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params_synapses.py
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# -*- coding: utf-8 -*-
"""
Created on Wed Jan 19 15:56:03 2022
@author: Beatriz Herrera
Synapse models parameters and number of synapses per cell.
"""
from __future__ import division
import scipy.stats as st
from LFPy.inputgenerators import get_activation_times_from_distribution
def get_synp_params():
"""
Define synaptic models parameters.
Returns
-------
dict
"""
# Parmeters - synapse models
syn_models_params_AMPA = {
"e": 0, # reversal potential
"syntype": "AMPA", # conductance based exponential synapse
"tau_r_AMPA": 0.3, # Time constant, rise
"tau_d_AMPA": 1.8, # Time constant, decay
"weight": 0.00073027, # Synaptic weight
"record_current": False, # record synaptic currents
}
syn_models_params_NMDA = {
"e": 0, # reversal potential
"syntype": "NMDA", # conductance based exponential synapse
"tau_r_NMDA": 8.019, # Time constant, rise
"tau_d_NMDA": 34.9884, # Time constant, decay
"n_NMDA": 0.28011,
"gama_NMDA": 0.0765685,
"weight": 0.00131038, # Synaptic weight
"record_current": False, # record synaptic currents
}
syn_models_params_GABAA = {
"e": -80, # reversal potential
"syntype": "GABAA", # conductance based exponential synapse
"tau_r_GABAA": 0.2, # Time constant, rise
"tau_d_GABAA": 1.7, # Time constant, decay
"weight": 0.0001, # Synaptic weight
"record_current": False, # record synaptic currents
# 'record_potential': True
}
syn_models_params_GABAB = {
"Erev": -95, # reversal potential
"syntype": "GABAB3", # conductance based exponential synapse #
"gmax": 0.0001, # synaptic weight == maximal conductance
"weight": 1,
"record_current": False, # record synaptic currents
# 'record_potential': True
}
return dict(
syn_models_params_AMPA=syn_models_params_AMPA,
syn_models_params_NMDA=syn_models_params_NMDA,
syn_models_params_GABAA=syn_models_params_GABAA,
syn_models_params_GABAB=syn_models_params_GABAB,
)
def get_synp_dist_L3PCs(tstop, **kwargs):
"""
Define presynaptic inputs - parameters.
where to insert, how many, and which input statistics.
Number of synapses and ratios estimated from: Rapan et al. 2021. Neuroimage
(226): 117574.
Parameters
----------
tstop : float
end of the simulation.
Returns
-------
dict
"""
# - start time of stimulation
tstart_AMPA = 0
tstart_NMDA = tstart_AMPA
tstart_GABAA = 0
tstart_GABAB = 0
# number of NMDA synapses in the ablique + basal dendrites, used as
# reference number
n_dend_NMDA_L5PCs = 890 # includes basal and oblique dendritic synapses
n_AMPA_L3PCs = 0.296 * n_dend_NMDA_L5PCs
n_NMDA_L3PCs = 3.27 * n_dend_NMDA_L5PCs
insert_synapses_AMPA_args = {
# 'section' : 'apic',
"n_dend": int(round((0.18 + 0.39) * n_AMPA_L3PCs)),
"n_apic": int(round(0.42 * n_AMPA_L3PCs)),
"spTimesFun": get_activation_times_from_distribution,
"args_dend": dict(
tstart=tstart_AMPA,
tstop=tstop,
distribution=st.expon,
rvs_args=dict(
loc=0.0,
scale=(
(1 / kwargs["r_AMPA_dend"]) * 1e3
if "r_AMPA_dend" in kwargs.keys()
else 0
),
),
),
"args_apic": dict(
tstart=tstart_AMPA,
tstop=tstop,
distribution=st.expon,
rvs_args=dict(
loc=0.0,
scale=(
(1 / kwargs["r_AMPA_apic"]) * 1e3
if "r_AMPA_apic" in kwargs.keys()
else 0
),
),
),
}
insert_synapses_NMDA_args = {
"n_dend": int(round((0.18 + 0.39) * n_NMDA_L3PCs)),
