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Awareness of being: a computational neurophenomenological model of mindfulness, mind-wandering, and meta-attentional control.

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C++ · 678 lines · 59 KB · Apache-2.0

  1. // Predictive-coding-inspired Variational RNN
  2. // error_regression.cpp
  3. // Copyright © 2022 Hayato Idei. All rights reserved.
  4. //
  5. #include "network.hpp"
  6. #include <sys/time.h>
  8. //hyper-parameter used in error regression
  9. #define TEST_SEQ_NUM 10 //sequence number of target data in error regression
  10. #define ADAM1 0.9
  11. #define ADAM2 0.999
  12. #define MAX_TIME_LENGTH 2200 //max time length including future states
  13. #define TIME_LENGTH 2000
  14. #define POINT 25
  15. #define CUE_STATE_STEP 5
  16. #define CUE_STATE_STEP_KEEP 10
  17. #define FOOD_CYCLE 100
  18. #define NO_FOOD_STEP 25
  19. #define GET_DISTANCE 0.2
  20. double sigmoid(double x, double bias, double gain)
  21. {
  22. return 1.0 / (1.0 + exp(-gain * (x-bias)));
  23. }
  24. //robot error regression
  25. int main(void){
  27. vector<vector<vector<double> > > real_state_extero(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(x_extero_num, 0)));
  28. vector<vector<vector<double> > > real_state_proprio(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(x_proprio_num, 0)));
  29. vector<vector<vector<double> > > real_state_intero(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(x_intero_num, 0)));
  30. vector<vector<vector<double> > > target(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(x_num, 0)));
  32. vector<vector<vector<double> > > target_extero(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(x_extero_num, 0)));
  33. vector<vector<vector<double> > > target_proprio(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(x_proprio_num, 0)));
  34. vector<vector<vector<double> > > joint_angle(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(x_proprio_num, 0)));
  35. vector<vector<vector<double> > > target_intero(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(x_intero_num, 0)));
  37. vector<vector<vector<double> > > pe_extero(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(x_extero_num, 0)));
  38. vector<vector<vector<double> > > pe_proprio(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(x_proprio_num, 0)));
  39. vector<vector<vector<double> > > pe_intero(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(x_intero_num, 0)));
  40. vector<vector<vector<double> > > wkl_exteroceptive(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(exteroceptive_z_num, 0)));
  41. vector<vector<vector<double> > > wkl_proprioceptive(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(proprioceptive_z_num, 0)));
  42. vector<vector<vector<double> > > wkl_interoceptive(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(interoceptive_z_num, 0)));
  43. vector<vector<vector<double> > > wkl_neuromodulation(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(neuromodulation_z_num, 0)));
  44. vector<vector<vector<double> > > wkl_associative(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(associative_z_num, 0)));
  45. vector<vector<vector<double> > > wkl_executive(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(executive_z_num, 0)));
  47. vector<vector<vector<double> > > in_p_mu_executive_window_head(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(executive_z_num, 0)));
  48. vector<vector<vector<double> > > in_p_sigma_executive_window_head(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(executive_z_num, 0)));
  49. vector<vector<vector<double> > > a_mu_executive_window_head(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(executive_z_num, 0)));
  50. vector<vector<vector<double> > > a_sigma_executive_window_head(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(executive_z_num, 0)));
  51. vector<vector<vector<double> > > wkl_executive_window_head(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(executive_z_num, 0)));
  53. vector<vector<vector<double> > > in_p_mu_neuromodulation_window_head(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(neuromodulation_z_num, 0)));
  54. vector<vector<vector<double> > > in_p_sigma_neuromodulation_window_head(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(neuromodulation_z_num, 0)));
  55. vector<vector<vector<double> > > a_mu_neuromodulation_window_head(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(neuromodulation_z_num, 0)));
  56. vector<vector<vector<double> > > a_sigma_neuromodulation_window_head(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(neuromodulation_z_num, 0)));
  57. vector<vector<vector<double> > > wkl_neuromodulation_window_head(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(neuromodulation_z_num, 0)));
  59. vector<vector<vector<double> > > in_p_mu_associative_window_head(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(associative_z_num, 0)));
  60. vector<vector<vector<double> > > in_p_sigma_associative_window_head(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(associative_z_num, 0)));
  61. vector<vector<vector<double> > > a_mu_associative_window_head(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(associative_z_num, 0)));
  62. vector<vector<vector<double> > > a_sigma_associative_window_head(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(associative_z_num, 0)));
  63. vector<vector<vector<double> > > wkl_associative_window_head(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(associative_z_num, 0)));
  65. vector<vector<vector<double> > > in_p_mu_exteroceptive_window_head(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(exteroceptive_z_num, 0)));
