Escape response latency in the Giant Fiber System of Drosophila melanogastor (Augustin et al 2019)

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Accession:245415
"The Giant Fiber System (GFS) is a multi-component neuronal pathway mediating rapid escape response in the adult fruit-fly Drosophila melanogaster, usually in the face of a threatening visual stimulus. Two branches of the circuit promote the response by stimulating an escape jump followed by flight initiation. Our recent work demonstrated an age-associated decline in the speed of signal propagation through the circuit, measured as the stimulus-to-muscle depolarization response latency. The decline is likely due to the diminishing number of inter-neuronal gap junctions in the GFS of ageing flies. In this work, we presented a realistic conductance-based, computational model of the GFS that recapitulates our experimental results and identifies some of the critical anatomical and physiological components governing the circuit's response latency. According to our model, anatomical properties of the GFS neurons have a stronger impact on the transmission than neuronal membrane conductance densities. The model provides testable predictions for the effect of experimental interventions on the circuit's performance in young and ageing flies."
Reference:
1 . Augustin H, Zylbertal A, Partridge L (2019) A computational model of the escape response latency in the Giant Fiber System of Drosophila melanogaster eNeuro
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Drosophila;
Cell Type(s):
Channel(s): I Na,t; I Na,p; I K;
Gap Junctions: Gap junctions;
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Invertebrate; Delay;
Implementer(s): Zylbertal, Asaph [asaph.zylbertal at mail.huji.ac.il]; Augustin, Hrvoje ;
Search NeuronDB for information about:  I Na,p; I Na,t; I K;
/
AugustinEtAl2018
channels
readme
gfpn.py
gfs_param_scan_conductances.py
                            
"""
(C) Asaph Zylbertal 2017, HUJI, Jerusalem, Israel

GFS latency as a function of gap junction conductance and 1) voltage gated sodium conductance, 2) voltage gated potassium conductance
3) leak conductance

****************

"""

import neuron
import gfpn
import numpy as np
import matplotlib.pyplot as plt
import matplotlib

matplotlib.rcParams['svg.fonttype'] = 'none'

neuron.load_mechanisms('./channels')
           
cv=neuron.h.CVode()
cv.active(1)

g = gfpn.gfs()
g.wire_cells()

ranges={}
ranges['g_gap'] = np.arange(20, 160, 10)
ranges['gnatbar'] = np.arange(230e-3, 530e-3, 20e-3)
ranges['gkbar'] = np.arange(1e-3, 25e-3, 1e-3)
ranges['gleak'] = np.arange(0, 100e-6, 5e-6)
ranges['TTMn_lat_L'] = np.arange(0.1, 45, 3)
ranges['TTMn_med_L'] = np.arange(0.1, 80, 3)
ranges['TTMn_syn_post_loc'] = np.arange(0, 1, 0.1)
ranges['TTMn_diam'] = np.arange(0.5, 10, 0.5)


original_ttmn_delay = 0.926
original_dlmn_delay = 1.44
old_g_gap = 34.5
ttmn_margin = [original_ttmn_delay * 0.99, original_ttmn_delay * 1.01]
dlmn_margin = [original_dlmn_delay * 0.99, original_dlmn_delay * 1.01]

max_ttmn = [1.12, 1.12, 1.12]
max_dlmn = [1.75, 2, 1.75]

param1 = 'g_gap'
param2s = ['gnatbar', 'gkbar', 'gleak']
fig1 = plt.figure()

ii = 1
for p2 in param2s:
    plt.subplot(2, 3, ii)
    param2 = p2
    
    x_young = g.params[param2]
    y_young = g.params[param1]
    y_old = old_g_gap
    
    x_young = (double(len(ranges[param2])) - 1.) * (x_young - ranges[param2][0]) / (ranges[param2][-1] - ranges[param2][0])
    y_young = (double(len(ranges[param1])) - 1.) * (y_young - ranges[param1][0]) / (ranges[param1][-1] - ranges[param1][0])
    y_old = (double(len(ranges[param1])) - 1.) * (y_old - ranges[param1][0]) / (ranges[param1][-1] - ranges[param1][0])
    
    
    delay_dict = g.param_mesh(param1, ranges[param1], param2, ranges[param2])
    delay_dict['DLMn_delays'][delay_dict['DLMn_delays']==-1] = nan
    delay_dict['TTMn_delays'][delay_dict['TTMn_delays']==-1] = nan
    
    
    tcks = np.linspace(np.min(delay_dict['TTMn_delays']), np.max(delay_dict['TTMn_delays']), 20)
    tck_lbls = tcks
    f1 = plt.gca()
    f1.set_xticks(range(0, len(ranges[param2]), 5))
    f1.set_yticks(range(0, len(ranges[param1]), 5))
    f1.set_xticklabels(ranges[param2][::5]*1e3)
    f1.set_yticklabels(ranges[param1][::5])
    plt.xlabel(param2 + ' (mS/cm^2)')
    plt.ylabel(param1 + ' (uS)')
    CS = plt.contour(np.log(delay_dict['TTMn_delays']), 25)
    CS.levels = np.exp(CS.levels)
    CS.levels = CS.levels[CS.levels<max_ttmn[ii-1]]
    plt.clabel(CS, inline=1, inline_spacing=1, use_clabeltext=True, fontsize=9)
   
    plt.contour(delay_dict['TTMn_delays'], levels = ttmn_margin, colors='r', linestyles='dashed')
    
    
    plt.scatter([x_young], [y_young])
    plt.scatter([x_young], [y_old])
    
    plt.subplot(2, 3, ii + 3)
    tcks = np.linspace(np.min(delay_dict['DLMn_delays']), np.max(delay_dict['DLMn_delays']), 20)
    tck_lbls = tcks
    f2 = plt.gca()
    f2.set_xticks(range(0, len(ranges[param2]), 5))
    f2.set_yticks(range(0, len(ranges[param1]), 5))
    f2.set_xticklabels(ranges[param2][::5]*1e3)
    f2.set_yticklabels(ranges[param1][::5])
    plt.xlabel(param2 + ' (mS/cm^2)')
    plt.ylabel(param1 + ' (uS)')
    CS = plt.contour(np.log(delay_dict['DLMn_delays']), 25)
    CS.levels = np.exp(CS.levels)
    CS.levels = CS.levels[CS.levels<max_dlmn[ii-1]]
           
    plt.clabel(CS, inline=1, inline_spacing=1, use_clabeltext=True, fontsize=9)
    plt.contour(delay_dict['DLMn_delays'], levels = dlmn_margin, colors='r', linestyles='dashed')
    
    plt.scatter([x_young], [y_young])
    plt.scatter([x_young], [y_old])

    ii += 1

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