Spinal Dorsal Horn Network Model (Medlock et al 2022)

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Accession:267056
To explore spinal dorsal horn (SDH) network function, we developed a computational model of the circuit that is tightly constrained by experimental data. Our model comprises conductance-based model neurons that reproduce the characteristic firing patterns of excitatory and inhibitory spinal neurons. Excitatory spinal neuron subtypes defined by calretinin, somatostatin, delta-opioid receptor, protein kinase C gamma, or vesicular glutamate transporter 3 expression or by transient/central spiking/morphology and inhibitory neuron subtypes defined by parvalbumin or dynorphin expression or by islet morphology were synaptically connected according to available qualitative data. Synaptic weights were adjusted to produce firing in projection neurons, defined by neurokinin-1 expression, matching experimentally measured responses to a range of mechanical stimulus intensities. Input to the circuit was provided by three types of afferents (Aß, Ad, and C-fibres) whose firing rates were also matched to experimental data.
Reference:
1 . Medlock L, Sekiguchi K, Hong S, Dura-Bernal S, Lytton WW, Prescott SA (2022) Multiscale computer model of the spinal dorsal horn reveals changes in network processing associated with chronic pain The Journal of Neuroscience [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism:
Cell Type(s): Spinal cord lamina I neuron; Spinal cord lamina I-III interneuron;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NetPyNE; NEURON;
Model Concept(s): Sensory processing; Pain processing;
Implementer(s): Medlock, Laura [laura.medlock at mail.utoronto.ca]; Sekiguchi, Kazutaka [kazutaka.sekiguchi at shionogi.co.jp];
'''
Network parameters (netParams) script for Spinal Dorsal Horn Network Model
from Sekiguchi et al. (2021)
'''

from netpyne import specs, sim
from neuron import h
import numpy as np
import cells
from spkt_gen import *
import json
import sys
sys.path.insert(0, 'spkt')  # adding path to spkt dir
import genrn

try: 
    from __main__ import cfg
except:
    from cfg_mechanical import cfg

netParams = specs.NetParams()

#------------------------------------------------------------------------------
# Population
#------------------------------------------------------------------------------

### INPUT FREQUENCY FROM AFFERENT FIBERS WITH FIXED FACTOR ###
with open('spkt/spkt_SAI_%s.json' %(cfg.freq), 'rb') as spkt_SAI: spkt_SAI = json.load(spkt_SAI)
with open('spkt/spkt_SAII_%s.json' %(cfg.freq), 'rb') as spkt_SAII: spkt_SAII = json.load(spkt_SAII)
with open('spkt/spkt_Ad_%s.json' %(cfg.freq), 'rb') as spkt_Ad: spkt_Ad = json.load(spkt_Ad)
with open('spkt/spkt_C_%s.json' %(cfg.freq), 'rb') as spkt_C: spkt_C = json.load(spkt_C)

## PRIMARY AFFERENTS TIME-VARYING STIMULUS (NORMAL STIMULUS)
netParams.popParams['Ab_SAI'] = {'cellModel': 'VecStim', 'numCells': 10, 'spkTimes': spkt_SAI}  # input from Ab_slow adapting type I
netParams.popParams['Ab_SAII'] = {'cellModel': 'VecStim', 'numCells': 10, 'spkTimes': spkt_SAII}  # input from Ab_slow adapting type II
netParams.popParams['Ad'] = {'cellModel': 'VecStim', 'numCells': 20, 'spkTimes': spkt_Ad}  # input from Adelta
netParams.popParams['C_PEP'] = {'cellModel': 'VecStim', 'numCells': 80, 'spkTimes': spkt_C}  # input from peptidergic C fibers
netParams.popParams['C_NP'] = {'cellModel': 'VecStim', 'numCells': 80, 'spkTimes': spkt_C}  # input from non-peptidergic C fibers

