#!/usr/bin/env python3
"""
Simulate upstate conditions for Patch Samples 4-5 and Matrix Samples 2-3 models.
Modify local channel conductances at site of clustered input for each neuron
to achieve upstate duration and amplitude consistent with experimental averages.
Do current injection with modified conductances to confirm modifying them does
not greatly alter the fit to current injection data.
Simulate without blocking sodium channels.
Simulate with additional dispersed inputs.
Simulation steps:
For each neuron:
- Randomly select parameters from within a range to vary
- parameters to vary:
- Random seed necessary for selecting parameters?
For each set of parameters:
- Use same random seeds to control synapse selection
- [done] simulate upstate only: Need an upstate seed (same every sim/param set)
- [done] Simulate dispersed only: Need a Dispersed seed (same every sim/param set for now)
- [done] Simulate upstate and dispersed together: Use same upstate seed and same dispersed seed
- [ ] range over dispersion frequency params
- [later] Should we simulate "spatially disperse" the clustered inputs but at the same time as a control? Not for now
- [done] Simulate single EPSP (EPSP seed?-same every sim)
- [done] Simulate IV traces to compare to original model IV to see how much the optimization fit is messed up
- [ ] simulate upstate plus current injection at increasing steps...
TO DO:
Save voltage at soma from each simulation
File name scheme:
param_set_X_sim_name_sim_variable_name_value_neuron-name_vm.txt
e.g.
param_set_1__upstate_plus_dispersed__dispersed_freq_375__D1PatchSample5_vm.txt
Make plotting optional argument
Save parameter variation values (and any necessary random seeds?) for each simulation
param_set_list.csv
set_ID (corresponds to param_set_X in filenames), var1name, var2name...
e.g.
0, 2, 3,...
"""
import importlib
import numpy as np
def rand_mod_dict():
mod_dict = {"D1MatrixSample2": {}, "D1PatchSample5": {}}
var_range = {
"KaS": [0, 4],
"NMDA": [0, 4],
"CaR": [0, 20],
"AMPA": [0.1, 1],
"CaL12": [0, 4],
"CaL13": [0, 4],
"CaT32": [0, 4],
"CaT33": [0, 4],
"Kir": [0, 4],
"KaF": [0, 4],
}
for mod in mod_dict:
for var in var_range:
mod_dict[mod][var] = np.random.uniform(*var_range[var])
return mod_dict
def make_mod_dict():
mod_dict = {
"D1MatrixSample2": {},
# "D1MatrixSample3": {},
# "D1PatchSample4": {},
"D1PatchSample5": {},
}
mod_dict["D1MatrixSample2"] = {
"KaS": 0.5, # 0.2,
"NMDA": 2.25,
"CaR": 10, # 1,
"AMPA": 0.4, # 2,
"CaL12": 1, # 0.01,
"CaL13": 1, # 0.25,
"CaT32": 0.5, # 0.001,
"CaT33": 1, # 0.05,
"Kir": 1,
}
# mod_dict["D1MatrixSample3"] = {
# "KaS": 1,
# "NMDA": 1,
# "CaR": 1,
# "AMPA": .25,#1,
# "CaL12": 1,
# "CaL13": 1,
# "CaT32": 1,
# "CaT33": 1,
# "Kir": 1,
# "KaF": 1,
# "CaCC": 0,
# }
# mod_dict["D1PatchSample4"] = {
# "KaS": 1,
# "NMDA": 2,
# "CaR": 100,
# "AMPA": .19,#1,
# "CaL12": 1,
# "CaL13": 1,
# "CaT32": 1,
# "CaT33": 1,
# "Kir": 1,
# "KaF": 1,
# }
mod_dict["D1PatchSample5"] = {
"KaS": 1,
"NMDA": 2.25,
"CaR": 2,
"AMPA": 0.4, # .3,#1,
"CaL12": 1,
"CaL13": 1,
"CaT32": 1,
"CaT33": 1,
"Kir": 1,
"KaF": 1, # 1,
}
return mod_dict
def mod_local_gbar(complist, mod_dict):
# input_parent_dends = set([i.parent.parent for i in inputs])
for comp in complist:
for chan in mod_dict:
for child in comp.children:
if chan == child[0].name and "HHChannel" in str(child[0].__class__):
# print(child[0].Gbar)
child[0].Gbar *= mod_dict[chan]
# print(child[0].Gbar)
# print(child.path)
def mod_dist_gbar(model, mod_dict):
for chan in mod_dict:
if chan in model.Condset.D1.keys():
#print("{} before: {}".format(chan, model.Condset.D1[chan]))
model.Condset.D1[chan][model.param_cond.dist] *= mod_dict[chan]
#print("modifying {} ".format(chan))
#print("{} after: {}".format(chan, model.Condset.D1[chan]))
def setup_model(model, mod_dict, block_naf=False, filename=None):
model = importlib.import_module("moose_nerp.{}".format(model))
# from IPython import embed; embed()
from moose_nerp.prototypes import create_model_sim
from moose_nerp.prototypes import spatiotemporalInputMapping as stim
import moose
if filename is not None:
model.param_sim.fname = filename
model.param_sim.save_txt = True
model.param_sim.plot_vm = False
model.param_sim.plot_current = True
model.param_sim.plot_current_message = "getIk"
model.spineYN = True
model.calYN = True
model.synYN = True
model.SpineParams.explicitSpineDensity = 1e6
if any("patch" in v for v in model.morph_file.values()):
# model.SpineParams.spineParent = "570_3"
model.clusteredparent = "570_3"
if any("matrix" in v for v in model.morph_file.values()):
# model.SpineParams.spineParent = "1157_3"
model.clusteredparent = "1157_3"
model.SpineParams.spineParent = model.clusteredparent # "soma"
modelname = model.__name__.split(".")[-1]
model.param_syn._SynNMDA.Gbar = 10e-09 * mod_dict[modelname]["NMDA"]
model.param_syn._SynNMDA.tau2 *= 2
model.param_syn._SynNMDA.tau1 *= 2
model.param_syn._SynAMPA.Gbar = 1e-9 * mod_dict[modelname]["AMPA"]
mod_dist_gbar(model, mod_dict[modelname])
if block_naf:
for k, v in model.Condset.D1.NaF.items():
model.Condset.D1.NaF[k] = 0.0
model.param_syn.SYNAPSE_TYPES.nmda.MgBlock.C = 1
# create_model_sim.setupOptions(model)
# create_model_sim.setupNeurons(model)
# create_model_sim.setupOutput(model)
return model
def iv_main(model, mod_dict, block_naf=False, filename=None):
print("filename: {}".format(filename))
import numpy as np
from moose_nerp.prototypes import create_model_sim
from moose_nerp.prototypes import spatiotemporalInputMapping as stim
import moose
model = setup_model(model, mod_dict, block_naf=block_naf, filename=filename)
model.param_sim.plot_current = False
create_model_sim.setupOptions(model)
create_model_sim.setupNeurons(model)
create_model_sim.setupOutput(model)
create_model_sim.setupStim(model)
create_model_sim.runAll(model)
def upstate_main(
model,
mod_dict,
block_naf=False,
num_clustered=14,
n_per_clustered=2,
num_dispersed=100,
freq_dispersed=375,
n_per_dispersed=10,
clustered_seed=None,
dispersed_seed=None,
filename=None,
do_plots=False,
injection_current = None,
):
import numpy as np
from moose_nerp.prototypes import create_model_sim, tables
from moose_nerp.prototypes import spatiotemporalInputMapping as stim
import moose
model = setup_model(model, mod_dict, block_naf=block_naf, filename=filename)
create_model_sim.setupOptions(model)
create_model_sim.setupNeurons(model)
modelname = model.__name__.split(".")[-1]
branch_list = ["/D1[0]/{}[0]".format(model.clusteredparent)]