  66. vector<vector<vector<double> > > in_p_sigma_exteroceptive_window_head(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(exteroceptive_z_num, 0)));
  67. vector<vector<vector<double> > > a_mu_exteroceptive_window_head(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(exteroceptive_z_num, 0)));
  68. vector<vector<vector<double> > > a_sigma_exteroceptive_window_head(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(exteroceptive_z_num, 0)));
  69. vector<vector<vector<double> > > wkl_exteroceptive_window_head(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(exteroceptive_z_num, 0)));
  71. vector<vector<vector<double> > > in_p_mu_proprioceptive_window_head(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(proprioceptive_z_num, 0)));
  72. vector<vector<vector<double> > > in_p_sigma_proprioceptive_window_head(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(proprioceptive_z_num, 0)));
  73. vector<vector<vector<double> > > a_mu_proprioceptive_window_head(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(proprioceptive_z_num, 0)));
  74. vector<vector<vector<double> > > a_sigma_proprioceptive_window_head(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(proprioceptive_z_num, 0)));
  75. vector<vector<vector<double> > > wkl_proprioceptive_window_head(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(proprioceptive_z_num, 0)));
  77. vector<vector<vector<double> > > in_p_mu_interoceptive_window_head(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(interoceptive_z_num, 0)));
  78. vector<vector<vector<double> > > in_p_sigma_interoceptive_window_head(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(interoceptive_z_num, 0)));
  79. vector<vector<vector<double> > > a_mu_interoceptive_window_head(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(interoceptive_z_num, 0)));
  80. vector<vector<vector<double> > > a_sigma_interoceptive_window_head(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(interoceptive_z_num, 0)));
  81. vector<vector<vector<double> > > wkl_interoceptive_window_head(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(interoceptive_z_num, 0)));
  83. vector<vector<vector<double> > > output_extero_window_head(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(x_extero_num, 0)));
  84. vector<vector<vector<double> > > output_proprio_window_head(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(x_proprio_num, 0)));
  85. vector<vector<vector<double> > > output_intero_window_head(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(x_intero_num, 0)));
  87. vector<vector<vector<double> > > sensory_sigma_window_head(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(x_num, 0)));
  89. vector<vector<vector<double> > > pe_extero_window_head(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(x_extero_num, 0)));
  90. vector<vector<vector<double> > > pe_proprio_window_head(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(x_proprio_num, 0)));
  91. vector<vector<vector<double> > > pe_intero_window_head(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(x_intero_num, 0)));
  93. vector<vector<vector<double> > > fe_past_window_head(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(1, 0)));
  94. vector<vector<vector<double> > > fe_future_window_head(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(1, 0)));
  96. vector<vector<double> > points(POINT, vector<double>(2, 0));
  97. for(int i=0; i<POINT; ++i){
  98. int x = i%5;
  99. int y = i/5;
  100. points[i][0] = -0.8 + x*0.4;
  101. points[i][1] = 0.8 - y*0.4;
  102. }
  104. //create food position
  105. uniform_int_distribution<> rand25(0, POINT-1);
  106. vector<vector<vector<double> > > food_position_state_sequence(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(3, 0)));// x, y, state
  107. int sample_point = rand25(engine);
  108. for(int s=0; s<TEST_SEQ_NUM; ++s){
  109. for(int t=0; t<TIME_LENGTH; ++t){
  110. if(t%FOOD_CYCLE==0){
  111. sample_point = rand25(engine);
  112. }
  113. food_position_state_sequence[s][t][0] = points[sample_point][0];
  114. food_position_state_sequence[s][t][1] = points[sample_point][1];
  115. }
  116. }
  118. //set model
  119. ER_PVRNNTopLayer executive(TEST_SEQ_NUM, MAX_TIME_LENGTH, executive_d_num, executive_z_num, executive_W, executive_W1, executive_tau, "executive");
  120. ER_PVRNNLayer associative(TEST_SEQ_NUM, MAX_TIME_LENGTH, associative_d_num, executive_d_num, associative_z_num, associative_W, associative_W1, associative_tau, "associative");
  122. ER_PVRNNLayer neuromodulation(TEST_SEQ_NUM, MAX_TIME_LENGTH, neuromodulation_d_num, executive_d_num, neuromodulation_z_num, neuromodulation_W, neuromodulation_W1, neuromodulation_tau, "neuromodulation");
  124. ER_PVRNNLayer exteroceptive(TEST_SEQ_NUM, MAX_TIME_LENGTH, exteroceptive_d_num, associative_d_num, exteroceptive_z_num, exteroceptive_W, exteroceptive_W1, exteroceptive_tau, "exteroceptive");
  125. ER_PVRNNLayer proprioceptive(TEST_SEQ_NUM, MAX_TIME_LENGTH, proprioceptive_d_num, associative_d_num, proprioceptive_z_num, proprioceptive_W, proprioceptive_W1, proprioceptive_tau, "proprioceptive");
  126. ER_PVRNNLayer interoceptive(TEST_SEQ_NUM, MAX_TIME_LENGTH, interoceptive_d_num, associative_d_num, interoceptive_z_num, interoceptive_W, interoceptive_W1, interoceptive_tau, "interoceptive");
  128. ER_Output out_extero(TEST_SEQ_NUM, MAX_TIME_LENGTH, x_extero_num, exteroceptive_d_num, "out_extero");
  129. ER_Output out_proprio(TEST_SEQ_NUM, MAX_TIME_LENGTH, x_proprio_num, proprioceptive_d_num, "out_proprio");
  130. ER_Output out_intero(TEST_SEQ_NUM, MAX_TIME_LENGTH, x_intero_num, interoceptive_d_num, "out_intero");