## SETTING OF SPINAL NEURONS
cells.PKCRule['conds'] = {'cellType': 'PKC'}
cells.INcellRule['conds'] = {'cellType': 'IN'}
cells.EXdelayedRule['conds'] = {'cellType': 'EXdl'}
cells.CRRule['conds'] = {'cellType': 'CR'}
cells.SOMRule['conds'] = {'cellType': 'SOM'}
cells.EXinitialRule['conds'] = {'cellType': 'EXib'}
cells.PROcellRule['conds'] = {'cellType': 'PRO'}

cells.PROcellRule['secs']['soma']['threshold'] = 0

netParams.cellParams['PKC'] = cells.PKCRule
netParams.cellParams['VGLUT3Rule'] = cells.EXdelayedRule
netParams.cellParams['PVRule'] = cells.INcellRule
netParams.cellParams['DORRule'] = cells.EXdelayedRule
netParams.cellParams['TrCRule'] = cells.EXinitialRule
netParams.cellParams['DYNRule'] = cells.INcellRule
netParams.cellParams['SOMRule'] = cells.SOMRule
netParams.cellParams['CRRule'] = cells.CRRule
netParams.cellParams['ISLETRule'] = cells.INcellRule
netParams.cellParams['NK1Rule'] = cells.PROcellRule

## SETTING POPULATION OF SPINAL NEURONS
netParams.popParams['PKC' ] = {'cellType': 'PKC', 'numCells': 30} # PKCg+ neurons (excitatory)
netParams.popParams['VGLUT3'] = {'cellType': 'EXdl', 'numCells': 4} # VGLUT3+ neurons (excitatory)
netParams.popParams['PV'] = {'cellType': 'IN', 'numCells': 15} # PV+ neurons (inhibitory)
netParams.popParams['DOR' ] = {'cellType': 'EXdl', 'numCells': 30} # DOR+ neurons (excitatory)
netParams.popParams['TrC'] = {'cellType': 'EXib', 'numCells': 10} # Transient Central neurons (excitatory)
netParams.popParams['DYN'] = {'cellType': 'IN', 'numCells': 60} # Central/DYN+ neurons (inhibitory)
netParams.popParams['SOM'] = {'cellType': 'SOM', 'numCells': 15} # SOM+ neurons (excitatory)
netParams.popParams['CR'] = {'cellType': 'CR', 'numCells': 20} # CR+ neurons (excitatory)
netParams.popParams['ISLET'] = {'cellType': 'IN', 'numCells': 15} # Islet-type neurons (inhibitory)
netParams.popParams['NK1'] = {'cellType': 'PRO', 'numCells': 10} # NK1+ neurons (projection)

###################################################################################################################################
#   Synaptic Mechanisms
###################################################################################################################################
netParams.defaultThreshold = -30

netParams.synMechParams['AMPA'] = {'mod': 'AMPA_DynSyn'   , 'tau_rise': 0.1, 'tau_decay': 5            }
netParams.synMechParams['NMDA'] = {'mod': 'NMDA_DynSyn'   , 'tau_rise': 2  , 'tau_decay': 100          }
netParams.synMechParams['NK13'] = {'mod': 'NK1_DynSyn'    , 'tau_rise': 100, 'tau_decay': 1000         }
netParams.synMechParams['NK23'] = {'mod': 'NK1_DynSyn'    , 'tau_rise': 200, 'tau_decay': 3000         }
netParams.synMechParams['GABA'] = {'mod': 'GABAa_DynSyn'  , 'tau_rise': 0.1, 'tau_decay': 20, 'e': -70 }
netParams.synMechParams['GLY']  = {'mod': 'Glycine_DynSyn', 'tau_rise': 0.1, 'tau_decay': 10, 'e': -70 }

###################################################################################################################################
#   Connectivity Mechanisms   
###################################################################################################################################
# From Abeta Fibres to Spinal Interneurons
netParams.connParams['Ab_SAI_AMPA->PKC'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'Ab_SAI'}, 
    'postConds': {'popLabel': 'PKC'},  
    'weight': cfg.Ab_EX_AMPA,           
    'sec': 'dend',
    'probability': 0.2,
    'delay': 1.0,
    'loc': 0.5,
    'synMech': 'AMPA'} 

netParams.connParams['Ab_SAI_NMDA->PKC'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'Ab_SAI'}, 
    'postConds': {'popLabel': 'PKC'},  
    'weight': cfg.Ab_EX_NMDA,           
    'sec': 'dend',
    'probability': 0.2,
    'delay': 1.0,
    'loc': 0.5,
    'synMech': 'NMDA'} 