if num_clustered > 0:
inputs = stim.exampleClusteredDistal(
model,
nInputs=num_clustered,
branch_list=branch_list,
seed=clustered_seed, # 6,
)
parent_dend = [i.parent.parent for i in inputs]
parent_spine = [i.parent for i in inputs]
parents = parent_dend + parent_spine
input_parent_dends = set(parents)
# mod_local_gbar(input_parent_dends, mod_dict[modelname])
neuron = model.neurons["D1"][0]
bd = stim.getBranchDict(neuron)
comps = [moose.element(comp) for comp in bd[branch_list[0]]["CompList"]]
spines = [sp[0] for comp in comps for sp in comp.children if "head" in sp.name]
model.param_sim.plotcomps = [
s.split("/")[-1] for s in bd[branch_list[0]]["BranchPath"]
]
create_model_sim.setupOutput(model)
# mod_local_gbar(set(comps+spines), mod_dict[modelname])
# from IPython import embed; embed()
# import pdb;pdb.set_trace()
if num_clustered > 0:
spine_cur_tab = []
which_spine = inputs[0].parent
for ch in ["SKCa", "CaL13", "CaL12", "CaR", "CaT33", "CaT32", "ampa", "nmda"]:
chan = moose.element(which_spine.path + "/" + ch)
tab = moose.Table("data/" + chan.path.replace("/", "__").replace("[0]", ""))
moose.connect(tab, "requestOut", chan, "getIk")
spine_cur_tab.append(tab)
plotgates = ["CaR", "CaT32", "CaT33", "CaL12", "CaL13"]
model.gatetables = {}
for plotgate in plotgates:
model.gatetables[plotgate] = {}
gatepath = which_spine.path + "/" + plotgate
gate = moose.element(gatepath)
gatextab = moose.Table("/data/" + plotgate + "_gatex")
moose.connect(gatextab, "requestOut", gate, "getX")
model.gatetables[plotgate]["gatextab"] = gatextab
gateytab = moose.Table("/data/" + plotgate + "_gatey")
moose.connect(gateytab, "requestOut", gate, "getY")
model.gatetables[plotgate]["gateytab"] = gateytab
if model.Channels[plotgate][0][2] > 0:
gateztab = moose.Table("/data/" + plotgate + "_gatez")
moose.connect(gateztab, "requestOut", gate, "getZ")
model.gatetables[plotgate]["gateztab"] = gateztab
dispersed_inputs = stim.dispersed(
model,
nInputs=num_dispersed,
exclude_branch_list=branch_list,
seed=dispersed_seed,
)
if num_clustered > 0:
input_times = stim.createTimeTables(
inputs, model, n_per_syn=n_per_clustered, start_time=0.3
)
stim.createTimeTables(
dispersed_inputs, model, n_per_syn=10, start_time=0.35, freq=freq_dispersed, duration_limit=0.3
)
# c = moose.element('D1/634_3')
# c.Rm = c.Rm*100
if injection_current is not None:
model.param_sim.injection_current = [injection_current]
model.param_sim.injection_delay = 0.3
model.param_sim.injection_width = 0.1
create_model_sim.setupStim(model)
#print(u'◢◤◢◤◢◤◢◤ injection_current = {} ◢◤◢◤◢◤◢◤'.format(injection_current))
model.pg.firstLevel = injection_current
#from IPython import embed; embed()
simtime = 1.5 # 1.5
moose.reinit()
moose.start(simtime)
if do_plots:
create_model_sim.neuron_graph.graphs(model, model.vmtab, False, simtime)
from matplotlib import pyplot as plt
ax = plt.gca()
ax.set_title(modelname)
ax.axvspan(
input_times[0], input_times[-1], facecolor="gray", alpha=0.5, zorder=-10
)
import pprint
# ax.text(.5,.5,pprint.pformat(mod_dict[modelname]), transform = ax.transAxes)
plt.ion()
plt.show()
plt.figure()
for i in model.spinevmtab[0]:
t = np.linspace(0, 0.4, len(i.vector))
plt.plot(t, i.vector)
