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  1. import os
  2. import nest
  3. from time import time
  4. from multiprocessing import cpu_count
  5.  
  6. from functions import Functions
  7. from functions import Parameters
  8.  
  9. import plot_results
  10.  
  11.  
  12. class V3(Functions):
  13. def __init__(self, parameters, iteration):
  14. """
  15. ToDo add info
  16. Args:
  17. parameters (Parameters):
  18. iteration (int):
  19. """
  20. nest.ResetKernel()
  21. nest.SetKernelStatus({'data_path': "dat",
  22. 'print_time': True,
  23. 'resolution': 0.025,
  24. 'overwrite_files': True,
  25. 'data_prefix': f"{iteration}_",
  26. 'total_num_virtual_procs': cpu_count(),
  27. 'rng_seeds': [int(time() * 10000 % 10000)] * cpu_count()})
  28.  
  29. super().__init__(parameters)
  30. self.P = parameters
  31.  
  32. self.init_network()
  33. self.simulate()
  34. self.resave()
  35.  
  36. def init_network(self):
  37. """
  38. TODO add info
  39. """
  40. inh_coef = 1
  41. quadru_coef = 0.5 if self.P.ped == 4 else 1
  42. sero_coef = 5.3 if self.P.ht5 else 1
  43.  
  44. syn_outdegree = 27 # synapse number outgoing from one neuron
  45. neurons_in_ip = 196 # number of neurons in interneuronal pool
  46. neurons_in_moto = 169 # motoneurons number
  47. neurons_in_group = 20 # number of neurons in a group
  48. neurons_in_aff_ip = 196 # number of neurons in interneuronal pool
  49. neurons_in_afferent = 120 # number of neurons in afferent
  50.  
  51. # groups of neurons
  52. EES = self.form_group("EES")
  53. E1 = self.form_group("E1")
  54. E2 = self.form_group("E2")
  55. E3 = self.form_group("E3")
  56. E4 = self.form_group("E4")
  57. E5 = self.form_group("E5")
  58.  
  59. CV1 = self.form_group("CV1", 40)
  60. CV2 = self.form_group("CV2", 40)
  61. CV3 = self.form_group("CV3", 40)
  62. CV4 = self.form_group("CV4", 40)
  63. CV5 = self.form_group("CV5", 40)
  64. CD4 = self.form_group("CD4", 40)
  65. CD5 = self.form_group("CD5", 40)
  66.  
  67. OM1_0 = self.form_group("OM1_0")
  68. OM1_1 = self.form_group("OM1_1")
  69. OM1_2_E = self.form_group("OM1_2_E")
  70. OM1_2_F = self.form_group("OM1_2_F")
  71. OM1_3 = self.form_group("OM1_3")
  72.  
  73. OM2_0 = self.form_group("OM2_0")
  74. OM2_1 = self.form_group("OM2_1")
  75. OM2_2_E = self.form_group("OM2_2_E")
  76. OM2_2_F = self.form_group("OM2_2_F")
  77. OM2_3 = self.form_group("OM2_3")
  78.  
  79. OM3_0 = self.form_group("OM3_0")
  80. OM3_1 = self.form_group("OM3_1")
  81. OM3_2_E = self.form_group("OM3_2_E")
  82. OM3_2_F = self.form_group("OM3_2_F")
  83. OM3_3 = self.form_group("OM3_3")
  84.  
  85. OM4_0 = self.form_group("OM4_0")
  86. OM4_1 = self.form_group("OM4_1")
  87. OM4_2_E = self.form_group("OM4_2_E")
  88. OM4_2_F = self.form_group("OM4_2_F")
  89. OM4_3 = self.form_group("OM4_3")
  90.  
  91. OM5_0 = self.form_group("OM5_0")
  92. OM5_1 = self.form_group("OM5_1")
  93. OM5_2_E = self.form_group("OM5_2_E")
  94. OM5_2_F = self.form_group("OM5_2_F")
  95. OM5_3 = self.form_group("OM5_3")
  96.  
  97. MN_E = self.form_group("MN_E", neurons_in_moto)
  98. MN_F = self.form_group("MN_F", neurons_in_moto)
  99.  