netParams.connParams['Ab_SAI_AMPA->VGLUT3'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'Ab_SAI'}, 
    'postConds': {'popLabel': 'VGLUT3'},  
    'weight': cfg.Ab_EX_AMPA,           
    'sec': 'dend',
    'probability': 0.2,
    'delay': 1.0,
    'loc': 0.5,
    'synMech': 'AMPA'} 

netParams.connParams['Ab_SAI_NMDA->VGLUT3'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'Ab_SAI'}, 
    'postConds': {'popLabel': 'VGLUT3'},  
    'weight': cfg.Ab_EX_NMDA,           
    'sec': 'dend',
    'probability': 0.2,
    'delay': 1.0,
    'loc': 0.5,
    'synMech': 'NMDA'} 

netParams.connParams['Ab_SAI_AMPA->PV'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'Ab_SAI'}, 
    'postConds': {'popLabel': 'PV'},  
    'weight': cfg.Ab_IN_AMPA,           
    'sec': 'dend',
    'probability': 0.2,
    'delay': 1.0,
    'loc': 0.5,
    'synMech': 'AMPA'} 

netParams.connParams['Ab_SAI_NMDA->PV'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'Ab_SAI'}, 
    'postConds': {'popLabel': 'PV'},  
    'weight': cfg.Ab_IN_NMDA,           
    'sec': 'dend',
    'probability': 0.2,
    'delay': 1.0,
    'loc': 0.5,
    'synMech': 'NMDA'} 

netParams.connParams['Ab_SAI_AMPA->DYN'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'Ab_SAI'}, 
    'postConds': {'popLabel': 'DYN'},  
    'weight': cfg.Ab_IN_AMPA,           
    'sec': 'dend',
    'probability': 0.2,
    'delay': 1.0,
    'loc': 0.5,
    'synMech': 'AMPA'} 

netParams.connParams['Ab_SAII_AMPA->PKC'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'Ab_SAII'}, 
    'postConds': {'popLabel': 'PKC'},  
    'weight': cfg.Ab_EX_AMPA,           
    'sec': 'dend',
    'probability': 0.2,
    'delay': 1.0,
    'loc': 0.5,
    'synMech': 'AMPA'} 

netParams.connParams['Ab_SAII_NMDA->PKC'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'Ab_SAII'}, 
    'postConds': {'popLabel': 'PKC'},  
    'weight': cfg.Ab_EX_NMDA,           
    'sec': 'dend',
    'probability': 0.2,
    'delay': 1.0,
    'loc': 0.5,
    'synMech': 'NMDA'} 

netParams.connParams['Ab_SAII_AMPA->VGLUT3'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'Ab_SAII'}, 
    'postConds': {'popLabel': 'VGLUT3'},  
    'weight': cfg.Ab_EX_AMPA,           
    'sec': 'dend',
    'probability': 0.2,
    'delay': 1.0,
    'loc': 0.5,
    'synMech': 'AMPA'} 

netParams.connParams['Ab_SAII_NMDA->VGLUT3'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'Ab_SAII'}, 
    'postConds': {'popLabel': 'VGLUT3'},  
    'weight': cfg.Ab_EX_NMDA,           
    'sec': 'dend',
    'probability': 0.2,
    'delay': 1.0,
    'loc': 0.5,
    'synMech': 'NMDA'} 

netParams.connParams['Ab_SAII_AMPA->PV'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'Ab_SAII'}, 
    'postConds': {'popLabel': 'PV'},  
    'weight': cfg.Ab_IN_AMPA,           
    'sec': 'dend',
    'probability': 0.2,
    'delay': 1.0,
    'loc': 0.5,
    'synMech': 'AMPA'} 

netParams.connParams['Ab_SAII_NMDA->PV'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'Ab_SAII'}, 
    'postConds': {'popLabel': 'PV'},  
    'weight': cfg.Ab_IN_NMDA,           
    'sec': 'dend',
    'probability': 0.2,
    'delay': 1.0,
    'loc': 0.5,
    'synMech': 'NMDA'} 

netParams.connParams['Ab_SAII_AMPA->DYN'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'Ab_SAII'}, 
    'postConds': {'popLabel': 'DYN'},  
    'weight': cfg.Ab_IN_AMPA,           
    'sec': 'dend',
    'probability': 0.2,
    'delay': 1.0,
    'loc': 0.5,
    'synMech': 'AMPA'} 