# n = model.neurons["D1"][0]
# d_vs_len = [
# (p, c.diameter) for c, p in zip(n.compartments, n.geometricalDistanceFromSoma)
# ]
# d_vs_len = np.array(d_vs_len)
# plt.figure()
# plt.scatter(d_vs_len[:,0],d_vs_len[:,1])
plt.figure()
for cur in spine_cur_tab:
plt.plot(cur.vector, label=cur.name.strip("_"))
plt.legend()
# create_model_sim.plot_channel.plot_gate_params(moose.element('/library/CaT32'),3)
# for c,d in model.gatetables.items():
# plt.figure()
# plt.title(c)
# for g,t in d.items():
# plt.plot(t.vector,label=g)
# plt.legend()
# from moose_nerp.prototypes import util
plt.show(block=True)
tables.write_textfiles(model, 0, ca=False, spines=False, spineca=False)
print("upstate filename: {}".format(filename))
# return model.vmtab['D1'][0].vector
# return plt.gcf()
#from IPython import embed
#embed()
def subprocess_main(function, model, mod_dict, kwds,time_limit):
from multiprocessing import Process, Queue
import time
# q = Queue()
p = Process(target=function, args=(model, mod_dict), kwargs=kwds)
p.start()
# result = q.get()
# print(result)
remaining = time_limit - time.time()
if remaining <=0:
p.terminate()
return
p.join(timeout=remaining-10)
p.terminate()
# return result
def mpi_main(mod_dict, sims):
if __name__ == "__main__":
from mpi4py import MPI
from mpi4py.futures import MPICommExecutor
import time
import pickle
with MPICommExecutor(MPI.COMM_WORLD, root=0) as executor:
time_limit = time.time() + 60 * 60 * 8#3.75 # 3.75 hours
if executor is not None:
results = []
make_new_params = False
if make_new_params:
param_set_list = [rand_mod_dict() for i in range(10000)]
with open("params.pickle", "wb") as f:
pickle.dump(param_set_list, f)
else:
with open("params.pickle",'rb') as f:
param_set_list = pickle.load(f)
# print(param_set_list)
for i, param_set in enumerate(param_set_list[:1020]):#1020
for key in mod_dict:
for sim in sims:
# param_set_1__upstate_plus_dispersed__dispersed_freq_375__D1PatchSample5_vm.txt
filename = (
"param_set_"
+ str(i)
+ "__"
+ sim["name"]
+ "__dispersed_freq_"
+ str(sim["kwds"].get("freq_dispersed"))
+ "__injection_current_"
+ str(sim["kwds"].get("injection_current"))
+ "__"
+ key
)
kwds = {k: v for k, v in sim["kwds"].items()}
kwds["filename"] = filename
# r = p.apply_async(upstate_main, args=(key, mod_dict),kwds={'num_dispersed':0})
# r = p.apply_async(sim["f"], args=(key, param_set), kwds=kwds)
r = executor.submit(
subprocess_main, *(sim["f"], key, param_set, kwds, time_limit)
)
results.append(r)
while True:
if all([res.done() for res in results]):
print('all results returned done; breaking')
break
if time.time() >= time_limit:
print("****************** TIME LIMIT EXCEEDED***********")
for res in results:
res.cancel()
#print('canceling', res)
#executor.shutdown(wait=False)
print('shutting down')
MPI.COMM_WORLD.Abort()
print('aborting')
break
print('done')
return
#while True:
# if time.time() >= time_limit:
# break
#MPI.COMM_WORLD.Abort()
if __name__ == "__main__":
mod_dict = make_mod_dict()
clustered_seed = 135
dispersed_seed = 172
single_epsp_seed = 314
sims = [
{
"name": "upstate_plus_rheobase",
"f": upstate_main,
"kwds": {
"num_dispersed": 0,
"block_naf": False,
"num_clustered": 14,
"clustered_seed": clustered_seed,
"injection_current": 25e-12,
},
},
