Look-Up Table Synapse (LUTsyn) models for AMPA and NMDA (Pham et al., 2021)

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Accession:267103
Fast input-output synapse model of glutamatergic receptors AMPA and NMDA that can capture nonlinear interactions via look-up table abstraction. Speeds are comparable to 'linear' exponential synapses. Download LUT files at: https://senselab.med.yale.edu/modeldb/data/267103/LUTs.zip
Reference:
1 . Pham DJ, Yu GJ, Bouteiller JC, Berger TW (2021) Bridging Hierarchies in Multi-Scale Models of Neural Systems: Look-Up Tables Enable Computationally Efficient Simulations of Non-linear Synaptic Dynamics Front. Comput. Neurosci.
Model Information (Click on a link to find other models with that property)
Model Type: Channel/Receptor; Synapse;
Brain Region(s)/Organism: Dentate gyrus;
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s): AMPA; NMDA;
Gene(s):
Transmitter(s): Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Multiscale;
Implementer(s):
Search NeuronDB for information about:  AMPA; NMDA; Glutamate;
# LUTsyn_example_main.py
# Code by Duy-Tan Jonathan Pham (duytanph@usc.edu)
# July 8, 2021

# This file is a Python script for demonstrating the LUTsyn synapse models described in
# (Pham, 2021). This file executes multiple NEURON simulations that compare the LUTsyn
# model to other synapse models and plots the results. Both AMPA and NMDA cases are
# simulated. This script has been tested using Python (v3.5) and NEURON (v7.6.7).
#
# NOTE: Please be sure to run 'nrnivmodl' in terminal/command prompt in your working
# directory that contains the .mod files before running this script.

######################################################################################
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# California. All Rights Reserved.
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#
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from LUTsyn_example_functions import *
import matplotlib.pyplot as plt
############################################# MAIN #############################################

# possible synapse models: 'LUTsyn_AMPA', 'E2_AMPA', 'Kinetic_AMPA'
#                          'LUTsyn_NMDA', 'E3_NMDA', 'Kinetic_NMDA'

# set stimulation parameters
freq = 10
stim_params = {}
stim_params['sim_time'] = 20000 # 20 second simulation
stim_params['freq'] = freq      # 10 Hz mean input firing rate
stim_params['tau_AHP'] = 0.035  # Poisson process parameter (seconds)

# run AMPA simulations
sim_LUTsyn_AMPA = nrn_sim(stim_params,'AMPA','LUTsyn_AMPA')  # LUTsyn AMPA
sim_E2 = nrn_sim(stim_params,'AMPA','E2_AMPA')               # double exponential AMPA
sim_kinetic_AMPA = nrn_sim(stim_params,'AMPA','Kinetic_AMPA')# Kinetic AMPA

# run NMDA simulations
sim_LUTsyn_NMDA = nrn_sim(stim_params,'NMDA','LUTsyn_NMDA')  # LUTsyn NMDA
sim_E3 = nrn_sim(stim_params,'NMDA','E3_NMDA')               # triple exponential NMDA
sim_kinetic_NMDA = nrn_sim(stim_params,'NMDA','Kinetic_NMDA')# Kinetic NMDA

# Calculate NRMSE values (round to 4 decimal spots)
NRMSE_LUTsyn_AMPA = round(calc_NRMSE(sim_kinetic_AMPA['g'], sim_LUTsyn_AMPA['g'] ), 4)
NRMSE_E2_AMPA = round(calc_NRMSE(sim_kinetic_AMPA['g'], sim_E2['g'] ), 4)

NRMSE_LUTsyn_NMDA = round(calc_NRMSE(sim_kinetic_NMDA['osp'], sim_LUTsyn_NMDA['osp'] ), 4)
NRMSE_E3_NMDA = round(calc_NRMSE(sim_kinetic_NMDA['osp'], sim_E3['osp'] ), 4)

#### PLOTTING ####
t = sim_LUTsyn_AMPA['t']

# AMPA plot comparison
plt.figure(1)
# multiply by 1000 to convert from nS to pS
plt.plot(t, sim_kinetic_AMPA['g']*1e3, 'b-', label = 'Kinetic AMPA', alpha = 0.6)
plt.plot(t, sim_LUTsyn_AMPA['g']*1e3, 'g--', label = 'LUTsyn_AMPA, NRMSE: ' + str(NRMSE_LUTsyn_AMPA), alpha = 0.6)
plt.plot(t, sim_E2['g']*1e3, 'r:', label = 'Double Exponential, NRMSE: ' + str(NRMSE_E2_AMPA), alpha = 0.6)
plt.title('AMPA Comparison at ' + str(freq) + ' Hz')
plt.ylabel('Conductance (pS)')
plt.xlabel('Time (ms)')
plt.legend()

# NMDA plot comparison
plt.figure(2)
plt.plot(t, sim_kinetic_NMDA['osp'], 'b-', label = 'Kinetic NMDA', alpha = 0.6)
plt.plot(t, sim_LUTsyn_NMDA['osp'], 'g--', label = 'LUTsyn_NMDA, NRMSE: ' + str(NRMSE_LUTsyn_NMDA), alpha = 0.6)
plt.plot(t, sim_E3['osp'], 'r:', label = 'Triple Exponential, NRMSE: ' + str(NRMSE_E3_NMDA), alpha = 0.6)
plt.title('NMDA Comparison at ' + str(freq) + ' Hz')
plt.ylabel('Open-state probability')
plt.xlabel('Time (ms)')
plt.legend()

plt.show()

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