# From Adelta to Spinal Interneurons
netParams.connParams['Ad_AMPA->DOR'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'Ad'}, 
    'postConds': {'popLabel': 'DOR'},  
    'weight': cfg.Ad_AMPA,
    'probability': 0.2,
    'sec': 'dend',
    'delay': 5.0,
    'loc': 0.50,
    'synMech': 'AMPA'}

netParams.connParams['Ad_NMDA->DOR'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'Ad'}, 
    'postConds': {'popLabel': 'DOR'},  
    'weight': cfg.Ad_NMDA,
    'probability': 0.2,
    'sec': 'dend',
    'delay': 5.0,
    'loc': 0.50,
    'synMech': 'NMDA'}

netParams.connParams['Ad_AMPA->SOM'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'Ad'}, 
    'postConds': {'popLabel': 'SOM'},  
    'weight': cfg.Ad_AMPA,
    'probability': 0.2,
    'sec': 'dend',
    'delay': 5.0,
    'loc': 0.50,
    'synMech': 'AMPA'}

netParams.connParams['Ad_NMDA->SOM'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'Ad'}, 
    'postConds': {'popLabel': 'SOM'},  
    'weight': cfg.Ad_NMDA,
    'probability': 0.2,
    'sec': 'dend',
    'delay': 5.0,
    'loc': 0.50,
    'synMech': 'NMDA'}

netParams.connParams['Ad_AMPA->CR'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'Ad'}, 
    'postConds': {'popLabel': 'CR'},  
    'weight': cfg.Ad_AMPA,
    'probability': 0.2,
    'sec': 'dend',
    'delay': 5.0,
    'loc': 0.50,
    'synMech': 'AMPA'}

netParams.connParams['Ad_NMDA->CR'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'Ad'}, 
    'postConds': {'popLabel': 'CR'},  
    'weight': cfg.Ad_NMDA,
    'probability': 0.2,
    'sec': 'dend',
    'delay': 5.0,
    'loc': 0.50,
    'synMech': 'NMDA'}

# From Peptidergic C (C,TRPV1) to Spinal Interneurons
netParams.connParams['C_PEP_AMPA->TrC'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'C_PEP'}, 
    'postConds': {'popLabel': 'TrC'},  
    'weight': cfg.C_TrC_AMPA,
    'probability': 0.2,
    'sec': 'dend',
    'delay': 10.0,
    'loc': 0.5,
    'synMech': 'AMPA'}

netParams.connParams['C_PEP_AMPA->SOM'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'C_PEP'}, 
    'postConds': {'popLabel': 'SOM'},  
    'weight': cfg.C_EX_AMPA,
    'probability': 0.2,
    'sec': 'dend',
    'delay': 10.0,
    'loc': 0.5,
    'synMech': 'AMPA'}

netParams.connParams['C_PEP_NMDA->SOM'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'C_PEP'}, 
    'postConds': {'popLabel': 'SOM'},  
    'weight': cfg.C_EX_NMDA,
    'probability': 0.2,
    'sec': 'dend',
    'delay': 10.0,
    'loc': 0.5,
    'synMech': 'NMDA'}

netParams.connParams['C_PEP_AMPA->CR'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'C_PEP'}, 
    'postConds': {'popLabel': 'CR'},  
    'weight': cfg.C_EX_AMPA,
    'probability': 0.2,
    'sec': 'dend',
    'delay': 10.0,
    'loc': 0.5,
    'synMech': 'AMPA'}

netParams.connParams['C_PEP_NMDA->CR'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'C_PEP'}, 
    'postConds': {'popLabel': 'CR'},  
    'weight': cfg.C_EX_NMDA,
    'probability': 0.2,
    'sec': 'dend',
    'delay': 10.0,
    'loc': 0.5,
    'synMech': 'NMDA'}

netParams.connParams['C_PEP_AMPA->DYN'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'C_PEP'}, 
    'postConds': {'popLabel': 'DYN'},  
    'weight': cfg.C_DYN_AMPA,
    'probability': 0.2,
    'sec': 'dend',
    'delay': 10.0,
    'loc': 0.5,
    'synMech': 'AMPA'}

