Vertical System (VS) tangential cells network model (Trousdale et al. 2014)

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Network model of the VS tangential cell system, with 10 cells per hemisphere. Each cell is a two compartment model with one compartment for dendrites and one for the axon. The cells are coupled through axonal gap junctions. The code allows to simulate responses of the VS network to a variety of visual stimuli to investigate coding as a function of gap junction strength.
1 . Trousdale J, Carroll SR, Gabbiani F, Josic K (2014) Near-optimal decoding of transient stimuli from coupled neuronal subpopulations. J Neurosci 34:12206-22 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Axon; Synapse; Dendrite;
Brain Region(s)/Organism:
Cell Type(s): Fly vertical system tangential cell;
Gap Junctions: Gap junctions;
Receptor(s): Nicotinic; GabaA;
Transmitter(s): Acetylcholine; Gaba;
Simulation Environment: Python;
Model Concept(s): Activity Patterns; Spatio-temporal Activity Patterns; Simplified Models; Invertebrate; Connectivity matrix;
Implementer(s): Gabbiani, F; Trousdale, James [jamest212 at];
Search NeuronDB for information about:  Nicotinic; GabaA; Acetylcholine; Gaba;
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.backends.backend_pdf import PdfPages
import numpy as np
from sim_vs_net import sim_vs_net

run_time_vars = {'g_gap':1000, # nS
                     'g_inh_scale':0.06, # Factor which multiplies g_gap to give g_inh
                     'g_L_dend':180, # nS
                     'g_L_axon':30, # nS
                     'g_axon_dend':110, #nS
                     'g_exc_in':10000, # nS
                     'g_inh_in':15000, # nS
                     'E_E':60, # mV
                     'E_I':-40, # mV,
                     'tau_m':1.4, # ms
                     'noise_std':6, #10**2.5, # pA
                     'tau_hp':50, # ms
                     'tau_lp':20, # ms
                     'dt_sim':0.01, # ms
                     'dt_im':1, # ms
                     'deg_per_ms': 0.125,
run_time_vars['image'] = {'type':'stripe','x_pixels':360,'y_pixels':180,'strip_rad':5,'freq_s':8}
T = run_time_vars['T']
t = np.linspace(0,T,T/run_time_vars['dt_sim'],endpoint=False)

nRotAngles = 361 #for rapid debugging and/or visualization use 3 or 37 instead of 361
rot_angles = np.linspace(-180,180,nRotAngles)
v_r_ss_ang = np.zeros([nRotAngles,10])
v_l_ss_ang = np.zeros([nRotAngles,10])

for cAng_ind in range(rot_angles.size):
    run_time_vars['rot_angle'] = rot_angles[cAng_ind]
    (V_L,V_R) = sim_vs_net(run_time_vars)
    #uncomment to see how individual responses look like over time
    #fig1 = plt.figure()
    #ax1 = fig1.add_subplot(121)
    #ax2 = fig1.add_subplot(122)
    #for i in range(10):
    #    ax1.plot(t,V_L[i,:].T,label='VS'+str(i+1))
    #    ax2.plot(t,V_R[i,:].T)
    #ax1.set_title('Left side responses - rot: ' + str(rot_angles[cAng_ind]))
    #ax2.set_title('Right side responses')
    #disp_lim = np.max([np.max(np.abs(V_L)),np.max(np.abs(V_R))])*1.1

    #average over the last 10 ms of stimulation to get steady state responses
    v_r_ss_ang[cAng_ind,:] = np.mean(V_R[:,10000-1000:10000],axis=1)
    v_l_ss_ang[cAng_ind,:] = np.mean(V_L[:,10000-1000:10000],axis=1)

#plot the tuning curves for all VS model cells
fig2 = plt.figure()
ax1f2 = fig2.add_subplot(111, projection='3d')
for vsInd in range(10):

#uncomment to save as pdf
#with PdfPages('vs_stripe_tuning.pdf') as pdf:
#    pdf.savefig(fig2)