"n_apic": int(round(0.42 * n_NMDA_L3PCs)),
"spTimesFun": get_activation_times_from_distribution,
"args_dend": dict(
tstart=tstart_NMDA,
tstop=tstop,
distribution=st.expon,
rvs_args=dict(
loc=0.0,
scale=(
(1 / kwargs["r_NMDA_dend"]) * 1e3
if "r_NMDA_dend" in kwargs.keys()
else 0
),
),
),
"args_apic": dict(
tstart=tstart_NMDA,
tstop=tstop,
distribution=st.expon,
rvs_args=dict(
loc=0.0,
scale=(
(1 / kwargs["r_NMDA_apic"]) * 1e3
if "r_NMDA_apic" in kwargs.keys()
else 0
),
),
),
}
n_GABA = int(
round(
(
insert_synapses_NMDA_args["n_dend"]
+ insert_synapses_NMDA_args["n_apic"]
+ insert_synapses_AMPA_args["n_apic"]
+ insert_synapses_AMPA_args["n_dend"]
)
/ 4
)
)
n_dend_GABA = int(round(n_GABA / 4.39))
n_apic_GABA = n_GABA - n_dend_GABA
insert_synapses_GABAA_args = {
"n_dend": int(round(n_dend_GABA / 1.77)),
"n_apic": int(round(n_apic_GABA / 1.67)),
"spTimesFun": get_activation_times_from_distribution,
"args_dend": dict(
tstart=tstart_GABAA,
tstop=tstop,
distribution=st.expon,
rvs_args=dict(
loc=0.0,
scale=(
(1 / kwargs["r_GABAA_dend"]) * 1e3
if "r_GABAA_dend" in kwargs.keys()
else 0
),
),
),
"args_apic": dict(
tstart=tstart_GABAA,
tstop=tstop,
distribution=st.expon,
rvs_args=dict(
loc=0.0,
scale=(
(1 / kwargs["r_GABAA_apic"]) * 1e3
if "r_GABAA_apic" in kwargs.keys()
else 0
),
),
),
}
insert_synapses_GABAB_args = {
"n_dend": int(round(n_dend_GABA - insert_synapses_GABAA_args["n_dend"])),
"n_apic": int(round(n_apic_GABA - insert_synapses_GABAA_args["n_apic"])),
"spTimesFun": get_activation_times_from_distribution,
"args_dend": dict(
tstart=tstart_GABAB,
tstop=tstop,
distribution=st.expon,
rvs_args=dict(
loc=0.0,
scale=(
(1 / kwargs["r_GABAB_dend"]) * 1e3
if "r_GABAB_dend" in kwargs.keys()
else 0
),
),
),
"args_apic": dict(
tstart=tstart_GABAB,
tstop=tstop,
distribution=st.expon,
rvs_args=dict(
loc=0.0,
scale=(
(1 / kwargs["r_GABAB_apic"]) * 1e3
if "r_GABAB_apic" in kwargs.keys()
else 0
),
),
),
}
return dict(
insert_synapses_AMPA_args=insert_synapses_AMPA_args,
insert_synapses_NMDA_args=insert_synapses_NMDA_args,
insert_synapses_GABAA_args=insert_synapses_GABAA_args,
insert_synapses_GABAB_args=insert_synapses_GABAB_args,
)
def get_synp_dist_L5PCs(tstop, **kwargs):
"""
Define presynaptic inputs - parameters.
where to insert, how many, and which input statistics.
Parameters
----------
tstop : float
end of the simulation.
Returns
-------
dict
"""
# - start time of stimulation
tstart_AMPA = 0
tstart_NMDA = tstart_AMPA
tstart_GABAA = 0
tstart_GABAB = 0
# number of NMDA synapses in the ablique + basal dendrites, used as
# reference number
n_dend_NMDA = 890 # includes basal and oblique dendritic synapses
insert_synapses_AMPA_args = {
# 'section' : 'apic',
"n_dend": int(round(0.1045 * n_dend_NMDA)),
"n_apic": int(round(0.397 * n_dend_NMDA)),
"spTimesFun": get_activation_times_from_distribution,
"args_dend": dict(
tstart=tstart_AMPA,
tstop=tstop,
distribution=st.expon,
rvs_args=dict(
loc=0.0,
scale=(
(1 / kwargs["r_AMPA_dend"]) * 1e3
if "r_AMPA_dend" in kwargs.keys()
else 0
),
),
),
"args_oblq": dict(
tstart=tstart_AMPA,
tstop=tstop,
distribution=st.expon,
rvs_args=dict(
loc=0.0,
scale=(
(1 / kwargs["r_AMPA_oblq"]) * 1e3
if "r_AMPA_oblq" in kwargs.keys()
else 0
),
),
),
"args_apic": dict(
tstart=tstart_AMPA,