netParams.connParams['C_PEP_AMPA->ISLET'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'C_PEP'}, 
    'postConds': {'popLabel': 'ISLET'},  
    'weight': cfg.C_ISLET_AMPA,
    'probability': 0.2,
    'sec': 'dend',
    'delay': 10.0,
    'loc': 0.5,
    'synMech': 'AMPA'}

netParams.connParams['C_PEP_AMPA->NK1'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'C_PEP'}, 
    'postConds': {'popLabel': 'NK1'},  
    'weight': cfg.C_NK1_AMPA,
    'probability': 0.2,
    'sec': 'dend',
    'delay': 10.0,
    'loc': 0.5,
    'synMech': 'AMPA'}

netParams.connParams['C_PEP_NMDA->NK1'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'C_PEP'}, 
    'postConds': {'popLabel': 'NK1'},  
    'weight': cfg.C_NK1_NMDA,
    'probability': 0.2,
    'sec': 'dend',
    'delay': 10.0,
    'loc': 0.5,
    'synMech': 'NMDA'}

netParams.connParams['C_PEP_NK1->NK1'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'C_PEP'}, 
    'postConds': {'popLabel': 'NK1'},  
    'weight': cfg.C_NK1_NK1,
    'probability': 0.2,
    'sec': 'dend',
    'delay': 10.0,
    'loc': 0.5,
    'synMech': 'NK13'}

# From Non-Peptidergic C (C,IB4) to Spinal Interneurons
netParams.connParams['C_NP_AMPA->TrC'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'C_NP'}, 
    'postConds': {'popLabel': 'TrC'},  
    'weight': cfg.C_TrC_AMPA,
    'probability': 0.2,
    'sec': 'dend',
    'delay': 10.0,
    'loc': 0.5,
    'synMech': 'AMPA'}

netParams.connParams['C_NP_AMPA->SOM'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'C_NP'}, 
    'postConds': {'popLabel': 'SOM'},  
    'weight': cfg.C_EX_AMPA,
    'probability': 0.2,
    'sec': 'dend',
    'delay': 10.0,
    'loc': 0.5,
    'synMech': 'AMPA'}

netParams.connParams['C_NP_NMDA->SOM'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'C_NP'}, 
    'postConds': {'popLabel': 'SOM'},  
    'weight': cfg.C_EX_NMDA,
    'probability': 0.2,
    'sec': 'dend',
    'delay': 10.0,
    'loc': 0.5,
    'synMech': 'NMDA'}

netParams.connParams['C_NP_AMPA->CR'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'C_NP'}, 
    'postConds': {'popLabel': 'CR'},  
    'weight': cfg.C_EX_AMPA,
    'probability': 0.2,
    'sec': 'dend',
    'delay': 10.0,
    'loc': 0.5,
    'synMech': 'AMPA'}

netParams.connParams['C_NP_NMDA->CR'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'C_NP'}, 
    'postConds': {'popLabel': 'CR'},  
    'weight': cfg.C_EX_NMDA,
    'probability': 0.2,
    'sec': 'dend',
    'delay': 10.0,
    'loc': 0.5,
    'synMech': 'NMDA'}

netParams.connParams['C_NP_AMPA->DYN'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'C_NP'}, 
    'postConds': {'popLabel': 'DYN'},  
    'weight': cfg.C_DYN_AMPA,
    'probability': 0.2,
    'sec': 'dend',
    'delay': 10.0,
    'loc': 0.5,
    'synMech': 'AMPA'}

netParams.connParams['C_NP_AMPA->ISLET'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'C_NP'}, 
    'postConds': {'popLabel': 'ISLET'},  
    'weight': cfg.C_ISLET_AMPA,
    'probability': 0.2,
    'sec': 'dend',
    'delay': 10.0,
    'loc': 0.5,
    'synMech': 'AMPA'}

