def __init__(self):
<EOL>point_neuron_network_params.__init__(self)<EOL><INDENT>SIMULATION PARAMETERS        <EOL><DEDENT>self.networkSimParams = {<EOL>'<STR_LIT>' :     self.tstop - self.tstart,<EOL>'<STR_LIT>' :          self.dt,<EOL>'<STR_LIT>' : self.spike_output_path,<EOL>'<STR_LIT:label>' :       '<STR_LIT>',<EOL>'<STR_LIT>' :         '<STR_LIT>',<EOL>'<STR_LIT>' : self.get_GIDs(),<EOL>}<EOL>self.calculateCSD = True<EOL><INDENT>MODEL PARAMETERS          <EOL><DEDENT>self.SCALING = <NUM_LIT:1.0><EOL>testing = False<EOL>if testing:<EOL><INDENT>self.PATH_m_y = os.path.join('<STR_LIT>', '<STR_LIT>')<EOL>self.m_y = [Y + '<STR_LIT:_>' + y + '<STR_LIT>' for Y, y in self.mapping_Yy]<EOL><DEDENT>else:<EOL><INDENT>self.PATH_m_y = os.path.join('<STR_LIT>', '<STR_LIT>')<EOL>self.m_y = [<EOL>'<STR_LIT>',<EOL>'<STR_LIT>',<EOL>'<STR_LIT>',<EOL>'<STR_LIT>',<EOL>'<STR_LIT>',<EOL>'<STR_LIT>',<EOL>'<STR_LIT>',<EOL>'<STR_LIT>',<EOL>'<STR_LIT>',<EOL>'<STR_LIT>',<EOL>'<STR_LIT>',<EOL>'<STR_LIT>',<EOL>'<STR_LIT>',<EOL>'<STR_LIT>',<EOL>'<STR_LIT>',<EOL>'<STR_LIT>',<EOL>]<EOL><DEDENT>self.J = self._compute_J()<EOL>J_YX = np.zeros(self.C_YX.shape)<EOL>J_YX += self.J<EOL>J_YX[:, <NUM_LIT:2>::<NUM_LIT:2>] *= self.g<EOL>if hasattr(self, '<STR_LIT>'):<EOL><INDENT>J_YX[<NUM_LIT:0>, <NUM_LIT:3>] *= self.PSP_23e_4e / self.PSP_e<EOL><DEDENT>if hasattr(self, '<STR_LIT>'):<EOL><INDENT>J_YX[<NUM_LIT:2>, <NUM_LIT:4>] *= self.g_4e_4i / self.g<EOL><DEDENT>self.J_yX = {}<EOL>for Y, y in self.mapping_Yy:<EOL><INDENT>[i] = np.where(np.array(self.Y) == Y)[<NUM_LIT:0>]<EOL>self.J_yX.update({y : J_YX[i, ]})<EOL><DEDENT>self.layerBoundaries = np.array([[     <NUM_LIT:0.0>,   -<NUM_LIT>],<EOL>[  -<NUM_LIT>,  -<NUM_LIT>],<EOL>[ -<NUM_LIT>,  -<NUM_LIT>],<EOL>[ -<NUM_LIT>, -<NUM_LIT>],<EOL>[-<NUM_LIT>, -<NUM_LIT>]])<EOL>self.depths = self._calcDepths()<EOL>self.y_zip_list = list(zip(self.y, self.m_y,<EOL>self.depths, self.N_y))<EOL>self.cellParams = {<EOL>'<STR_LIT>' : self.model_params['<STR_LIT>'],<EOL>'<STR_LIT>' : True,<EOL>'<STR_LIT>' : self.model_params['<STR_LIT>'] * <NUM_LIT> / <NUM_LIT:1.0>, <EOL>'<STR_LIT>' : <NUM_LIT:1.0>,<EOL>'<STR_LIT>' : <NUM_LIT>,<EOL>'<STR_LIT>' : self.model_params['<STR_LIT>'],    <EOL>'<STR_LIT>' : '<STR_LIT>',<EOL>'<STR_LIT>' : <NUM_LIT:100>,<EOL>'<STR_LIT>' : self.dt,<EOL>'<STR_LIT>' : self.dt,<EOL>'<STR_LIT>' : self.tstart,<EOL>'<STR_LIT>' : self.tstop,<EOL>'<STR_LIT>' : False,<EOL>}<EOL>self.yCellParams = self._yCellParams()<EOL>self.rand_rot_axis = {}<EOL>for y, _, _, _ in self.y_zip_list:<EOL><INDENT>if y.rfind('<STR_LIT:p>') >= <NUM_LIT:0>:<EOL><INDENT>self.rand_rot_axis.update({y : ['<STR_LIT:z>']})<EOL><DEDENT>else:<EOL><INDENT>self.rand_rot_axis.update({y : ['<STR_LIT:x>', '<STR_LIT:z>']})<EOL><DEDENT><DEDENT>self.simulationParams = {}<EOL>self.populationParams = {}<EOL>for y, _, depth, N_y in self.y_zip_list:<EOL><INDENT>self.populationParams.update({<EOL>y : {<EOL>'<STR_LIT>' : int(N_y*self.SCALING),<EOL>'<STR_LIT>' : np.sqrt(<NUM_LIT:1000>**<NUM_LIT:2> / np.pi),<EOL>'<STR_LIT>' : depth - <NUM_LIT>,<EOL>'<STR_LIT>' : depth + <NUM_LIT>,<EOL>'<STR_LIT>' : <NUM_LIT:1.>,            <EOL>}<EOL>})<EOL><DEDENT>self.synParams = {}<EOL>for y in self.y:<EOL><INDENT>if y.rfind('<STR_LIT:p>') >= <NUM_LIT:0>:<EOL><INDENT>section = ['<STR_LIT>', '<STR_LIT>']<EOL><DEDENT>else:<EOL><INDENT>section = ['<STR_LIT>']<EOL><DEDENT>self.synParams.update({<EOL>y : {<EOL>'<STR_LIT>' : '<STR_LIT>',  <EOL>'<STR_LIT>' : section,<EOL>},<EOL>})<EOL><DEDENT>self.tau_yX = {}<EOL>for y in self.y:<EOL><INDENT>self.tau_yX.update({<EOL>y : [self.model_params["<STR_LIT>"] if '<STR_LIT:I>' in X else<EOL>self.model_params["<STR_LIT>"] for X in self.X]<EOL>})<EOL><DEDENT>self.synDelayLoc, self.synDelayScale = self._synDelayParams()<EOL>self.electrodeParams = {<EOL>'<STR_LIT:x>' : np.zeros(<NUM_LIT:16>),<EOL>'<STR_LIT:y>' : np.zeros(<NUM_LIT:16>),<EOL>'<STR_LIT:z>' : -np.mgrid[<NUM_LIT:0>:<NUM_LIT:16>] * <NUM_LIT:100>,<EOL>'<STR_LIT>' : <NUM_LIT>,<EOL>'<STR_LIT:N>' : np.array([[<NUM_LIT:1>, <NUM_LIT:0>, <NUM_LIT:0>]]*<NUM_LIT:16>),<EOL>'<STR_LIT:r>' : <NUM_LIT>,<EOL>'<STR_LIT:n>' : <NUM_LIT:50>,<EOL>'<STR_LIT>' : None,<EOL>'<STR_LIT>' : '<STR_LIT>',<EOL>'<STR_LIT>': np.array([[-<NUM_LIT>, -<NUM_LIT>, -<NUM_LIT>, <NUM_LIT>],[<NUM_LIT:0>, <NUM_LIT:0>, <NUM_LIT:10>, <NUM_LIT:10>]]),<EOL>}<EOL>self.savelist = [<EOL>'<STR_LIT>',<EOL>'<STR_LIT>',<EOL>'<STR_LIT>',<EOL>'<STR_LIT>',<EOL>'<STR_LIT:x>',<EOL>'<STR_LIT:y>',<EOL>'<STR_LIT:z>',<EOL>'<STR_LIT>',<EOL>'<STR_LIT>',<EOL>'<STR_LIT>',<EOL>'<STR_LIT>',<EOL>'<STR_LIT>',<EOL>]<EOL>self.dt_output = <NUM_LIT:1.><EOL>self.recordSingleContribFrac = <NUM_LIT:0.><EOL>
Inherited class defining additional attributes needed by e.g., the
classes population.Population and
population.DummyNetwork

This class do not take any kwargs