"""tonic_GABAAR.py examines the effects of a tonic GABAAR channel in a modified Iascone et al 2020 model""" from solo_ap import * # turns off bAP stim once AP detected from run_simulation_python3 import * # modified Iascone et al model to # have no synaptic activity and # not run so can modify further # and run in this file from g_scale import * # includes g_piecewise_linear import matplotlib.pyplot as plt from copy import deepcopy from pathlib import Path from write_vec import * plt.ion() # interactive plot mode on from setup_tGARs import * # import many functions and setup recordings ############# # # retrieve data from run with blocked tonic GABAAR # ############# # simulation data from a run where tGARs were blocked: blocked_file_path = 'data/blocked_tGAR_data.pkl' use_file_path = blocked_file_path # using a pickle that contains blocked and/or control (possibly levels of) tGARs with open(use_file_path, 'rb') as f: h.tstop = pickle.load(f) session_record.update(pickle.load(f)) # if these are assigned instead of # update's then the scope of these dict's are lost in integrated_charge() ica_lva_session_record.update(pickle.load(f)) ica_hva_session_record.update(pickle.load(f)) cai_session_record.update(pickle.load(f)) # g_lva_session_record.update(pickle.load(f)) g_hva_session_record.update(pickle.load(f)) m_lva_session_record.update(pickle.load(f)) h_lva_session_record.update(pickle.load(f)) m_hva_session_record.update(pickle.load(f)) h_hva_session_record.update(pickle.load(f)) # pre_bAP_index = pickle.load(f) blocked_soma = pickle.load(f) blocked_soma_vec = pickle.load(f) record_distances = pickle.load(f) blocked_dend_preAP_v = pickle.load(f) ############# # # run with control tonic GABAAR # ############# # already defined in setup_tGARs.py # start_loc=200 # 200 250 300 # end_loc=378.5 # most distal location is 378.46965982624585 um from soma(0.5) plt.figure() # sample gbar_exGABALeak values previously studied in model in # constant and distal distributions [0.5e-4, 1e-4, 2e-4, 3e-4, 4e-4, # 10e-4] from tonic_GABAAR_gbar import * # tonic_GABAAR_gbar, control() sets tGAR gbars control() # depol_iclamp.amp = depol_iv_pairs_dict['control'][amp_index][0] h.v_init=depol_iv_pairs_dict['control'][amp_index][1] # -66 my_run(400) ############# # # store control tonic GABAAR (tGAR) pre bAP V's # ############# # The control(s) is/are always compared to a blocked case (0 pS/um2 # tGAR (gbar_exGABALeak)) control_soma=[soma_voltageVector[pre_bAP_index]] # list of control # soma voltage delta_bAP_V_soma=control_soma[0]-blocked_soma[0] control_soma_vec = deepcopy(soma_voltageVector) # store more general records results of this control tonic GABAAR results control_dend_preAP_v = {} # keys are record distances, values are # lists of pre_bAP voltages at those locations for location in record_distances: control_dend_preAP_v[location]= [v_trace.x[pre_bAP_index] for v_trace in [v_traces for v_traces in deepcopy(dendrite_records[location])]] delta_bAP_V_dend = {} # keys are record locations, values are # lists of delta V's at those locations for location in record_distances: delta_bAP_V_dend[location]= np.array(control_dend_preAP_v[location]) - \ np.array(blocked_dend_preAP_v[location]) data= \ [f"{np.mean(delta_bAP_V_dend[x]):.3f}+-{np.std(delta_bAP_V_dend[x]):.3f}" for x in delta_bAP_V_dend] data_string = ', '.join(data) # for plotting like # https://stackoverflow.com/questions/22481854/plot-mean-and-standard-deviation x =record_distances y = [np.mean(delta_bAP_V_dend[x]) for x in delta_bAP_V_dend] e = [np.std(delta_bAP_V_dend[x]) for x in delta_bAP_V_dend] if distribution=='distal': plt.errorbar(x, y, yerr=e, fmt='-o', label=f"distal") else: plt.errorbar(x, y, yerr=e, fmt='-o', label=f"constant") ############# # # store control(s) tonic GABAAR V's, ica's, cai's # ############# tGAR_status='control' session_record[tGAR_status]=deepcopy(dendrite_records) ica_lva_session_record[tGAR_status]=deepcopy(ica_lva_records) ica_hva_session_record[tGAR_status]=deepcopy(ica_hva_records) cai_session_record[tGAR_status]=deepcopy(cai_records) g_lva_session_record[tGAR_status]=deepcopy(g_lva_records) g_hva_session_record[tGAR_status]=deepcopy(g_hva_records) m_lva_session_record[tGAR_status]=deepcopy(m_lva_records) h_lva_session_record[tGAR_status]=deepcopy(h_lva_records) m_hva_session_record[tGAR_status]=deepcopy(m_hva_records) h_hva_session_record[tGAR_status]=deepcopy(h_hva_records) soma_v_session_record[tGAR_status]=deepcopy(soma_voltageVector) soma_AP_t_session_record[tGAR_status]=deepcopy(AP_t_vec) bAP_stimulation_session_record[tGAR_status]=deepcopy(bAP_stimulation_record) print(f"{distribution} {tGAR_label}, " + data_string) tmp=plt.title( f'delta V between blocked and {distribution} control tonic GABAAR') tmp=plt.ylabel('pre bAP delta V (mV)') tmp=plt.xlabel('dendritic distance from the soma, no SK') tmp=plt.legend(loc='best') tmp=plt.close() # not interested in this figure # both_tgAR_data.pkl contains both control and blocked simulation data with open('data/both_tGAR_data.pkl', 'wb') as f: pickle.dump(h.tstop, f) pickle.dump(session_record,f) pickle.dump(ica_lva_session_record, f) pickle.dump(ica_hva_session_record, f) pickle.dump(cai_session_record, f) # pickle.dump(g_lva_session_record, f) pickle.dump(g_hva_session_record, f) pickle.dump(m_lva_session_record, f) pickle.dump(h_lva_session_record, f) pickle.dump(m_hva_session_record, f) pickle.dump(h_hva_session_record, f) # pickle.dump(pre_bAP_index, f) pickle.dump(blocked_soma, f) pickle.dump(blocked_soma_vec, f) pickle.dump(record_distances, f) pickle.dump(blocked_dend_preAP_v, f) # pickle.dump(control_soma, f) pickle.dump(delta_bAP_V_soma, f) pickle.dump(control_soma_vec, f) pickle.dump(control_dend_preAP_v, f) pickle.dump(delta_bAP_V_dend, f) pickle.dump(data_string, f) pickle.dump(distribution, f)