  100. Ia_E_aff = self.form_group("Ia_E_aff", neurons_in_afferent)
  101. Ia_F_aff = self.form_group("Ia_F_aff", neurons_in_afferent)
  102.  
  103. R_E = self.form_group("R_E")
  104. R_F = self.form_group("R_F")
  105.  
  106. Ia_E_pool = self.form_group("Ia_E_pool", neurons_in_aff_ip)
  107. Ia_F_pool = self.form_group("Ia_F_pool", neurons_in_aff_ip)
  108.  
  109. eIP_E = self.form_group("eIP_E", neurons_in_ip)
  110. eIP_F = self.form_group("eIP_F", neurons_in_ip)
  111.  
  112. iIP_E = self.form_group("iIP_E", neurons_in_ip)
  113. iIP_F = self.form_group("iIP_F", neurons_in_ip)
  114.  
  115. self.connect_spike_generator(EES, rate=self.P.EES)
  116. self.connect_noise_generator(CV1, rate=5000, t_end=self.P.skin_stim - 2)
  117. self.connect_noise_generator(CV2, rate=5000, t_start=self.P.skin_stim, t_end=2 * self.P.skin_stim - 2)
  118. self.connect_noise_generator(CV3, rate=5000, t_start=2 * self.P.skin_stim, t_end=3 * self.P.skin_stim - 2)
  119. self.connect_noise_generator(CV4, rate=5000, t_start=3 * self.P.skin_stim, t_end=5 * self.P.skin_stim - 2)
  120. self.connect_noise_generator(CV5, rate=5000, t_start=5 * self.P.skin_stim, t_end=6 * self.P.skin_stim - 2)
  121. self.connect_noise_generator(iIP_F, rate=3000, t_start=6 * self.P.skin_stim, t_end=11 * self.P.skin_stim - 5)
  122.  
  123. # connectomes
  124. self.connect_fixed_outdegree(EES, E1, 1, 370, no_distr=True)
  125. self.connect_fixed_outdegree(E1, E2, 1, 80, no_distr=True)
  126. self.connect_fixed_outdegree(E2, E3, 1, 80, no_distr=True)
  127. self.connect_fixed_outdegree(E3, E4, 1, 80, no_distr=True)
  128. self.connect_fixed_outdegree(E4, E5, 1, 80, no_distr=True)
  129.  
  130. self.connect_one_to_all(CV1, iIP_E, 0.5, 20)
  131. self.connect_one_to_all(CV2, iIP_E, 0.5, 20)
  132. self.connect_one_to_all(CV3, iIP_E, 0.5, 20)
  133. self.connect_one_to_all(CV4, iIP_E, 0.5, 20)
  134. self.connect_one_to_all(CV5, iIP_E, 0.5, 20)
  135.  
  136. #########################################################################
  137. # 1 and 2 slices are good with weights 14.4 and 7.9, and delays 1 and 1.75 (eIP_E -> MN_E: 3)
  138. #
  139. #########################################################################
  140. #
  141. #
  142. self.connect_fixed_outdegree(OM1_2_E, eIP_E, 1, 14.4, neurons_in_ip) # 1, 14.4
  143. self.connect_fixed_outdegree(OM2_2_E, eIP_E, 1.75, 7.9, neurons_in_ip) # 1.75, 7.9
  144. self.connect_fixed_outdegree(OM3_2_E, eIP_E, 1.85, 7.35, neurons_in_ip) # 1.85, 7.35
  145. self.connect_fixed_outdegree(OM4_2_E, eIP_E, 3, 9.65, neurons_in_ip)
  146. self.connect_fixed_outdegree(OM5_2_E, eIP_E, 3.5, 7.2, neurons_in_ip)
  147. #
  148. self.connect_fixed_outdegree(eIP_E, MN_E, 2.5, 3, neurons_in_moto)
  149. #
  150. self.connect_fixed_outdegree(MN_E, R_E, 0.7, 4)
  151. self.connect_fixed_outdegree(R_E, MN_E, 0.7, 0, neurons_in_moto)
  152. #
  153. #
  154. #########################################################################
  155. #########################################################################
  156.  
