An unsuspected physiological role for mGluRIII glutamate receptors in hippocampal area CA1.

1. # %%
2. # Created by Namit Dwivedi (Scimemi Lab) on 09/19/2024
3. # Please contact Namit ([email hidden]) or Dr Annalisa Scimemi ([email hidden]) for questions
4. # The goal of this code is to identify the number of active inhibitory synaptic inputs in DHK that allow us to reproduce the amplitude of oIPSCs from PV-INs recorded experimentally
7. import numpy as np
8. import matplotlib.pyplot as plt
9. from neuron import h
10. import pandas as pd
11. import random
13. # Save the average soma current to a CSV file
14. output_file_path = 'C:/Users/ND653384/Documents/Namit/CSV file/nrnez_2024_10_08_11_13_44/PV_dhk108.csv'
16. # Function to calculate distance from soma
17. def calculate_distance(section):
18. h.distance(sec=h.soma[0]) # Set soma as reference
19. return h.distance(0.5, sec=section)
21. # Function to run a single simulation and return the somatic current
22. def run_simulation():
23. # Load morphology and hoc files
24. h.load_file('C:/Users/ND653384/Documents/Namit/PV/nrnez_2024_09_12_16_49_35/run_1/CA1PC.nrn')
26. # Define file path for the NRN-EZ location file
27. syn_loc_file = 'C:/Users/ND653384/Documents/Namit/PV/nrnez_2024_09_12_16_49_35/run_1/Inhibitory/syn_loc_dhk.dat'
28. #syn_loc_dhk.dat is a .dat file created once the active number of synapses are known with reduced total synapses (from 40 to 30) to keep release probability constant
30. # Read synapse location data
31. with open(syn_loc_file, 'r') as file:
32. synapse_data = [line.strip().split() for line in file.readlines()]
35. selected_synapses = random.sample(synapse_data, 3)
37. soma = h.soma[0]
38. axon = h.axon
40. # Create empty lists
41. recorders = [] # Store the voltage recordings from the selected synapses
42. Rec_id = [] # Store the identifiers of the sections (e.g., soma, apical) where the recorders are placed
43. sections = [] # Store the actual sections (compartments) of the selected synapses
44. distances = [] # Store the calculated distances of the synapses from the soma
45. h.dt= 0.025
47. # Set axial resistance for all sections
48. for sec in h.allsec():
49. sec.Ra = 150 # ohm.cm
51. # Introduce leak channels to all sections
52. for sec in h.allsec():
53. sec.insert('pas') # Insert leak channel
54. distance = calculate_distance(sec)
55. sec.g_pas = 1e-5 * np.exp(distance / 100) # Set leak conductance (S/cm2)
56. sec.e_pas = 0 # Set leak reversal potential
58. soma_mechs = [mech.name() for seg in soma for mech in seg]
59. print(f"Soma mechanisms: {soma_mechs}")
61. # Iterate through the selected synapse data
62. for section_type, section_number in selected_synapses:
63. section_type = int(section_type)
64. section_number = int(section_number)
66. if section_type == 0:
67. section = h.soma[section_number]
68. elif section_type == 3:
69. section = h.apical[section_number]
70. else:
71. continue
73. # Calculate and store distance
74. distance = calculate_distance(section)
75. if distance <= 100: # Only include sections at <50 um in radial distance from the soma and <100 um in geodesic distance from the soma
76. distances.append(distance)
78. # Record membrane potential
79. rec = h.Vector()
80. rec.record(section(0.5)._ref_v)
81. recorders.append(rec)
82. sections.append(section)
83. Rec_id.append(section_number if section_type == 3 else "soma")
85. # Print distances for each selected section
86. for idx, distance in enumerate(distances):
87. section_type = "Soma" if Rec_id[idx] == "soma" else "Apical"
88. print(f"Section {section_type} {Rec_id[idx]}: Distance from soma = {distance:.2f} µm")
90. # Access the soma
91. soma_rec = h.Vector()
92. soma_rec.record(h.soma[0](0.5)._ref_v)
94. # Create SEClamp
95. seclamp1 = h.SEClamp(0.5, sec=soma)
96. seclamp1.dur1 = 1e9
97. seclamp1.amp1 = 0
98. seclamp1.rs = 12
