# ConnPlotter --- A Tool to Generate Connectivity Pattern Matrices
#
# This file is part of ConnPlotter.
#
# Copyright (C) 2009 Hans Ekkehard Plesser/UMB
#
# ConnPlotter is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or
# (at your option) any later version.
#
# ConnPlotter is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with ConnPlotter. If not, see <http://www.gnu.org/licenses/>.
"""
Non-Dale example model.
Two layer A, B, with single population each.
Both layers make excitatory and inhibitory projections
to each other, violating Dale's law.
Build with
ConnectionPattern(..., ..., synTypes=(((SynType('exc', 1.0, 'b'),
SynType('inh', -1.0, 'r')),)))
"""
def non_dale():
"""
Build lists representing non-Dale network model.
Returns:
layerList, connectList, modelList
"""
def modCopy(orig, diff):
"""Create copy of dict orig, update with diff, return."""
assert(isinstance(orig, dict))
assert(isinstance(diff, dict))
tmp = orig.copy()
tmp.update(diff)
return tmp
N = 40
modelList = []
layerList = [('A', {'columns': N, 'rows': N, 'extent': [1.0, 1.0],
'elements': 'iaf_neuron'}),
('B', {'columns': N, 'rows': N, 'extent': [1.0, 1.0],
'elements': 'iaf_neuron'})]
common = {'connection_type': 'divergent',
'synapse_model' : 'static_synapse',
'delays' : 1.0}
connectList = [
('A', 'B',
modCopy(common, {'mask' : {'circular': {'radius': 0.2}},
'kernel' : 0.8,
'weights': 2.0})),
('A', 'B',
modCopy(common, {'mask' : {'circular': {'radius': 0.3}},
'kernel' : 0.4,
'weights': -2.0})),
('B', 'A',
modCopy(common, {'mask' : {'rectangular':
{'lower_left' : [-0.4,-0.2],
'upper_right': [ 0.4, 0.2]}},
'kernel' : 1.0,
'weights': 2.0})),
('B', 'A',
modCopy(common, {'mask' : {'rectangular':
{'lower_left' : [-0.2,-0.4],
'upper_right': [ 0.2, 0.4]}},
'kernel' : 1.0,
'weights': -2.0})),
]
return layerList, connectList, modelList