  132. ER_OutputNM out_nm(TEST_SEQ_NUM, MAX_TIME_LENGTH, x_num, neuromodulation_d_num, x_nm_tau, "out_nm");
  134. //concatenated variables used in backprop
  135. vector<vector<vector<double> > > x_mean(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(x_num, 0)));
  136. vector<vector<vector<double> > > x_sigma_extero(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(x_extero_num, 0)));
  137. vector<vector<vector<double> > > x_sigma_proprio(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(x_proprio_num, 0)));
  138. vector<vector<vector<double> > > x_sigma_intero(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(x_intero_num, 0)));
  140. vector<double> exte_prop_intero_tau(exteroceptive_d_num+proprioceptive_d_num+interoceptive_d_num, 0);
  141. vector<vector<double> > exte_prop_intero_weight_ld(exteroceptive_d_num+proprioceptive_d_num+interoceptive_d_num, vector<double>(associative_d_num, 0));
  142. vector<vector<vector<double> > > exte_prop_intero_grad_internal_state_d(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(exteroceptive_d_num+proprioceptive_d_num+interoceptive_d_num, 0)));
  144. vector<double> nm_ass_tau(neuromodulation_d_num+associative_d_num, 0);
  145. vector<vector<double> > nm_ass_weight_ld(neuromodulation_d_num+associative_d_num, vector<double>(executive_d_num, 0));
  146. vector<vector<vector<double> > > nm_ass_grad_internal_state_d(TEST_SEQ_NUM, vector<vector<double> >(MAX_TIME_LENGTH, vector<double>(neuromodulation_d_num+associative_d_num, 0)));
  148. //set variables related with sensory uncertainty
  149. normal_distribution<> dist_sn(0.0, sqrt(0.0001)); //sensory noise
  150. normal_distribution<> dist_normal(0.0, 1.0); //sensory noise caused by interoception
  152. vector<int> future_window_list = {200}; //time window in future
  153. vector<int> past_window_list ={10}; //time window in past
  154. vector<int> iteration_list = {200};
  155. vector<double> learning_rate_list = {0.1};
  156. int future_window_list_size = int(future_window_list.size());
  157. int past_window_list_size = int(past_window_list.size());
  158. int iteration_list_size = int(iteration_list.size());
  159. int learning_rate_list_size = int(learning_rate_list.size());
  160. for(int fw=0;fw<future_window_list_size;++fw){
  161. int future_window = future_window_list[fw];
  162. for(int pw=0;pw<past_window_list_size;++pw){
  163. int past_window = past_window_list[pw];
  164. for(int itr=0;itr<iteration_list_size;++itr){
  165. int iteration = iteration_list[itr];
  166. for(int lr=0;lr<learning_rate_list_size;++lr){
  167. double alpha = learning_rate_list[lr];
  168. //make save directory
  169. stringstream path_generation_executive;
  170. path_generation_executive << "./test_generation_allostasis" << "/fw_" << future_window << "/pw_" << past_window << "/itr_" << iteration << "/lr_" << alpha << "/executive";
  171. string str_path_generation_executive = path_generation_executive.str();
  172. mkdir(str_path_generation_executive.c_str(), 0777);
  174. stringstream path_generation_associative;
  175. path_generation_associative << "./test_generation_allostasis" << "/fw_" << future_window << "/pw_" << past_window << "/itr_" << iteration << "/lr_" << alpha << "/associative";
  176. string str_path_generation_associative = path_generation_associative.str();
  177. mkdir(str_path_generation_associative.c_str(), 0777);
  179. stringstream path_generation_neuromodulation;
  180. path_generation_neuromodulation << "./test_generation_allostasis" << "/fw_" << future_window << "/pw_" << past_window << "/itr_" << iteration << "/lr_" << alpha << "/neuromodulation";
  181. string str_path_generation_neuromodulation = path_generation_neuromodulation.str();
  182. mkdir(str_path_generation_neuromodulation.c_str(), 0777);
  184. stringstream path_generation_exteroceptive;
  185. path_generation_exteroceptive << "./test_generation_allostasis" << "/fw_" << future_window << "/pw_" << past_window << "/itr_" << iteration << "/lr_" << alpha << "/exteroceptive";
  186. string str_path_generation_exteroceptive = path_generation_exteroceptive.str();
  187. mkdir(str_path_generation_exteroceptive.c_str(), 0777);
  189. stringstream path_generation_proprioceptive;
  190. path_generation_proprioceptive << "./test_generation_allostasis" << "/fw_" << future_window << "/pw_" << past_window << "/itr_" << iteration << "/lr_" << alpha << "/proprioceptive";
  191. string str_path_generation_proprioceptive = path_generation_proprioceptive.str();
  192. mkdir(str_path_generation_proprioceptive.c_str(), 0777);
  194. stringstream path_generation_interoceptive;
  195. path_generation_interoceptive << "./test_generation_allostasis" << "/fw_" << future_window << "/pw_" << past_window << "/itr_" << iteration << "/lr_" << alpha << "/interoceptive";
  196. string str_path_generation_interoceptive = path_generation_interoceptive.str();
  197. mkdir(str_path_generation_interoceptive.c_str(), 0777);
  199. stringstream path_generation_out_extero;
  200. path_generation_out_extero << "./test_generation_allostasis" << "/fw_" << future_window << "/pw_" << past_window << "/itr_" << iteration << "/lr_" << alpha << "/out_extero";
  201. string str_path_generation_out_extero = path_generation_out_extero.str();
  202. mkdir(str_path_generation_out_extero.c_str(), 0777);
  204. stringstream path_generation_out_proprio;
  205. path_generation_out_proprio << "./test_generation_allostasis" << "/fw_" << future_window << "/pw_" << past_window << "/itr_" << iteration << "/lr_" << alpha << "/out_proprio";
  206. string str_path_generation_out_proprio = path_generation_out_proprio.str();
  207. mkdir(str_path_generation_out_proprio.c_str(), 0777);