  157. # OM 1
  158. # input from EES group 1
  159. self.connect_fixed_outdegree(E1, OM1_0, 1, 20)
  160. # input from sensory
  161. self.connect_one_to_all(CV1, OM1_0, 0.5, 2 * quadru_coef * sero_coef)
  162. self.connect_one_to_all(CV2, OM1_0, 0.5, 2 * quadru_coef * sero_coef)
  163. # [inhibition]
  164. self.connect_one_to_all(CV3, OM1_3, 1, 80)
  165. self.connect_one_to_all(CV4, OM1_3, 1, 80)
  166. self.connect_one_to_all(CV5, OM1_3, 1, 80)
  167. # inner connectomes
  168. self.connect_fixed_outdegree(OM1_0, OM1_1, 1, 30)
  169. self.connect_fixed_outdegree(OM1_1, OM1_2_E, 1, 24)
  170. self.connect_fixed_outdegree(OM1_1, OM1_2_F, 0.4, 20)
  171. self.connect_fixed_outdegree(OM1_1, OM1_3, 3.5, 4)
  172. self.connect_fixed_outdegree(OM1_2_E, OM1_1, 2.5, 20)
  173. self.connect_fixed_outdegree(OM1_2_F, OM1_1, 2.5, 16)
  174. self.connect_fixed_outdegree(OM1_2_E, OM1_3, 1, 2)
  175. self.connect_fixed_outdegree(OM1_2_F, OM1_3, 0.4, 15.5)
  176. self.connect_fixed_outdegree(OM1_3, OM1_1, 1.5, -3 * inh_coef)
  177. self.connect_fixed_outdegree(OM1_3, OM1_2_E, 0.4, -60 * inh_coef)
  178. self.connect_fixed_outdegree(OM1_3, OM1_2_F, 1, -1 * inh_coef)
  179. # output to OM2
  180. self.connect_fixed_outdegree(OM1_2_F, OM2_2_F, 1, 50)
  181. # # output to IP
  182. self.connect_fixed_outdegree(OM1_2_F, eIP_F, 1, 30, neurons_in_ip)
  183. #
  184. # OM 2
  185. # input from EES group 2
  186. self.connect_fixed_outdegree(E2, OM2_0, 1, 7)
  187. # input from sensory [CV]
  188. self.connect_one_to_all(CV2, OM2_0, 0.5, 2 * quadru_coef * sero_coef)
  189. self.connect_one_to_all(CV3, OM2_0, 0.5, 2 * quadru_coef * sero_coef)
  190. # [inhibition]
  191. self.connect_one_to_all(CV4, OM2_3, 1, 80)
  192. self.connect_one_to_all(CV5, OM2_3, 1, 80)
  193. # # inner connectomes
  194. self.connect_fixed_outdegree(OM2_0, OM2_1, 1, 30)
  195. self.connect_fixed_outdegree(OM2_1, OM2_2_E, 1, 24)
  196. self.connect_fixed_outdegree(OM2_1, OM2_2_F, 0.4, 20)
  197. self.connect_fixed_outdegree(OM2_1, OM2_3, 3.5, 4)
  198. self.connect_fixed_outdegree(OM2_2_E, OM2_1, 2.5, 20)
  199. self.connect_fixed_outdegree(OM2_2_F, OM2_1, 2.5, 16)
  200. self.connect_fixed_outdegree(OM2_2_E, OM2_3, 1, 2)
  201. self.connect_fixed_outdegree(OM2_2_F, OM2_3, 0.4, 15.5)
  202. self.connect_fixed_outdegree(OM2_3, OM2_1, 1.5, -3 * inh_coef)
  203. self.connect_fixed_outdegree(OM2_3, OM2_2_E, 0.4, -60 * inh_coef)
  204. self.connect_fixed_outdegree(OM2_3, OM2_2_F, 1, -1 * inh_coef)
  205. # output to OM3
  206. self.connect_fixed_outdegree(OM2_2_F, OM3_2_F, 1, 50)
  207. # output to IP
  208. self.connect_fixed_outdegree(OM2_2_F, eIP_F, 2, 30, neurons_in_ip)
  209.  