###################################################################################################################################
#   Connectivity Betweeen Spinal Neurons (inh>ex, ex>ex, ex/inh>projection)
###################################################################################################################################
# TO ePKC NEURONS:
netParams.connParams['VGLUT3_AMPA->PKC'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'VGLUT3'}, 
    'postConds': {'popLabel':'PKC'},  
    'weight': cfg.VGLUT3_PKC_AMPA,           
    'probability': 0.2,
    'sec': 'dend',
    'delay': 0.5, 
    'loc': 0.5,
    'synMech': 'AMPA'}

netParams.connParams['VGLUT3_NMDA->PKC'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'VGLUT3'}, 
    'postConds': {'popLabel':'PKC'},  
    'weight': cfg.VGLUT3_PKC_NMDA,           
    'probability': 0.2,
    'sec': 'dend',
    'delay': 0.5, 
    'loc': 0.5,
    'synMech': 'NMDA'}

netParams.connParams['PV_GABA->PKC'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'PV'}, 
    'postConds': {'popLabel':'PKC'},  
    'weight': cfg.PV_GABA,          
    'probability': 0.2,
    'sec': 'dend',
    'delay': 0.5, 
    'loc': 0.5,
    'synMech': 'GABA'}

netParams.connParams['PV_GLY->PKC'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'PV'}, 
    'postConds': {'popLabel':'PKC'},  
    'weight': cfg.PV_GLY,        
    'probability': 0.2,
    'sec': 'dend',
    'delay': 0.5, 
    'loc': 0.5,
    'synMech': 'GLY'}

# TO eDOR NEURONS
netParams.connParams['PV_GABA->DOR'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'PV'}, 
    'postConds': {'popLabel':'DOR'},  
    'weight': cfg.PV_GABA,
    'probability': 0.2,
    'sec': 'dend',
    'delay': 0.5, 
    'loc': 0.5,
    'synMech': 'GABA'}

netParams.connParams['PV_GLY->DOR'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'PV'}, 
    'postConds': {'popLabel':'DOR'},  
    'weight': cfg.PV_GLY,
    'probability': 0.2,
    'sec': 'dend',
    'delay': 0.5, 
    'loc': 0.5,
    'synMech': 'GLY'}

netParams.connParams['VGLUT3_AMPA->DOR'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'VGLUT3'}, 
    'postConds': {'popLabel':'DOR'},  
    'weight': cfg.VGLUT3_PKC_AMPA,           
    'probability': 0.2,
    'sec': 'dend',
    'delay': 0.5, 
    'loc': 0.5,
    'synMech': 'AMPA'}

netParams.connParams['VGLUT3_NMDA->DOR'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'VGLUT3'}, 
    'postConds': {'popLabel':'DOR'},  
    'weight': cfg.VGLUT3_PKC_NMDA,           
    'probability': 0.2,
    'sec': 'dend',
    'delay': 0.5, 
    'loc': 0.5,
    'synMech': 'NMDA'}

# TO eTrC NEURONS
netParams.connParams['PKC_AMPA->TrC'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'PKC'}, 
    'postConds': {'popLabel':'TrC'},  
    'weight': cfg.PKC_AMPA,           
    'probability': 0.2,
    'sec': 'dend',
    'delay': 0.5, 
    'loc': 0.5,
    'synMech': 'AMPA'}

netParams.connParams['ISLET_GABA->TrC'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'ISLET'}, 
    'postConds': {'popLabel':'TrC'},  
    'weight': cfg.ISLET_GABA,       
    'probability': 0.2,
    'sec': 'dend',
    'delay': 0.5, 
    'loc': 0.5,
    'synMech': 'GABA'}

# TO iISLET NEURONS
netParams.connParams['DYN_GABA->ISLET'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'DYN'}, 
    'postConds': {'popLabel':'ISLET'},  
    'weight': cfg.DYN_ISLET_GABA,           
    'probability': 0.2,
    'sec': 'dend',
    'delay': 0.5, 
    'loc': 0.5,
    'synMech': 'GABA'}

# TO iDYN NEURONS
netParams.connParams['ISLET_GABA->DYN'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'ISLET'}, 
    'postConds': {'popLabel':'DYN'},  
    'weight': cfg.ISLET_GABA,           
    'probability': 0.2,
    'sec': 'dend',
    'delay': 0.5, 
    'loc': 0.5,
    'synMech': 'GABA'}