tstop=tstop,
distribution=st.expon,
rvs_args=dict(
loc=0.0,
scale=(
(1 / kwargs["r_AMPA_apic"]) * 1e3
if "r_AMPA_apic" in kwargs.keys()
else 0
),
),
),
}
insert_synapses_NMDA_args = {
"n_dend": n_dend_NMDA,
"n_apic": int(round(1.35 * n_dend_NMDA)),
"spTimesFun": get_activation_times_from_distribution,
"args_dend": dict(
tstart=tstart_NMDA,
tstop=tstop,
distribution=st.expon,
rvs_args=dict(
loc=0.0,
scale=(
(1 / kwargs["r_NMDA_dend"]) * 1e3
if "r_NMDA_dend" in kwargs.keys()
else 0
),
),
),
"args_oblq": dict(
tstart=tstart_NMDA,
tstop=tstop,
distribution=st.expon,
rvs_args=dict(
loc=0.0,
scale=(
(1 / kwargs["r_NMDA_oblq"]) * 1e3
if "r_NMDA_oblq" in kwargs.keys()
else 0
),
),
),
"args_apic": dict(
tstart=tstart_NMDA,
tstop=tstop,
distribution=st.expon,
rvs_args=dict(
loc=0.0,
scale=(
(1 / kwargs["r_NMDA_apic"]) * 1e3
if "r_NMDA_apic" in kwargs.keys()
else 0
),
),
),
}
n_GABA = int(
round(
(
n_dend_NMDA
+ insert_synapses_NMDA_args["n_apic"]
+ insert_synapses_AMPA_args["n_apic"]
+ insert_synapses_AMPA_args["n_dend"]
)
/ 4
)
)
n_dend_GABA = int(round(n_GABA / 4.39))
n_apic_GABA = n_GABA - n_dend_GABA
insert_synapses_GABAA_args = {
"n_dend": int(round(n_dend_GABA / 1.77)),
"n_apic": int(round(n_apic_GABA / 1.67)),
"spTimesFun": get_activation_times_from_distribution,
"args_dend": dict(
tstart=tstart_GABAA,
tstop=tstop,
distribution=st.expon,
rvs_args=dict(
loc=0.0,
scale=(
(1 / kwargs["r_GABAA_dend"]) * 1e3
if "r_GABAA_dend" in kwargs.keys()
else 0
),
),
),
"args_oblq": dict(
tstart=tstart_GABAA,
tstop=tstop,
distribution=st.expon,
rvs_args=dict(
loc=0.0,
scale=(
(1 / kwargs["r_GABAA_oblq"]) * 1e3
if "r_GABAA_oblq" in kwargs.keys()
else 0
),
),
),
"args_apic": dict(
tstart=tstart_GABAA,
tstop=tstop,
distribution=st.expon,
rvs_args=dict(
loc=0.0,
scale=(
(1 / kwargs["r_GABAA_apic"]) * 1e3
if "r_GABAA_apic" in kwargs.keys()
else 0
),
),
),
}
insert_synapses_GABAB_args = {
"n_dend": int(round(n_dend_GABA - insert_synapses_GABAA_args["n_dend"])),
"n_apic": int(round(n_apic_GABA - insert_synapses_GABAA_args["n_apic"])),
"spTimesFun": get_activation_times_from_distribution,
"args_dend": dict(
tstart=tstart_GABAB,
tstop=tstop,
distribution=st.expon,
rvs_args=dict(
loc=0.0,
scale=(
(1 / kwargs["r_GABAB_dend"]) * 1e3
if "r_GABAB_dend" in kwargs.keys()
else 0
),
),
),
"args_oblq": dict(
tstart=tstart_GABAB,
tstop=tstop,
distribution=st.expon,
rvs_args=dict(
loc=0.0,
scale=(
(1 / kwargs["r_GABAB_oblq"]) * 1e3
if "r_GABAB_oblq" in kwargs.keys()
else 0
),
),
),
"args_apic": dict(
tstart=tstart_GABAB,
tstop=tstop,
distribution=st.expon,
rvs_args=dict(
loc=0.0,
scale=(
(1 / kwargs["r_GABAB_apic"]) * 1e3
if "r_GABAB_apic" in kwargs.keys()
else 0
),
),
),
}
return dict(
insert_synapses_AMPA_args=insert_synapses_AMPA_args,
insert_synapses_NMDA_args=insert_synapses_NMDA_args,
insert_synapses_GABAA_args=insert_synapses_GABAA_args,
insert_synapses_GABAB_args=insert_synapses_GABAB_args,
)
def get_cluster_parameters():
"""
Return cluster parameters.
Returns
-------
cluster_parameters : dict
Dictionary containing the clusters' size and length,
based on the literature, and other parameters.
"""
cluster_parameters = {
"CLUSTER_L": 20, # length of the clusters in um
"CLUSTER_SIZE": 20, # number of synapses in a cluster
}
return cluster_parameters