  210. # OM 3
  211. # input from EES group 3
  212. self.connect_fixed_outdegree(E3, OM3_0, 1, 7)
  213. # input from sensory [CV]
  214. self.connect_one_to_all(CV3, OM3_0, 0.5, 2 * quadru_coef * sero_coef)
  215. self.connect_one_to_all(CV4, OM3_0, 0.5, 2 * quadru_coef * sero_coef)
  216. # [inhibition]
  217. self.connect_one_to_all(CV5, OM3_3, 1, 80)
  218. # input from sensory [CD]
  219. self.connect_one_to_all(CD4, OM3_0, 1, 11)
  220. # inner connectomes
  221. self.connect_fixed_outdegree(OM3_0, OM3_1, 1, 30)
  222. self.connect_fixed_outdegree(OM3_1, OM3_2_E, 1, 24)
  223. self.connect_fixed_outdegree(OM3_1, OM3_2_F, 0.4, 20)
  224. self.connect_fixed_outdegree(OM3_1, OM3_3, 3.5, 4)
  225. self.connect_fixed_outdegree(OM3_2_E, OM3_1, 2.5, 20)
  226. self.connect_fixed_outdegree(OM3_2_F, OM3_1, 2.5, 16)
  227. self.connect_fixed_outdegree(OM3_2_E, OM3_3, 1, 2)
  228. self.connect_fixed_outdegree(OM3_2_F, OM3_3, 0.4, 15.5)
  229. self.connect_fixed_outdegree(OM3_3, OM3_1, 1.5, -3 * inh_coef)
  230. self.connect_fixed_outdegree(OM3_3, OM3_2_E, 0.4, -60 * inh_coef)
  231. self.connect_fixed_outdegree(OM3_3, OM3_2_F, 1, -1 * inh_coef)
  232. # output to OM3
  233. self.connect_fixed_outdegree(OM3_2_F, OM4_2_F, 1, 50)
  234. # output to IP
  235. self.connect_fixed_outdegree(OM3_2_F, eIP_F, 3, 30, neurons_in_ip)
  236.  
  237. # OM 4
  238. # input from EES group 4
  239. self.connect_fixed_outdegree(E4, OM4_0, 1, 7)
  240. # input from sensory [CV]
  241. self.connect_one_to_all(CV4, OM4_0, 0.5, 2 * quadru_coef * sero_coef)
  242. self.connect_one_to_all(CV5, OM4_0, 0.5, 2 * quadru_coef * sero_coef)
  243. # input from sensory [CD]
  244. self.connect_one_to_all(CD4, OM4_0, 1, 11)
  245. self.connect_one_to_all(CD5, OM4_0, 1, 11)
  246. # inner connectomes
  247. self.connect_fixed_outdegree(OM4_0, OM4_1, 1, 30)
  248. self.connect_fixed_outdegree(OM4_1, OM4_2_E, 1, 23)
  249. self.connect_fixed_outdegree(OM4_1, OM4_2_F, 0.4, 20)
  250. self.connect_fixed_outdegree(OM4_1, OM4_3, 3.5, 4)
  251. self.connect_fixed_outdegree(OM4_2_E, OM4_1, 2.5, 18)
  252. self.connect_fixed_outdegree(OM4_2_F, OM4_1, 2.5, 16)
  253. self.connect_fixed_outdegree(OM4_2_E, OM4_3, 1, 2)
  254. self.connect_fixed_outdegree(OM4_2_F, OM4_3, 0.4, 15.5)
  255. self.connect_fixed_outdegree(OM4_3, OM4_1, 1.5, -3 * inh_coef)
  256. self.connect_fixed_outdegree(OM4_3, OM4_2_E, 0.4, -60 * inh_coef)
  257. self.connect_fixed_outdegree(OM4_3, OM4_2_F, 1, -1 * inh_coef)
  258. # output to OM4
  259. self.connect_fixed_outdegree(OM4_2_F, OM5_2_F, 1, 50)
  260. # output to IP
  261. self.connect_fixed_outdegree(OM4_2_F, eIP_F, 1, 30, neurons_in_ip)
  262.  