  209. stringstream path_generation_out_intero;
  210. path_generation_out_intero << "./test_generation_allostasis" << "/fw_" << future_window << "/pw_" << past_window << "/itr_" << iteration << "/lr_" << alpha << "/out_intero";
  211. string str_path_generation_out_intero = path_generation_out_intero.str();
  212. mkdir(str_path_generation_out_intero.c_str(), 0777);
  214. stringstream path_generation_out_nm;
  215. path_generation_out_nm << "./test_generation_allostasis" << "/fw_" << future_window << "/pw_" << past_window << "/itr_" << iteration << "/lr_" << alpha << "/out_nm";
  216. string str_path_generation_out_nm = path_generation_out_nm.str();
  217. mkdir(str_path_generation_out_nm.c_str(), 0777);
  219. stringstream path_generation_food;
  220. path_generation_food << "./test_generation_allostasis" << "/fw_" << future_window << "/pw_" << past_window << "/itr_" << iteration << "/lr_" << alpha << "/food";
  221. string str_path_generation_food = path_generation_food.str();
  222. mkdir(str_path_generation_food.c_str(), 0777);
  224. stringstream path_generation_fe;
  225. path_generation_fe << "./test_generation_allostasis" << "/fw_" << future_window << "/pw_" << past_window << "/itr_" << iteration << "/lr_" << alpha;
  226. string str_path_generation_fe = path_generation_fe.str();
  228. double max_sigma_intero = sqrt(0.1);
  229. double cue_x = 0.0;
  230. double cue_y = 0.4;
  231. //error regression
  232. for(int s=0;s<TEST_SEQ_NUM;++s){
  233. //measure time
  234. struct timeval start, end;
  235. gettimeofday(&start, NULL);
  236. double interoceptive_state = 0.0; // initialize interoception
  237. double cue_state_x = -0.8;
  238. double cue_state_y = -0.8;
  239. int cue_flag = 1;
  240. int cue_count = 0;
  241. double cue_state_x_store = 0.0;
  242. double cue_state_y_store = 0.0;
  243. int hit_count = 0;
  244. for(int ct=0;ct<TIME_LENGTH;++ct){//ct: current time step
  245. printf("Future window size: %d, Past window size: %d, Iteration size: %d, Alpha: %lf, Sequence: %d, Time step: %d\n", future_window, past_window, iteration, alpha, s, ct);
  246. int initial_regression;
  247. (ct==0) ? (initial_regression=1) : (initial_regression=0);
  248. int er_past_window;
  249. (ct>=past_window-1) ? (er_past_window=past_window) : (er_past_window=ct+1);
  250. int lr_normalization = er_past_window+future_window;
  251. //int epoch_flag = 0;
  252. for(int epoch=0;epoch<iteration;++epoch){
  253. //Feedforward
  254. for(int wt=ct-er_past_window+1;wt<=ct+future_window;++wt){//wt: time step within time window
  255. int last_window_step=0;
  256. (wt>=ct) ? (last_window_step=1) : (last_window_step=0);
  258. executive.er_forward(wt, s, epoch, initial_regression, last_window_step, "posterior");
  259. associative.er_forward(executive.d, wt, s, epoch, initial_regression, last_window_step, "posterior");
  260. neuromodulation.er_forward(executive.d, wt, s, epoch, initial_regression, last_window_step, "posterior");
  261. exteroceptive.er_forward(associative.d, wt, s, epoch, initial_regression, last_window_step, "posterior");
  262. proprioceptive.er_forward(associative.d, wt, s, epoch, initial_regression, last_window_step, "posterior");
  263. interoceptive.er_forward(associative.d, wt, s, epoch, initial_regression, last_window_step, "posterior");
  265. out_extero.er_forward(exteroceptive.d, wt, s);
  266. out_proprio.er_forward(proprioceptive.d, wt, s);
  267. out_intero.er_forward(interoceptive.d, wt, s);
  268. out_nm.er_forward(neuromodulation.d, wt, s);
  270. //Set future target and so on
  271. for(int i = 0; i < x_num; ++i){
  272. if(i<x_extero_num){
  273. x_mean[s][wt][i] = out_extero.output[s][wt][i];
  274. x_sigma_extero[s][wt][i] = out_nm.output[s][wt][i];
  275. if(wt>=ct+1){
  276. target_extero[s][wt][i] = out_extero.output[s][wt][i];
  277. target[s][wt][i] = x_mean[s][wt][i];
  278. }
  279. }else if(i<x_extero_num+x_proprio_num){
  280. x_mean[s][wt][i] = out_proprio.output[s][wt][i-x_extero_num];
  281. x_sigma_proprio[s][wt][i-x_extero_num] = out_nm.output[s][wt][i];
  282. if(wt>=ct+1){
  283. target_proprio[s][wt][i-x_extero_num] = out_proprio.output[s][wt][i-x_extero_num];
  284. target[s][wt][i] = x_mean[s][wt][i];
  285. }
  286. }else{
  287. x_mean[s][wt][i] = out_intero.output[s][wt][i-x_extero_num-x_proprio_num];
  288. x_sigma_intero[s][wt][i-x_extero_num-x_proprio_num] = out_nm.output[s][wt][i];
  289. if(wt>=ct+1){
  290. target_intero[s][wt][i-x_extero_num-x_proprio_num] = out_intero.output[s][wt][i-x_extero_num-x_proprio_num];
  291. target[s][wt][i] = x_mean[s][wt][i];
  292. }
  293. }
  294. }
  295. }
  298. //set incoming target at the current time step (if online robot operation)
  299. //set through PID control using proprioceptive prediction in robot operation
  300. if(epoch==0){
  301. double std = max_sigma_intero*(sigmoid(-interoceptive_state, 0.5, 20) + sigmoid(interoceptive_state, 0.5, 20));
  302. if(ct%FOOD_CYCLE<=FOOD_CYCLE-NO_FOOD_STEP){
  303. food_position_state_sequence[s][ct][2] = 0.2*(1.0-1.0/(FOOD_CYCLE-NO_FOOD_STEP)*(ct%FOOD_CYCLE));
  304. }else{
  305. food_position_state_sequence[s][ct][2] = 0.0;
  306. }
  307. real_state_extero[s][ct][0] = cue_state_x;
  308. real_state_extero[s][ct][1] = cue_state_y;
  309. target_extero[s][ct][0] = cue_state_x + std*dist_normal(engine) + dist_sn(engine);
  310. target_extero[s][ct][1] = cue_state_y + std*dist_normal(engine) + dist_sn(engine);
  311. for(int i=0; i<x_extero_num; ++i){
  312. if(target_extero[s][ct][i] >= 1.0){
  313. target_extero[s][ct][i] = 1.0;
  314. }else if(target_extero[s][ct][i] <= -1.0){
  315. target_extero[s][ct][i] = -1.0;
  316. }