{
"name": "upstate_plus_rheobase",
"f": upstate_main,
"kwds": {
"num_dispersed": 0,
"block_naf": False,
"num_clustered": 14,
"clustered_seed": clustered_seed,
"injection_current": 0e-12,
},
},
# {
# "name": "upstate_plus_rheobase",
# "f": upstate_main,
# "kwds": {
# "num_dispersed": 0,
# "block_naf": False,
# "num_clustered": 14,
# "clustered_seed": clustered_seed,
# "injection_current": 75e-12,
# },
# },
# {
# "name": "upstate_plus_rheobase",
# "f": upstate_main,
# "kwds": {
# "num_dispersed": 0,
# "block_naf": False,
# "num_clustered": 14,
# "clustered_seed": clustered_seed,
# "injection_current": 100e-12,
# },
# },
# {
# "name": "upstate_plus_rheobase",
# "f": upstate_main,
# "kwds": {
# "num_dispersed": 0,
# "block_naf": False,
# "num_clustered": 14,
# "clustered_seed": clustered_seed,
# "injection_current": 125e-12,
# },
# },
# {
# "name": "upstate_plus_rheobase",
# "f": upstate_main,
# "kwds": {
# "num_dispersed": 0,
# "block_naf": False,
# "num_clustered": 14,
# "clustered_seed": clustered_seed,
# "injection_current": 150e-12,
# },
# },
# {
# "name": "upstate_plus_rheobase",
# "f": upstate_main,
# "kwds": {
# "num_dispersed": 0,
# "block_naf": False,
# "num_clustered": 14,
# "clustered_seed": clustered_seed,
# "injection_current": 175e-12,
# },
# },
# {
# "name": "upstate_plus_rheobase",
# "f": upstate_main,
# "kwds": {
# "num_dispersed": 0,
# "block_naf": False,
# "num_clustered": 14,
# "clustered_seed": clustered_seed,
# "injection_current": 200e-12,
# },
# },
# {
# "name": "upstate_plus_rheobase",
# "f": upstate_main,
# "kwds": {
# "num_dispersed": 0,
# "block_naf": False,
# "num_clustered": 14,
# "clustered_seed": clustered_seed,
# "injection_current": 225e-12,
# },
# },
# {"name": "iv_curves", "f": iv_main, "kwds": {}},
# {
# "name": "upstate_only",
# "f": upstate_main,
# "kwds": {
# "num_dispersed": 0,
# "block_naf": True,
# "num_clustered": 14,
# "clustered_seed": clustered_seed,
# },
# },
# {"name": "iv_curves", "f": iv_main, "kwds": {}},
{
"name": "upstate_plus_new_dispersed_300ms",
"f": upstate_main,
"kwds": {
"num_dispersed": 100,
"freq_dispersed": 200,
"dispersed_seed": dispersed_seed,
"clustered_seed": clustered_seed,
},
},
{
"name": "upstate_plus_new_dispersed_300ms",
"f": upstate_main,
"kwds": {
"num_dispersed": 100,
"freq_dispersed": 250,
"dispersed_seed": dispersed_seed,
"clustered_seed": clustered_seed,
},
},
# {
# "name": "upstate_plus_new_dispersed_300ms",
# "f": upstate_main,
# "kwds": {
# "num_dispersed": 100,
# "freq_dispersed": 300,
# "dispersed_seed": dispersed_seed,
# "clustered_seed": clustered_seed,
# },
# },
# {
# "name": "upstate_plus_new_dispersed_300ms",
# "f": upstate_main,
# "kwds": {
# "num_dispersed": 100,
# "freq_dispersed": 350,
# "dispersed_seed": dispersed_seed,
# "clustered_seed": clustered_seed,
# },
# },
# {
# "name": "upstate_plus_new_dispersed_300ms",
# "f": upstate_main,
# "kwds": {
# "num_dispersed": 100,
# "freq_dispersed": 400,
# "dispersed_seed": dispersed_seed,
# "clustered_seed": clustered_seed,
# },
# },
# {
# "name": "upstate_plus_new_dispersed_300ms",
# "f": upstate_main,
# "kwds": {
# "num_dispersed": 100,
# "freq_dispersed": 450,
# "dispersed_seed": dispersed_seed,
# "clustered_seed": clustered_seed,
# },
# },
# {
# "name": "upstate_plus_new_dispersed_300ms",
# "f": upstate_main,
# "kwds": {
# "num_dispersed": 100,
# "freq_dispersed": 500,
# "dispersed_seed": dispersed_seed,
# "clustered_seed": clustered_seed,
# },
# },
# {
# "name": "single_epsp",
# "f": upstate_main,
# "kwds": {
# "num_dispersed": 0,
# "block_naf": True,
# "num_clustered": 1,
# "n_per_clustered": 1,
# "clustered_seed": single_epsp_seed,
# },
# },
{
