__author__ = 'milsteina'
from specify_cells import *
import random
import os
"""
Builds a cell locally so each engine is ready to receive jobs one at a time, specified by a list of indexes
corresponding to which synapses to stimulate. Remember to categorize output by distance from dendrite origin to soma.
"""
morph_filename = 'EB2-late-bifurcation.swc'
mech_filename = '043016 Type A - km2_NMDA_KIN5_Pr'
rec_filename = 'output'+datetime.datetime.today().strftime('%m%d%Y%H%M')+'-pid'+str(os.getpid())
NMDA_type = 'NMDA_KIN5'
ISI = 0.3
def stim_actual_group((group_index, num_spines)):
"""
Called by controller, mapped to each engine. Activates the specified number of spines in the specified group.
:param group_index: int
:param num_spines: int
:return: str
"""
spine_group = groups_to_stim[group_index]
loc = np.median([spine.synapses[0].loc for spine in spine_group['spines']])
path_type = spine_group['path_type']
path_index = spine_group['path_index']
sim.parameters['path_index'] = path_index
sim.parameters['path_type'] = path_type
sim.parameters['path_category'] = spine_group['path_category']
spine = spine_group['spines'][0]
sim.modify_rec(2, node=spine.parent.parent, loc=loc)
if path_type == 'trunk':
sim.modify_rec(3, node=spine.parent.parent)
else:
sim.modify_rec(3, node=cell.get_dendrite_origin(spine))
sim.modify_rec(4, node=spine, object=spine.synapses[0].target(NMDA_type), param='_ref_g')
sim.parameters['syn_indexes'] = []
for i, spine in enumerate(spine_group['spines'][:num_spines]):
sim.parameters['syn_indexes'].append(spine.index)
syn = spine.synapses[0]
spike_times = h.Vector([equilibrate + ISI * i])
syn.source.play(spike_times)
start_time = time.time()
sim.run(v_init)
with h5py.File(data_dir+rec_filename+'.hdf5', 'a') as f:
sim.export_to_file(f, int(group_index*1e6+num_spines))
print 'Process: %i took %i s to stimulate %i synapses in path %i' % (os.getpid(), time.time() - start_time,
num_spines, path_index)
for spine in spine_group['spines'][:num_spines]:
syn = spine.synapses[0]
syn.source.play(h.Vector())
return rec_filename
def stim_single_expected((group_index, spine_index)):
"""
Called by controller, mapped to each engine. Activates a single spine specified by an index and saves the
resulting output to a file.
:param group_index: int
:param spine_index: int
:return: str
"""
spine_group = groups_to_stim[group_index]
path_type = spine_group['path_type']
loc = np.median([spine.synapses[0].loc for spine in spine_group['spines']])
spine = spine_group['spines'][spine_index]
syn = spine.synapses[0]
spike_times = h.Vector([equilibrate])
syn.source.play(spike_times)
sim.parameters['spine_index'] = spine.index
sim.parameters['path_index'] = spine_group['path_index']
sim.parameters['path_type'] = path_type
sim.parameters['path_category'] = spine_group['path_category']
sim.modify_rec(2, node=spine.parent.parent, loc=loc)
if path_type == 'trunk':
sim.modify_rec(3, node=spine.parent.parent)
else:
sim.modify_rec(3, node=cell.get_dendrite_origin(spine))
sim.modify_rec(4, node=spine, object=syn.target(NMDA_type), param='_ref_g')
start_time = time.time()
sim.run(v_init)
with h5py.File(data_dir+rec_filename+'.hdf5', 'a') as f:
sim.export_to_file(f, int(group_index*1e6+spine_index))
syn.source.play(h.Vector())
print 'Process: %i stimulated spine: %i in %i s' % (os.getpid(), spine.index, time.time() - start_time)
return rec_filename
equilibrate = 250. # time to steady-state
duration = 450.
v_init = -67.