  263. # OM 5
  264. # input from EES group 5
  265. self.connect_fixed_outdegree(E5, OM5_0, 1, 7)
  266. # input from sensory [CV]
  267. self.connect_one_to_all(CV5, OM5_0, 0.5, 2 * quadru_coef * sero_coef)
  268. # input from sensory [CD]
  269. self.connect_one_to_all(CD5, OM5_0, 1, 11)
  270. # inner connectomes
  271. self.connect_fixed_outdegree(OM5_0, OM5_1, 1, 30)
  272. self.connect_fixed_outdegree(OM5_1, OM5_2_E, 1, 24)
  273. self.connect_fixed_outdegree(OM5_1, OM5_2_F, 0.4, 20)
  274. self.connect_fixed_outdegree(OM5_1, OM5_3, 3.5, 4)
  275. self.connect_fixed_outdegree(OM5_2_E, OM5_1, 2.5, 20)
  276. self.connect_fixed_outdegree(OM5_2_F, OM5_1, 2.5, 16)
  277. self.connect_fixed_outdegree(OM5_2_E, OM5_3, 1, 2)
  278. self.connect_fixed_outdegree(OM5_2_F, OM5_3, 0.4, 15.5)
  279. self.connect_fixed_outdegree(OM5_3, OM5_1, 1.5, -2 * inh_coef)
  280. self.connect_fixed_outdegree(OM5_3, OM5_2_E, 0.4, -40 * inh_coef)
  281. self.connect_fixed_outdegree(OM5_3, OM5_2_F, 1, -1 * inh_coef)
  282. # output to IP
  283. self.connect_fixed_outdegree(OM5_2_F, eIP_F, 3, 30, neurons_in_ip)
  284.  
  285. # reflex arc
  286. self.connect_fixed_outdegree(iIP_E, eIP_F, 0.5, -40, neurons_in_ip)
  287. self.connect_fixed_outdegree(iIP_F, eIP_E, 0.5, -40, neurons_in_ip)
  288.  
  289. self.connect_fixed_outdegree(iIP_E, OM1_2_F, 0.5, -500, neurons_in_ip)
  290. self.connect_fixed_outdegree(iIP_E, OM2_2_F, 0.5, -100, neurons_in_ip)
  291. self.connect_fixed_outdegree(iIP_E, OM3_2_F, 0.5, -100, neurons_in_ip)
  292. self.connect_fixed_outdegree(iIP_E, OM4_2_F, 0.5, -100, neurons_in_ip)
  293.  
  294. self.connect_fixed_outdegree(EES, Ia_E_aff, 1, 500)
  295. self.connect_fixed_outdegree(EES, Ia_F_aff, 1, 500)
  296.  
  297. self.connect_fixed_outdegree(eIP_F, MN_F, 1, 200, neurons_in_moto)
  298.  
  299. self.connect_fixed_outdegree(iIP_E, Ia_E_pool, 1, 30, neurons_in_ip)
  300. self.connect_fixed_outdegree(iIP_F, Ia_F_pool, 1, 30, neurons_in_ip)
  301.  
  302. self.connect_fixed_outdegree(Ia_E_pool, MN_F, 1, -150, neurons_in_ip)
  303. self.connect_fixed_outdegree(Ia_E_pool, Ia_F_pool, 1, -1, neurons_in_ip)
  304. self.connect_fixed_outdegree(Ia_F_pool, MN_E, 1, -10, neurons_in_ip)
  305. self.connect_fixed_outdegree(Ia_F_pool, Ia_E_pool, 1, -1, neurons_in_ip)
  306.  
  307. self.connect_fixed_outdegree(Ia_E_aff, MN_E, 2, 30, neurons_in_moto)
  308. self.connect_fixed_outdegree(Ia_F_aff, MN_F, 2, 22, neurons_in_moto)
  309.  
  310. self.connect_fixed_outdegree(MN_F, R_F, 0.5, 6)
  311.  
  312. self.connect_fixed_outdegree(R_E, R_F, 2, -1)
  313.  
  314. self.connect_fixed_outdegree(R_F, MN_F, 2, -50, neurons_in_moto)
  315. self.connect_fixed_outdegree(R_F, R_E, 2, -1)
  316.  
  317.  
  318. if __name__ == "__main__":
  319. parameters = Parameters()
  320. parameters.tests = 25
  321. parameters.steps = 1
  322. parameters.cms = 21
  323. parameters.EES = 40
  324. parameters.inh = 100
  325. parameters.ped = 2
  326. parameters.ht5 = False
  327. parameters.save_all = False
  328.  
  329. for i in range(parameters.tests):
  330. try:
  331. V3(parameters, iteration=i)
  332. except Exception:
  333. continue
  334.  
  335. plot_results.run()
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