range(len(self.output[0])):
pattern_neuron = []
for iteration in range(start_iteration, stop_iteration):
pattern_neuron.append(heaviside(self.output[iteration][index_neuron]))
pattern_matrix.append(pattern_neuron)
return pattern_matrix
def allocate_observation_matrix(self):
"""!
@brief Allocates observation matrix in line with output dynamic of the network.
@details Matrix where state of each neuron is denoted by zero/one in line with Heaviside function on each iteration.
@return (list) Observation matrix of the network dynamic.
"""
number_neurons = len(self.output[0])
observation_matrix = []
for iteration in range(len(self.output)):
obervation_column = []
for index_neuron in range(number_neurons):
obervation_column.append(heaviside(self.output[iteration][index_neuron]))
observation_matrix.append(obervation_column)
return observation_matrix