syn_types = ['AMPA_KIN', NMDA_type]
cell = CA1_Pyr(morph_filename, mech_filename, full_spines=True)
cell.zero_na()
# these synapses will not be used, but must be inserted for inheritance of synaptic parameters from trunk
for branch in cell.trunk:
for spine in branch.spines:
syn = Synapse(cell, spine, syn_types, stochastic=0)
min_num_spines = 25
grouped_spines = {sec_type: [] for sec_type in ['basal', 'apical', 'tuft']}
for sec_type in ['basal', 'apical', 'tuft']:
path_category = 'proximal'
for branch in (branch for branch in cell._node_dict[sec_type] if cell.get_branch_order(branch) == 1 and
branch.sec.L >= 30. and len(branch.spines) >= min_num_spines):
spine_list = [spine for spine in branch.spines if cell.get_distance_to_node(cell.get_dendrite_origin(branch),
spine, loc=0.) <= 30.]
grouped_spines[sec_type].append({'path_category': path_category, 'spines': spine_list,
'path_index': spine_list[0].index, 'path_type': sec_type})
path_category = 'terminal'
for branch in (branch for branch in cell._node_dict[sec_type] if cell.is_terminal(branch) and
branch.sec.L >= 30. and len(branch.spines) >= min_num_spines):
distance = cell.get_distance_to_node(cell.get_dendrite_origin(branch), branch, loc=1.)
spine_list = [spine for spine in branch.spines if distance -
cell.get_distance_to_node(cell.get_dendrite_origin(branch), spine, loc=0.) <= 30.]
grouped_spines[sec_type].append({'path_category': path_category, 'spines': spine_list,
'path_index': spine_list[0].index, 'path_type': sec_type})
path_category = 'intermediate'
for branch in (branch for branch in cell._node_dict[sec_type] if
cell.get_distance_to_node(cell.get_dendrite_origin(branch), branch, loc=1.) >= 80.):
spine_list = [spine for spine in branch.spines if
30. <= cell.get_distance_to_node(cell.get_dendrite_origin(branch), spine, loc=0.) <= 60.]
if len(spine_list) >= min_num_spines:
grouped_spines[sec_type].append({'path_category': path_category, 'spines': spine_list,
'path_index': spine_list[0].index, 'path_type': sec_type})
local_random = random.Random()
groups_to_stim = []
for sec_type in ['basal', 'apical', 'tuft']:
groups_to_stim.extend(grouped_spines[sec_type])
trunk_bifurcation = [trunk for trunk in cell.trunk if cell.is_bifurcation(trunk, 'trunk')]
if trunk_bifurcation:
trunk_branches = [branch for branch in trunk_bifurcation[0].children if branch.type == 'trunk']
# get where the thickest trunk branch gives rise to the tuft
trunk = max(trunk_branches, key=lambda node: node.sec(0.).diam)
trunk = (node for node in cell.trunk if cell.node_in_subtree(trunk, node) and 'tuft' in (child.type
for child in node.children)).next()
else:
trunk_bifurcation = [node for node in cell.trunk if 'tuft' in (child.type for child in node.children)]
trunk = trunk_bifurcation[0]
for spine_group in groups_to_stim:
local_random.seed(spine_group['path_index'])
local_random.shuffle(spine_group['spines'])
for spine in spine_group['spines']:
syn = Synapse(cell, spine, syn_types, stochastic=0)
cell.init_synaptic_mechanisms()
sim = QuickSim(duration, verbose=0)
sim.parameters['equilibrate'] = equilibrate
sim.parameters['duration'] = duration
sim.append_rec(cell, cell.tree.root, description='soma', loc=0.)
sim.append_rec(cell, trunk, description='trunk', loc=0.)
sim.append_rec(cell, trunk, description='branch', loc=0.5) # placeholder for local branch
sim.append_rec(cell, trunk, description='origin', loc=1.) # placeholder for local dendrite origin
spine = groups_to_stim[0]['spines'][0]
sim.append_rec(cell, spine, object=spine.synapses[0].target(NMDA_type), param='_ref_g', description='NMDA_g')