  317. target[s][ct][i]=target_extero[s][ct][i];
  318. }
  320. for(int i=0;i<x_proprio_num;++i){
  321. if(ct==0){
  322. joint_angle[s][ct][i] = PID(joint_angle[s][ct][i], out_proprio.output[s][ct][i]);
  323. }else{
  324. joint_angle[s][ct][i] = PID(joint_angle[s][ct-1][i], out_proprio.output[s][ct][i]);
  325. }
  326. real_state_proprio[s][ct][i] = joint_angle[s][ct][i];
  327. target_proprio[s][ct][i] = joint_angle[s][ct][i] + std*dist_normal(engine) + dist_sn(engine);
  328. if(target_proprio[s][ct][i] >= 1.0){
  329. target_proprio[s][ct][i] = 1.0;
  330. }else if(target_proprio[s][ct][i] <= -1.0){
  331. target_proprio[s][ct][i] = -1.0;
  332. }
  333. target[s][ct][i+x_extero_num] = target_proprio[s][ct][i];
  334. }
  335. real_state_intero[s][ct][0] = interoceptive_state;
  336. target_intero[s][ct][0] = interoceptive_state + std*dist_normal(engine) + dist_sn(engine);
  337. if(target_intero[s][ct][0] >= 1.0){
  338. target_intero[s][ct][0] = 1.0;
  339. }else if(target_intero[s][ct][0] <= -1.0){
  340. target_intero[s][ct][0] = -1.0;
  341. }
  342. target[s][ct][x_extero_num+x_proprio_num] = target_intero[s][ct][0];
  344. double food_distance = sqrt(pow(real_state_proprio[s][ct][0] - food_position_state_sequence[s][ct][0],2) + pow(real_state_proprio[s][ct][1] - food_position_state_sequence[s][ct][1],2));
  345. double cue_distance = sqrt(pow(real_state_proprio[s][ct][0] - cue_x,2) + pow(real_state_proprio[s][ct][1] - cue_y,2));
  346. double movement;
  347. if(ct==0){
  348. movement = 0.0;
  349. }else{
  350. movement = sqrt(pow(real_state_proprio[s][ct][0] - real_state_proprio[s][ct-1][0],2) + pow(real_state_proprio[s][ct][1] - real_state_proprio[s][ct-1][1],2));
  351. }
  352. interoceptive_state -= 0.013*(0.1+movement);
  354. //get food
  355. if(food_distance <= GET_DISTANCE){
  356. interoceptive_state += food_position_state_sequence[s][ct][2];
  357. hit_count++;
  358. }
  359. //get cue
  360. if(ct%FOOD_CYCLE>=FOOD_CYCLE-5){
  361. if(ct%FOOD_CYCLE==FOOD_CYCLE-5){
  362. cue_state_x_store = cue_state_x;
  363. cue_state_y_store = cue_state_y;
  364. }
  365. cue_state_x -= (cue_state_x_store+0.8)/5;
  366. cue_state_y -= (cue_state_y_store+0.8)/5;
  367. cue_flag = 1;
  368. cue_count = 0;
  369. }else{
  370. if(cue_flag==1){
  371. if(cue_distance <= GET_DISTANCE){
  372. cue_flag = 0;
  373. }
  374. }else if(cue_flag==0){
  375. cue_count++;
  376. if(cue_count<=CUE_STATE_STEP){
  377. cue_state_x += (food_position_state_sequence[s][ct][0]+0.8)/CUE_STATE_STEP;
  378. cue_state_y += (food_position_state_sequence[s][ct][1]+0.8)/CUE_STATE_STEP;
  379. }else if(cue_count>CUE_STATE_STEP && cue_count<=CUE_STATE_STEP+CUE_STATE_STEP_KEEP){
  380. cue_state_x = food_position_state_sequence[s][ct][0];
  381. cue_state_y = food_position_state_sequence[s][ct][1];
  382. }else if(cue_count>CUE_STATE_STEP+CUE_STATE_STEP_KEEP && cue_count<=2*CUE_STATE_STEP+CUE_STATE_STEP_KEEP){
  383. cue_state_x -= (food_position_state_sequence[s][ct][0]+0.8)/CUE_STATE_STEP;
  384. cue_state_y -= (food_position_state_sequence[s][ct][1]+0.8)/CUE_STATE_STEP;
  385. }else{
  386. cue_flag = 1;
  387. cue_count = 0;
  388. }
  389. }
  390. }
  391. if(interoceptive_state >= 1.0){
  392. interoceptive_state = 1.0;
  393. }else if(interoceptive_state <= -1.0){
  394. interoceptive_state = -1.0;
  395. }
  396. }
  398. //error
  399. double normalize = 1.0/lr_normalization;
  400. fe_past_window_head[s][ct][0] = 0.0;
  401. fe_future_window_head[s][ct][0] = 0.0;
  402. for(int wt=ct-er_past_window+1;wt<=ct+future_window;++wt){
  403. double future_entropy_constant_term;
  404. (wt<=ct) ? (future_entropy_constant_term=0.0) : (future_entropy_constant_term=1.0);
  405. double past_flag;
  406. (wt<=ct) ? (past_flag=1.0) : (past_flag=0.0);
  407. double future_flag;
  408. (wt<=ct) ? (future_flag=0.0) : (future_flag=1.0);
  409. for(int i=0; i<x_extero_num; ++i){
  410. pe_extero[s][wt][i] = 0.5*(pow((target_extero[s][wt][i]-out_extero.output[s][wt][i])/x_sigma_extero[s][wt][i],2)+log(2*PI)+2.0*log(x_sigma_extero[s][wt][i])+future_entropy_constant_term)*normalize/x_extero_num;
  411. fe_past_window_head[s][ct][0] += past_flag*pe_extero[s][wt][i];
  412. fe_future_window_head[s][ct][0] += future_flag*pe_extero[s][wt][i];
  413. }
  414. for(int i=0; i<x_proprio_num; ++i){
  415. pe_proprio[s][wt][i] = 0.5*(pow((target_proprio[s][wt][i]-out_proprio.output[s][wt][i])/x_sigma_proprio[s][wt][i],2)+log(2*PI)+2.0*log(x_sigma_proprio[s][wt][i])+future_entropy_constant_term)*normalize/x_proprio_num;
  416. fe_past_window_head[s][ct][0] += past_flag*pe_proprio[s][wt][i];
  417. fe_future_window_head[s][ct][0] += future_flag*pe_proprio[s][wt][i];
  418. }
  419. for(int i=0; i<x_intero_num; ++i){
  420. pe_intero[s][wt][i] = 0.5*(pow((target_intero[s][wt][i]-out_intero.output[s][wt][i])/x_sigma_intero[s][wt][i],2)+log(2*PI)+2.0*log(x_sigma_intero[s][wt][i])+future_entropy_constant_term)*normalize/x_intero_num;
  421. fe_past_window_head[s][ct][0] += past_flag*pe_intero[s][wt][i];
  422. fe_future_window_head[s][ct][0] += future_flag*pe_intero[s][wt][i];
  423. }
  424. for(int i=0; i<exteroceptive_z_num; ++i){
  425. wkl_exteroceptive[s][wt][i] = exteroceptive_W*(log(exteroceptive.p_sigma[s][wt][i])-log(exteroceptive.q_sigma[s][wt][i])+0.5*(pow(exteroceptive.p_mu[s][wt][i]-exteroceptive.q_mu[s][wt][i],2)+pow(exteroceptive.q_sigma[s][wt][i],2))/pow(exteroceptive.p_sigma[s][wt][i],2)-0.5)*normalize/exteroceptive_z_num;
  426. fe_past_window_head[s][ct][0] += past_flag*wkl_exteroceptive[s][wt][i];
  427. fe_future_window_head[s][ct][0] += future_flag*wkl_exteroceptive[s][wt][i];
  428. }
  429. for(int i=0; i<proprioceptive_z_num; ++i){