# TO eSOM NEURONS
netParams.connParams['DYN_GABA->SOM'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'DYN'}, 
    'postConds': {'popLabel':'SOM'},  
    'weight': cfg.DYN_EX_GABA,           
    'probability': 0.2,
    'sec': 'dend',
    'delay': 0.5, 
    'loc': 0.5,
    'synMech': 'GABA'}

netParams.connParams['DYN_GLY->SOM'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'DYN'}, 
    'postConds': {'popLabel':'SOM'},  
    'weight': cfg.DYN_EX_GLY,           
    'probability': 0.2,
    'sec': 'dend',
    'delay': 0.5, 
    'loc': 0.5,
    'synMech': 'GLY'}

netParams.connParams['TrC_AMPA->SOM'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'TrC'}, 
    'postConds': {'popLabel':'SOM'},  
    'weight': cfg.TrC_AMPA,           
    'probability': 0.2,
    'sec': 'dend',
    'delay': 0.5, 
    'loc': 0.5,
    'synMech': 'AMPA'}

netParams.connParams['TrC_NMDA->SOM'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'TrC'}, 
    'postConds': {'popLabel':'SOM'},  
    'weight': cfg.TrC_NMDA,           
    'probability': 0.2,
    'sec': 'dend',
    'delay': 0.5, 
    'loc': 0.5,
    'synMech': 'NMDA'}

netParams.connParams['PKC_AMPA->SOM'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'PKC'}, 
    'postConds': {'popLabel':'SOM'},  
    'weight': cfg.PKC_AMPA,           
    'probability': 0.2,
    'sec': 'dend',
    'delay': 0.5, 
    'loc': 0.5,
    'synMech': 'AMPA'}

netParams.connParams['PKC_NMDA->SOM'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'PKC'}, 
    'postConds': {'popLabel':'SOM'},  
    'weight': cfg.PKC_NMDA,           
    'probability': 0.2,
    'sec': 'dend',
    'delay': 0.5, 
    'loc': 0.5,
    'synMech': 'NMDA'}

netParams.connParams['VGLUT3_AMPA->SOM'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'VGLUT3'}, 
    'postConds': {'popLabel':'SOM'},  
    'weight': cfg.VGLUT3_SOM_AMPA,           
    'probability': 0.2,
    'sec': 'dend',
    'delay': 0.5, 
    'loc': 0.5,
    'synMech': 'AMPA'}

netParams.connParams['VGLUT3_NMDA->SOM'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'VGLUT3'}, 
    'postConds': {'popLabel':'SOM'},  
    'weight': cfg.VGLUT3_SOM_NMDA,           
    'probability': 0.2,
    'sec': 'dend',
    'delay': 0.5, 
    'loc': 0.5,
    'synMech': 'NMDA'}

netParams.connParams['DOR_AMPA->SOM'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'DOR'}, 
    'postConds': {'popLabel':'SOM'},  
    'weight': cfg.DOR_AMPA,           
    'probability': 0.2,
    'sec': 'dend',
    'delay': 0.5, 
    'loc': 0.5,
    'synMech': 'AMPA'}

netParams.connParams['DOR_NMDA->SOM'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'DOR'}, 
    'postConds': {'popLabel':'SOM'},  
    'weight': cfg.DOR_NMDA,           
    'probability': 0.2,
    'sec': 'dend',
    'delay': 0.5, 
    'loc': 0.5,
    'synMech': 'NMDA'}

# TO eCR NEURONS
netParams.connParams['DYN_GABA->CR'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'DYN'}, 
    'postConds': {'popLabel':'CR'},  
    'weight': cfg.DYN_EX_GABA,           
    'probability': 0.2,
    'sec': 'dend',
    'delay': 0.5, 
    'loc': 0.5,
    'synMech': 'GABA'}

netParams.connParams['DYN_GLY->CR'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'DYN'}, 
    'postConds': {'popLabel':'CR'},  
    'weight': cfg.DYN_EX_GLY,           
    'probability': 0.2,
    'sec': 'dend',
    'delay': 0.5, 
    'loc': 0.5,
    'synMech': 'GLY'}

netParams.connParams['TrC_AMPA->CR'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'TrC'}, 
    'postConds': {'popLabel':'CR'},  
    'weight': cfg.TrC_AMPA,           
    'probability': 0.2,
    'sec': 'dend',
    'delay': 0.5, 
    'loc': 0.5,
    'synMech': 'AMPA'}

netParams.connParams['TrC_NMDA->CR'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'TrC'}, 
    'postConds': {'popLabel':'CR'},  
    'weight': cfg.TrC_NMDA,           
    'probability': 0.2,
    'sec': 'dend',
    'delay': 0.5, 
    'loc': 0.5,
    'synMech': 'NMDA'}

