def run(config_file):
synapses.load()
from bmtk.simulator.bionet.pyfunction_cache import add_weight_function
def gaussianBL(edge_props, source, target):
w0 = edge_props["syn_weight"]
sigma = edge_props["weight_sigma"]
return np.random.normal(w0, sigma, 1)
def lognormal(edge_props, source, target):
m = edge_props["syn_weight"]
s = edge_props["weight_sigma"]
mean = np.log(m) - 0.5 * np.log((s / m)**2 + 1)
std = np.sqrt(np.log((s / m)**2 + 1))
return np.random.lognormal(mean, std, 1)
add_weight_function(lognormal)
add_weight_function(gaussianBL)
conf = bionet.Config.from_json(config_file, validate=True)
conf.build_env()
graph = bionet.BioNetwork.from_config(conf)
sim = bionet.BioSimulator.from_config(conf, network=graph)
sim.run()
bionet.nrn.quit_execution()
def run(config_file):
conf = config.from_json(config_file)
io.setup_output_dir(conf)
add_weight_function(gaussianLL)
nrn.load_neuron_modules(conf)
graph = BioGraph.from_config(conf, property_schema=AIPropertySchema)
net = BioNetwork.from_config(conf, graph)
sim = Simulation.from_config(conf, network=net)
sim.run()
nrn.quit_execution()
def run(config_file):
from bmtk.simulator.bionet.pyfunction_cache import add_weight_function
def gaussianBL(edge_props, source, target):
w0 = edge_props["syn_weight"]
sigma = edge_props["weight_sigma"]
return np.random.normal(w0, sigma, 1)
add_weight_function(gaussianBL)
conf = bionet.Config.from_json(config_file, validate=True)
conf.build_env()
graph = bionet.BioNetwork.from_config(conf)
sim = bionet.BioSimulator.from_config(conf, network=graph)
sim.run()
bionet.nrn.quit_execution()
def run(config_file):
warnings.simplefilter(action='ignore', category=FutureWarning)
synapses.load()
from bmtk.simulator.bionet.pyfunction_cache import add_weight_function
def gaussianBL(edge_props, source, target):
w0 = edge_props["syn_weight"]
sigma = edge_props["weight_sigma"]
return np.random.normal(w0, sigma, 1)
def lognormal(edge_props, source, target):
m = edge_props["syn_weight"]
s = edge_props["weight_sigma"]
mean = np.log(m) - 0.5 * np.log((s/m)**2+1)
std = np.sqrt(np.log((s/m)**2 + 1))
return np.random.lognormal(mean, std, 1)
add_weight_function(lognormal)
add_weight_function(gaussianBL)
conf = bionet.Config.from_json(config_file, validate=True)
conf.build_env()
graph = bionet.BioNetwork.from_config(conf)
# This fixes the morphology error in LFP calculation
pop = graph._node_populations['SPWR_biophysical']
for node in pop.get_nodes():
node._node._node_type_props['morphology'] = node.model_template[1]
sim = bionet.BioSimulator.from_config(conf, network=graph)
# This calls insert_mechs() on each cell to use its gid as a seed
# to the random number generator, so that each cell gets a different
# random seed for the point-conductance noise
#cells = graph.get_local_cells()
#for cell in cells:
# cells[cell].hobj.insert_mechs(cells[cell].gid)
sim.run()
bionet.nrn.quit_execution()
def lognormal(edge_props, source, target):
m = edge_props["syn_weight"]
s = edge_props["weight_sigma"]
mean = np.log(m) - 0.5 * np.log((s / m)**2 + 1)
std = np.sqrt(np.log((s / m)**2 + 1))
try:
maximum = edge_props["weight_max"]
return max(min(maximum, np.random.lognormal(mean, std, 1)), 0)
except:
return max(0, np.random.lognormal(mean, std, 1))
add_weight_function(lognormal)
add_weight_function(gaussianBL)
conf = bionet.Config.from_json(config_file, validate=True)
conf.build_env()
graph = bionet.BioNetwork.from_config(conf)
#import pdb; pdb.set_trace()
sim = bionet.BioSimulator.from_config(conf, network=graph)
# from analyze_area import analyze_area
# analyze_area(graph.get_local_cells()[0]._morph.seg_prop)
#import pdb; pdb.set_trace()
# cell = graph.get_local_cells()[1]
# memb = h.Vector()
# memb.record(cell.hobj.soma[0](0.5)._ref_v)
#
import math
from bmtk.simulator.bionet.pyfunction_cache import add_weight_function
def default_weight_fnc(edge_props, src_props, trg_props):
return edge_props['syn_weight']
def wmax(edge_props, src_props, trg_props):
return edge_props["syn_weight"]
def gaussianLL(edge_props, src_props, trg_props):
src_tuning = src_props['tuning_angle']
tar_tuning = trg_props['tuning_angle']
w0 = edge_props["syn_weight"]
sigma = edge_props["weight_sigma"]
delta_tuning = abs(abs(abs(180.0 - abs(float(tar_tuning) - float(src_tuning)) % 360.0) - 90.0) - 90.0)
weight = w0 * math.exp(-(delta_tuning / sigma) ** 2)
return weight
add_weight_function(wmax, 'wmax', overwrite=False)
add_weight_function(gaussianLL, 'gaussianLL', overwrite=False)
add_weight_function(default_weight_fnc, 'default_weight_fnc', overwrite=False)
# following conditions are met: # # 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following # disclaimer. # # 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following # disclaimer in the documentation and/or other materials provided with the distribution. # # 3. Redistributions for commercial purposes are not permitted without the Allen Institute's written permission. For # purposes of this license, commercial purposes is the incorporation of the Allen Institute's software into anything for # which you will charge fees or other compensation. Contact [email protected] for commercial licensing # opportunities. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, # INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE # DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, # SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR # SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, # WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # from bmtk.simulator.bionet.pyfunction_cache import add_weight_function def wmax(tar_prop, src_prop, con_prop): w0 = con_prop["weight_max"] return w0 add_weight_function(wmax, 'wmax', overwrite=False)
"""Simulates an example network of 14 cell receiving two kinds of exernal input as defined in configuration file"""
import os, sys
from bmtk.simulator import bionet
import numpy as np
from bmtk.simulator.bionet.pyfunction_cache import add_weight_function
def Lognormal(edge_props, source, target):
w0 = edge_props["syn_weight"]
sigma = edge_props["weight_sigma"]
s = np.random.lognormal(w0, sigma)
return s
add_weight_function(Lognormal)
def run(config_file):
conf = bionet.Config.from_json(config_file, validate=True)
conf.build_env()
graph = bionet.BioNetwork.from_config(conf)
sim = bionet.BioSimulator.from_config(conf, network=graph)
sim.run()
bionet.nrn.quit_execution()
if __name__ == '__main__':
if __file__ != sys.argv[-1]:
run(sys.argv[-1])