  430. wkl_proprioceptive[s][wt][i] = proprioceptive_W*(log(proprioceptive.p_sigma[s][wt][i])-log(proprioceptive.q_sigma[s][wt][i])+0.5*(pow(proprioceptive.p_mu[s][wt][i]-proprioceptive.q_mu[s][wt][i],2)+pow(proprioceptive.q_sigma[s][wt][i],2))/pow(proprioceptive.p_sigma[s][wt][i],2)-0.5)*normalize/proprioceptive_z_num;
  431. fe_past_window_head[s][ct][0] += past_flag*wkl_proprioceptive[s][wt][i];
  432. fe_future_window_head[s][ct][0] += future_flag*wkl_proprioceptive[s][wt][i];
  433. }
  434. for(int i=0; i<interoceptive_z_num; ++i){
  435. wkl_interoceptive[s][wt][i] = interoceptive_W*(log(interoceptive.p_sigma[s][wt][i])-log(interoceptive.q_sigma[s][wt][i])+0.5*(pow(interoceptive.p_mu[s][wt][i]-interoceptive.q_mu[s][wt][i],2)+pow(interoceptive.q_sigma[s][wt][i],2))/pow(interoceptive.p_sigma[s][wt][i],2)-0.5)*normalize/interoceptive_z_num;
  436. fe_past_window_head[s][ct][0] += past_flag*wkl_interoceptive[s][wt][i];
  437. fe_future_window_head[s][ct][0] += future_flag*wkl_interoceptive[s][wt][i];
  438. }
  439. for(int i=0; i<neuromodulation_z_num; ++i){
  440. wkl_neuromodulation[s][wt][i] = neuromodulation_W*(log(neuromodulation.p_sigma[s][wt][i])-log(neuromodulation.q_sigma[s][wt][i])+0.5*(pow(neuromodulation.p_mu[s][wt][i]-neuromodulation.q_mu[s][wt][i],2)+pow(neuromodulation.q_sigma[s][wt][i],2))/pow(neuromodulation.p_sigma[s][wt][i],2)-0.5)*normalize/neuromodulation_z_num;
  441. fe_past_window_head[s][ct][0] += past_flag*wkl_neuromodulation[s][wt][i];
  442. fe_future_window_head[s][ct][0] += future_flag*wkl_neuromodulation[s][wt][i];
  443. }
  444. for(int i=0; i<associative_z_num; ++i){
  445. wkl_associative[s][wt][i] = associative_W*(log(associative.p_sigma[s][wt][i])-log(associative.q_sigma[s][wt][i])+0.5*(pow(associative.p_mu[s][wt][i]-associative.q_mu[s][wt][i],2)+pow(associative.q_sigma[s][wt][i],2))/pow(associative.p_sigma[s][wt][i],2)-0.5)*normalize/associative_z_num;
  446. fe_past_window_head[s][ct][0] += past_flag*wkl_associative[s][wt][i];
  447. fe_future_window_head[s][ct][0] += future_flag*wkl_associative[s][wt][i];
  448. }
  449. for(int i=0; i<executive_z_num; ++i){
  450. wkl_executive[s][wt][i] = executive_W*(log(executive.p_sigma[s][wt][i])-log(executive.q_sigma[s][wt][i])+0.5*(pow(executive.p_mu[s][wt][i]-executive.q_mu[s][wt][i],2)+pow(executive.q_sigma[s][wt][i],2))/pow(executive.p_sigma[s][wt][i],2)-0.5)*normalize/executive_z_num;
  451. fe_past_window_head[s][ct][0] += past_flag*wkl_executive[s][wt][i];
  452. fe_future_window_head[s][ct][0] += future_flag*wkl_executive[s][wt][i];
  453. }
  454. }
  456. //Backward
  457. //set constant for Rectified Adam
  458. double rho_inf = 2.0/(1.0-ADAM2)-1.0;
  459. double rho = rho_inf-2.0*(epoch+1)*pow(ADAM2,epoch+1)/(1.0-pow(ADAM2,epoch+1));
  460. double r_store = sqrt((rho-4.0)*(rho-2.0)*rho_inf/ (rho_inf-4.0)/(rho_inf-2.0)/rho);
  461. double m_radam1 = 1.0/(1.0-pow(ADAM1,epoch+1));
  462. double l_radam2 = 1.0-pow(ADAM2,epoch+1);
  463. for(int wt=ct+future_window;wt>=ct-er_past_window+1;--wt){
  464. int last_window_step;
  465. (wt==ct+future_window) ? (last_window_step=1) : (last_window_step=0);
  466. out_intero.er_backward_nm(target_intero, x_sigma_intero, wt, s, lr_normalization);
  467. out_proprio.er_backward_nm(target_proprio, x_sigma_proprio, wt, s, lr_normalization);
  468. out_extero.er_backward_nm(target_extero, x_sigma_extero, wt, s, lr_normalization);
  469. out_nm.er_backward(target, x_mean, last_window_step, wt, s, lr_normalization);
  471. interoceptive.er_backward(out_intero.weight_ohd, out_intero.grad_internal_state_output, out_intero.tau, last_window_step, wt, s, lr_normalization);
  472. proprioceptive.er_backward(out_proprio.weight_ohd, out_proprio.grad_internal_state_output, out_proprio.tau, last_window_step, wt, s, lr_normalization);
  473. exteroceptive.er_backward(out_extero.weight_ohd, out_extero.grad_internal_state_output, out_extero.tau, last_window_step, wt, s, lr_normalization);
  474. neuromodulation.er_backward(out_nm.weight_ohd, out_nm.grad_internal_state_output, out_nm.tau, last_window_step, wt, s, lr_normalization);
  476. //concatenate some variables in proprioceptive and exteroceptive areas to make backprop-inputs to the associative area
  477. for(int i=0;i<exteroceptive_d_num+proprioceptive_d_num+interoceptive_d_num;++i){
  478. if(i<exteroceptive_d_num) {
  479. exte_prop_intero_grad_internal_state_d[s][wt][i] = exteroceptive.grad_internal_state_d[s][wt][i];
  480. exte_prop_intero_tau[i] = exteroceptive.tau[i];
  481. }else if(i<exteroceptive_d_num+proprioceptive_d_num){
  482. exte_prop_intero_grad_internal_state_d[s][wt][i] = proprioceptive.grad_internal_state_d[s][wt][i-exteroceptive_d_num];
  483. exte_prop_intero_tau[i] = proprioceptive.tau[i-exteroceptive_d_num];
  484. }else{
  485. exte_prop_intero_grad_internal_state_d[s][wt][i] = interoceptive.grad_internal_state_d[s][wt][i-exteroceptive_d_num-proprioceptive_d_num];
  486. exte_prop_intero_tau[i] = interoceptive.tau[i-exteroceptive_d_num-proprioceptive_d_num];
  487. }
  488. for(int j=0;j<associative_d_num;++j){
  489. if(i<exteroceptive_d_num){
  490. exte_prop_intero_weight_ld[i][j] = exteroceptive.weight_dhd[i][j];
  491. }else if(i<exteroceptive_d_num+proprioceptive_d_num){
  492. exte_prop_intero_weight_ld[i][j] = proprioceptive.weight_dhd[i-exteroceptive_d_num][j];
  493. }else{
  494. exte_prop_intero_weight_ld[i][j] = interoceptive.weight_dhd[i-exteroceptive_d_num-proprioceptive_d_num][j];
  495. }
  496. }
  497. }
  498. associative.er_backward(exte_prop_intero_weight_ld, exte_prop_intero_grad_internal_state_d, exte_prop_intero_tau, last_window_step, wt, s, lr_normalization);