"name": "new_dispersed_300ms_only",
"f": upstate_main,
"kwds": {
"num_dispersed": 100,
"num_clustered": 0,
"freq_dispersed": 200,
"dispersed_seed": dispersed_seed,
"clustered_seed": clustered_seed,
},
},
{
"name": "new_dispersed_300ms_only",
"f": upstate_main,
"kwds": {
"num_dispersed": 100,
"num_clustered": 0,
"freq_dispersed": 250,
"dispersed_seed": dispersed_seed,
"clustered_seed": clustered_seed,
},
},
# {
# "name": "new_dispersed_300ms_only",
# "f": upstate_main,
# "kwds": {
# "num_dispersed": 100,
# "num_clustered": 0,
# "freq_dispersed": 300,
# "dispersed_seed": dispersed_seed,
# "clustered_seed": clustered_seed,
# },
# },
# {
# "name": "new_dispersed_300ms_only",
# "f": upstate_main,
# "kwds": {
# "num_dispersed": 100,
# "num_clustered": 0,
# "freq_dispersed": 350,
# "dispersed_seed": dispersed_seed,
# "clustered_seed": clustered_seed,
# },
# },
# {
# "name": "new_dispersed_300ms_only",
# "f": upstate_main,
# "kwds": {
# "num_dispersed": 100,
# "num_clustered": 0,
# "freq_dispersed": 400,
# "dispersed_seed": dispersed_seed,
# "clustered_seed": clustered_seed,
# },
# },
# {
# "name": "new_dispersed_300ms_only",
# "f": upstate_main,
# "kwds": {
# "num_dispersed": 100,
# "num_clustered": 0,
# "freq_dispersed": 450,
# "dispersed_seed": dispersed_seed,
# "clustered_seed": clustered_seed,
# },
# },
# {
# "name": "new_dispersed_300ms_only",
# "f": upstate_main,
# "kwds": {
# "num_dispersed": 100,
# "num_clustered": 0,
# "freq_dispersed": 500,
# "dispersed_seed": dispersed_seed,
# "clustered_seed": clustered_seed,
# },
# },
]
import sys
args = sys.argv
# args.append('--single')
if len(args) > 1 and args[1] == "--single":
# upstate_main(list(mod_dict.keys())[0],mod_dict)
upstate_main("D1PatchSample5", mod_dict, do_plots=True)
elif len(args) > 1 and args[1] == "--iv":
# upstate_main(list(mod_dict.keys())[0],mod_dict)
iv_main("D1PatchSample5", mod_dict, filename="test")
elif len(args) > 1 and args[1] == "--mp":
results = []
from multiprocessing import Pool
with Pool(16, maxtasksperchild=1) as p:
param_set_list = [rand_mod_dict() for i in range(10000)]
import pickle
with open("params.pickle", "wb") as f:
pickle.dump(param_set_list, f)
#print(param_set_list)
for i, param_set in enumerate(param_set_list):
for key in mod_dict:
for sim in sims:
# param_set_1__upstate_plus_dispersed__dispersed_freq_375__D1PatchSample5_vm.txt
filename = (
"param_set_"
+ str(i)
+ "__"
+ sim["name"]
+ "__dispersed_freq_"
+ str(sim["kwds"].get("freq_dispersed"))
+ "__"
+ key
)
kwds = {k: v for k, v in sim["kwds"].items()}
kwds["filename"] = filename
# r = p.apply_async(upstate_main, args=(key, mod_dict),kwds={'num_dispersed':0})
r = p.apply_async(sim["f"], args=(key, param_set), kwds=kwds)
results.append(r)
for res in results:
res.wait()
else:
mpi_main(mod_dict, sims)
print('done?')