100. t = h.Vector().record(h._ref_t)
102. # Run simulation
103. h.load_file("stdrun.hoc")
104. h.finitialize(0)
105. h.continuerun(300)
107. # Parameters for the inhibitory synapses
108. syn_tau1 = 3 # Rise time constant in ms
109. syn_tau2 = 24 # Decay time constant in ms
110. syn_e = -70 # GABA reversal potential
112. # List to hold synapses and netstims
113. synapses = [] # Store the inhibitory synapses created for each selected section
114. netstims = [] # Store the NetStims used to trigger the synaptic events
115. netcons = [] # Store the NetCons (connections between NetStim and synapse)
116. syn_voltage = [] # Store the voltage recordings of the synapse for each section
117. syn_currents = [] # Store the synaptic current values recorded for each synapse
119. # Release probability
120. release_probability = 0.1
122. for section in sections:
123. syn = h.Exp2Syn(0.5, sec=section)
124. syn.tau1 = syn_tau1
125. syn.tau2 = syn_tau2
126. syn.e = syn_e
128. # Create a NetStim to activate the synapse
129. stim = h.NetStim()
130. stim.number = 1 # Number of spikes
131. stim.start = 100 # Start time in ms
132. stim.interval = 0 # Interval between spikes in ms
134. # Connect the NetStim to the synapse
135. nc = h.NetCon(stim, syn)
136. nc.weight[0] = 0.485e-3 # Synaptic weight in uS
138. # Record the synaptic current
139. syn_current = h.Vector()
140. syn_current.record(syn._ref_i)
142. synapses.append(syn)
143. netstims.append(stim)
144. netcons.append(nc)
145. syn_currents.append(syn_current)
146. syn_voltage.append(rec)
148. h.finitialize(0)
149. h.continuerun(300)
151. # Record the current at soma
152. soma_current = h.Vector()
153. soma_current.record(seclamp1._ref_i)
155. h.finitialize(0)
156. h.continuerun(300)
158. return soma_current, t
160. # Run simulation 100 times and collect results
161. soma_currents = []
162. times = None
164. for i in range(100):
165. soma_current, t = run_simulation()
166. soma_currents.append(np.array(soma_current))
167. if times is None:
168. times = np.array(t)
170. # Calculate average soma current
171. average_soma_current = np.mean(soma_currents, axis=0) * 1000 # Convert to pA
173. # Calculate the rise time of the soma current recorded wave
174. peak_index = np.argmax(average_soma_current)
175. peak_value = average_soma_current[peak_index]
177. # Find 20% and 80% of the peak value
178. threshold_20 = 0.2 * peak_value
179. threshold_80 = 0.8 * peak_value
181. # Find indices where the current crosses these thresholds
182. crossing_20 = np.where(average_soma_current[:peak_index] >= threshold_20)[0][0]
183. crossing_80 = np.where(average_soma_current[:peak_index] >= threshold_80)[0][0]
185. rise_time_20_80 = times[crossing_80] - times[crossing_20]
187. # Calculate half-decay time (t50)
188. half_value = 0.5 * peak_value
189. crossing_50 = np.where(average_soma_current[peak_index:] <= half_value)[0][0]
190. half_decay_time = times[peak_index + crossing_50] - times[peak_index]
193. df = pd.DataFrame({
194. 'Time (ms)': times,
195. 'Average Somatic Current (pA)': average_soma_current
196. })
197. df.to_csv(output_file_path, index=False)
199. # Plot average somatic current
200. plt.figure(figsize=(10, 6))
201. plt.plot(times, average_soma_current, label='Average Somatic Current', color='red')
202. plt.xlabel('Time (ms)')
203. plt.ylabel('Current (pA)')
204. plt.title('Average Somatic Inhibitory Postsynaptic Current')
205. plt.legend()
206. plt.show()
209. # %%
210. print(max(average_soma_current))
211. print(rise_time_20_80)
212. print(half_decay_time)
214. #Release probability= No.of active synapses/Total SST synapses
215. # Release_probability= 5/50= 0.1
217. #Values for oIPSC recorded experimentally at soma for PV_DHK (Figure 5)
218. #Amplitude: 51.1
219. #Rise_time: 3.3
220. #Half_decay_time: 21.1

PV_DHK.ipynb at commit ef01e61, under GPL-3.0 · at the source