  500. //concatenate some variables in associative and neuromodulation areas to make backprop-inputs to the executive area
  501. for(int i=0;i<neuromodulation_d_num+associative_d_num;++i){
  502. if(i<neuromodulation_d_num){
  503. nm_ass_grad_internal_state_d[s][wt][i] = neuromodulation.grad_internal_state_d[s][wt][i];
  504. nm_ass_tau[i] = neuromodulation.tau[i];
  505. }else{
  506. nm_ass_grad_internal_state_d[s][wt][i] = associative.grad_internal_state_d[s][wt][i-neuromodulation_d_num];
  507. nm_ass_tau[i] = associative.tau[i-neuromodulation_d_num];
  508. }
  509. for(int j=0;j<executive_d_num;++j){
  510. if(i<neuromodulation_d_num){
  511. nm_ass_weight_ld[i][j] = neuromodulation.weight_dhd[i][j];
  512. }else{
  513. nm_ass_weight_ld[i][j] = associative.weight_dhd[i-neuromodulation_d_num][j];
  514. }
  515. }
  516. }
  518. executive.er_backward(nm_ass_weight_ld, nm_ass_grad_internal_state_d, nm_ass_tau, last_window_step, wt, s, lr_normalization);
  520. //update adaptive vector (Rectified Adam)
  521. interoceptive.er_update_parameter_radam(wt, s, epoch, alpha, ADAM1, ADAM2, rho, r_store, m_radam1, l_radam2);
  522. proprioceptive.er_update_parameter_radam(wt, s, epoch, alpha, ADAM1, ADAM2, rho, r_store, m_radam1, l_radam2);
  523. exteroceptive.er_update_parameter_radam(wt, s, epoch, alpha, ADAM1, ADAM2, rho, r_store, m_radam1, l_radam2);
  524. neuromodulation.er_update_parameter_radam(wt, s, epoch, alpha, ADAM1, ADAM2, rho, r_store, m_radam1, l_radam2);
  525. associative.er_update_parameter_radam(wt, s, epoch, alpha, ADAM1, ADAM2, rho, r_store, m_radam1, l_radam2);
  526. executive.er_update_parameter_radam(wt, s, epoch, alpha, ADAM1, ADAM2, rho, r_store, m_radam1, l_radam2);
  527. }
  529. }
  530. //set timeseries at the head of the past time window
  531. for(int i=0; i<executive_z_num; ++i){
  532. in_p_mu_executive_window_head[s][ct][i] = executive.internal_state_p_mu[s][ct][i];
  533. in_p_sigma_executive_window_head[s][ct][i] = executive.internal_state_p_sigma[s][ct][i];
  534. a_mu_executive_window_head[s][ct][i] = executive.a_mu[s][ct][i];
  535. a_sigma_executive_window_head[s][ct][i] = executive.a_sigma[s][ct][i];
  536. wkl_executive_window_head[s][ct][i] = wkl_executive[s][ct][i];
  537. }
  538. for(int i=0; i<neuromodulation_z_num; ++i){
  539. in_p_mu_neuromodulation_window_head[s][ct][i] = neuromodulation.internal_state_p_mu[s][ct][i];
  540. in_p_sigma_neuromodulation_window_head[s][ct][i] = neuromodulation.internal_state_p_sigma[s][ct][i];
  541. a_mu_neuromodulation_window_head[s][ct][i] = neuromodulation.a_mu[s][ct][i];
  542. a_sigma_neuromodulation_window_head[s][ct][i] = neuromodulation.a_sigma[s][ct][i];
  543. wkl_neuromodulation_window_head[s][ct][i] = wkl_neuromodulation[s][ct][i];
  544. }
  545. for(int i=0; i<associative_z_num; ++i){
  546. in_p_mu_associative_window_head[s][ct][i] = associative.internal_state_p_mu[s][ct][i];
  547. in_p_sigma_associative_window_head[s][ct][i] = associative.internal_state_p_sigma[s][ct][i];
  548. a_mu_associative_window_head[s][ct][i] = associative.a_mu[s][ct][i];
  549. a_sigma_associative_window_head[s][ct][i] = associative.a_sigma[s][ct][i];
  550. wkl_associative_window_head[s][ct][i] = wkl_associative[s][ct][i];
  551. }
  552. for(int i=0; i<exteroceptive_z_num; ++i){
  553. in_p_mu_exteroceptive_window_head[s][ct][i] = exteroceptive.internal_state_p_mu[s][ct][i];
  554. in_p_sigma_exteroceptive_window_head[s][ct][i] = exteroceptive.internal_state_p_sigma[s][ct][i];
  555. a_mu_exteroceptive_window_head[s][ct][i] = exteroceptive.a_mu[s][ct][i];
  556. a_sigma_exteroceptive_window_head[s][ct][i] = exteroceptive.a_sigma[s][ct][i];
  557. wkl_exteroceptive_window_head[s][ct][i] = wkl_exteroceptive[s][ct][i];
  558. }
  559. for(int i=0; i<proprioceptive_z_num; ++i){
  560. in_p_mu_proprioceptive_window_head[s][ct][i] = proprioceptive.internal_state_p_mu[s][ct][i];
  561. in_p_sigma_proprioceptive_window_head[s][ct][i] = proprioceptive.internal_state_p_sigma[s][ct][i];
  562. a_mu_proprioceptive_window_head[s][ct][i] = proprioceptive.a_mu[s][ct][i];
  563. a_sigma_proprioceptive_window_head[s][ct][i] = proprioceptive.a_sigma[s][ct][i];
  564. wkl_proprioceptive_window_head[s][ct][i] = wkl_proprioceptive[s][ct][i];
  565. }
  566. for(int i=0; i<interoceptive_z_num; ++i){
  567. in_p_mu_interoceptive_window_head[s][ct][i] = interoceptive.internal_state_p_mu[s][ct][i];
  568. in_p_sigma_interoceptive_window_head[s][ct][i] = interoceptive.internal_state_p_sigma[s][ct][i];
  569. a_mu_interoceptive_window_head[s][ct][i] = interoceptive.a_mu[s][ct][i];
  570. a_sigma_interoceptive_window_head[s][ct][i] = interoceptive.a_sigma[s][ct][i];
  571. wkl_interoceptive_window_head[s][ct][i] = wkl_interoceptive[s][ct][i];
  572. }
  573. for(int i = 0; i < x_num; ++i){
  574. sensory_sigma_window_head[s][ct][i] = out_nm.output[s][ct][i];
  575. if(i<x_extero_num){
  576. output_extero_window_head[s][ct][i] = out_extero.output[s][ct][i];
  577. pe_extero_window_head[s][ct][i] = pe_extero[s][ct][i];
  578. }else if(i<x_extero_num+x_proprio_num){
  579. output_proprio_window_head[s][ct][i-x_extero_num] = out_proprio.output[s][ct][i-x_extero_num];
  580. pe_proprio_window_head[s][ct][i-x_extero_num] = pe_proprio[s][ct][i-x_extero_num];
  581. }else{
  582. output_intero_window_head[s][ct][i-x_extero_num-x_proprio_num] = out_intero.output[s][ct][i-x_extero_num-x_proprio_num];
  583. pe_intero_window_head[s][ct][i-x_extero_num-x_proprio_num] = pe_intero[s][ct][i-x_extero_num-x_proprio_num];
  584. }
  585. }
  587. //Save generated sequence
  588. if(ct==TIME_LENGTH-1){
  589. executive.er_save_sequence(str_path_generation_executive, TIME_LENGTH, s, ct);