netParams.connParams['PKC_AMPA->CR'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'PKC'}, 
    'postConds': {'popLabel':'CR'},  
    'weight': cfg.PKC_AMPA,           
    'probability': 0.2,
    'sec': 'dend',
    'delay': 0.5, 
    'loc': 0.5,
    'synMech': 'AMPA'}

netParams.connParams['PKC_NMDA->CR'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'PKC'}, 
    'postConds': {'popLabel':'CR'},  
    'weight': cfg.PKC_NMDA,           
    'probability': 0.2,
    'sec': 'dend',
    'delay': 0.5, 
    'loc': 0.5,
    'synMech': 'NMDA'}

netParams.connParams['DOR_AMPA->CR'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'DOR'}, 
    'postConds': {'popLabel':'CR'},  
    'weight': cfg.DOR_AMPA,           
    'probability': 0.2,
    'sec': 'dend',
    'delay': 0.5, 
    'loc': 0.5,
    'synMech': 'AMPA'}

netParams.connParams['DOR_NMDA->CR'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'DOR'}, 
    'postConds': {'popLabel':'CR'},  
    'weight': cfg.DOR_NMDA,           
    'probability': 0.2,
    'sec': 'dend',
    'delay': 0.5, 
    'loc': 0.5,
    'synMech': 'NMDA'}

# TO pNK1 NEURONS
netParams.connParams['SOM_AMPA->NK1'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'SOM'}, 
    'postConds': {'popLabel': 'NK1'},  
    'weight': cfg.EX_NK1_AMPA,
    'probability': 0.2,
    'sec': 'dend',
    'delay': 0.5, 
    'loc': 0.5,
    'synMech': 'AMPA'}

netParams.connParams['SOM_NMDA->NK1'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'SOM'}, 
    'postConds': {'popLabel': 'NK1'},  
    'weight': cfg.EX_NK1_NMDA,
    'probability': 0.2,
    'sec': 'dend',
    'delay': 0.5, 
    'loc': 0.5,
    'synMech': 'NMDA'}

netParams.connParams['SOM_NK1->NK1'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'SOM'}, 
    'postConds': {'popLabel': 'NK1'},  
    'weight': cfg.EX_NK1_NK1,
    'probability': 0.2,
    'sec': 'dend',
    'delay': 0.5, 
    'loc': 0.5,
    'synMech': 'NK13'}

netParams.connParams['CR_AMPA->NK1'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'CR'}, 
    'postConds': {'popLabel': 'NK1'},  
    'weight': cfg.EX_NK1_AMPA,
    'probability': 0.2,
    'sec': 'dend',
    'delay': 0.5, 
    'loc': 0.5,
    'synMech': 'AMPA'}

netParams.connParams['CR_NMDA->NK1'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'CR'}, 
    'postConds': {'popLabel': 'NK1'},  
    'weight': cfg.EX_NK1_NMDA,
    'probability': 0.2,
    'sec': 'dend',
    'delay': 0.5, 
    'loc': 0.5,
    'synMech': 'NMDA'}

netParams.connParams['CR_NK1->NK1'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'CR'}, 
    'postConds': {'popLabel': 'NK1'},  
    'weight': cfg.EX_NK1_NK1,
    'probability': 0.2,
    'sec': 'dend',
    'delay': 0.5, 
    'loc': 0.5,
    'synMech': 'NK13'}

netParams.connParams['DYN_GABA->NK1'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'DYN'}, 
    'postConds': {'popLabel': 'NK1'},  
    'weight': cfg.DYN_NK1_GABA,
    'probability': 0.2,
    'sec': 'dend',
    'delay': 0.5, 
    'loc': 0.5,
    'synMech': 'GABA'}

netParams.connParams['DYN_GLY->NK1'] = {
    'oneSynPerNetcon': True,
    'preConds': {'popLabel': 'DYN'}, 
    'postConds': {'popLabel': 'NK1'},  
    'weight': cfg.DYN_NK1_GLY,
    'probability': 0.2,
    'sec': 'dend',
    'delay': 0.5, 
    'loc': 0.5,
    'synMech': 'GLY'}

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