  590. associative.er_save_sequence(str_path_generation_associative, TIME_LENGTH, s, ct);
  591. neuromodulation.er_save_sequence(str_path_generation_neuromodulation, TIME_LENGTH, s, ct);
  592. exteroceptive.er_save_sequence(str_path_generation_exteroceptive, TIME_LENGTH, s, ct);
  593. proprioceptive.er_save_sequence(str_path_generation_proprioceptive, TIME_LENGTH, s, ct);
  594. interoceptive.er_save_sequence(str_path_generation_interoceptive, TIME_LENGTH, s, ct);
  595. out_nm.er_save_sequence(str_path_generation_out_nm, TIME_LENGTH, s, ct);
  596. out_extero.er_save_sequence(str_path_generation_out_extero, target_extero, TIME_LENGTH, s, ct);
  597. out_proprio.er_save_sequence(str_path_generation_out_proprio, target_proprio, TIME_LENGTH, s, ct);
  598. out_intero.er_save_sequence(str_path_generation_out_intero, target_intero, TIME_LENGTH, s, ct);
  600. save_generated_sequence(str_path_generation_executive, "wkld", wkl_executive, TIME_LENGTH, s, ct);
  601. save_generated_sequence(str_path_generation_associative, "wkld", wkl_associative, TIME_LENGTH, s, ct);
  602. save_generated_sequence(str_path_generation_neuromodulation, "wkld", wkl_neuromodulation, TIME_LENGTH, s, ct);
  603. save_generated_sequence(str_path_generation_exteroceptive, "wkld", wkl_exteroceptive, TIME_LENGTH, s, ct);
  604. save_generated_sequence(str_path_generation_proprioceptive, "wkld", wkl_proprioceptive, TIME_LENGTH, s, ct);
  605. save_generated_sequence(str_path_generation_interoceptive, "wkld", wkl_interoceptive, TIME_LENGTH, s, ct);
  607. save_generated_sequence(str_path_generation_out_extero, "pe", pe_extero, TIME_LENGTH, s, ct);
  608. save_generated_sequence(str_path_generation_out_proprio, "pe", pe_proprio, TIME_LENGTH, s, ct);
  609. save_generated_sequence(str_path_generation_out_intero, "pe", pe_intero, TIME_LENGTH, s, ct);
  610. save_generated_sequence(str_path_generation_out_extero, "real", real_state_extero, TIME_LENGTH, s, ct);
  611. save_generated_sequence(str_path_generation_out_proprio, "real", real_state_proprio, TIME_LENGTH, s, ct);
  612. save_generated_sequence(str_path_generation_out_intero, "real", real_state_intero, TIME_LENGTH, s, ct);
  613. save_generated_sequence(str_path_generation_food, "food", food_position_state_sequence, TIME_LENGTH, s, ct);
  615. //save timeseries at the head of the past time window
  616. save_generated_sequence(str_path_generation_executive, "in_p_mu_window_head", in_p_mu_executive_window_head, TIME_LENGTH, s, ct);
  617. save_generated_sequence(str_path_generation_executive, "in_p_sigma_window_head", in_p_sigma_executive_window_head, TIME_LENGTH, s, ct);
  618. save_generated_sequence(str_path_generation_executive, "a_mu_window_head", a_mu_executive_window_head, TIME_LENGTH, s, ct);
  619. save_generated_sequence(str_path_generation_executive, "a_sigma_window_head", a_sigma_executive_window_head, TIME_LENGTH, s, ct);
  620. save_generated_sequence(str_path_generation_executive, "wkld_window_head", wkl_executive_window_head, TIME_LENGTH, s, ct);
  622. save_generated_sequence(str_path_generation_neuromodulation, "in_p_mu_window_head", in_p_mu_neuromodulation_window_head, TIME_LENGTH, s, ct);
  623. save_generated_sequence(str_path_generation_neuromodulation, "in_p_sigma_window_head", in_p_sigma_neuromodulation_window_head, TIME_LENGTH, s, ct);
  624. save_generated_sequence(str_path_generation_neuromodulation, "a_mu_window_head", a_mu_neuromodulation_window_head, TIME_LENGTH, s, ct);
  625. save_generated_sequence(str_path_generation_neuromodulation, "a_sigma_window_head", a_sigma_neuromodulation_window_head, TIME_LENGTH, s, ct);
  626. save_generated_sequence(str_path_generation_neuromodulation, "wkld_window_head", wkl_neuromodulation_window_head, TIME_LENGTH, s, ct);
  628. save_generated_sequence(str_path_generation_associative, "in_p_mu_window_head", in_p_mu_associative_window_head, TIME_LENGTH, s, ct);
  629. save_generated_sequence(str_path_generation_associative, "in_p_sigma_window_head", in_p_sigma_associative_window_head, TIME_LENGTH, s, ct);
  630. save_generated_sequence(str_path_generation_associative, "a_mu_window_head", a_mu_associative_window_head, TIME_LENGTH, s, ct);
  631. save_generated_sequence(str_path_generation_associative, "a_sigma_window_head", a_sigma_associative_window_head, TIME_LENGTH, s, ct);
  632. save_generated_sequence(str_path_generation_associative, "wkld_window_head", wkl_associative_window_head, TIME_LENGTH, s, ct);
  634. save_generated_sequence(str_path_generation_exteroceptive, "in_p_mu_window_head", in_p_mu_exteroceptive_window_head, TIME_LENGTH, s, ct);
  635. save_generated_sequence(str_path_generation_exteroceptive, "in_p_sigma_window_head", in_p_sigma_exteroceptive_window_head, TIME_LENGTH, s, ct);
  636. save_generated_sequence(str_path_generation_exteroceptive, "a_mu_window_head", a_mu_exteroceptive_window_head, TIME_LENGTH, s, ct);
  637. save_generated_sequence(str_path_generation_exteroceptive, "a_sigma_window_head", a_sigma_exteroceptive_window_head, TIME_LENGTH, s, ct);
  638. save_generated_sequence(str_path_generation_exteroceptive, "wkld_window_head", wkl_exteroceptive_window_head, TIME_LENGTH, s, ct);
  640. save_generated_sequence(str_path_generation_proprioceptive, "in_p_mu_window_head", in_p_mu_proprioceptive_window_head, TIME_LENGTH, s, ct);
  641. save_generated_sequence(str_path_generation_proprioceptive, "in_p_sigma_window_head", in_p_sigma_proprioceptive_window_head, TIME_LENGTH, s, ct);
  642. save_generated_sequence(str_path_generation_proprioceptive, "a_mu_window_head", a_mu_proprioceptive_window_head, TIME_LENGTH, s, ct);
  643. save_generated_sequence(str_path_generation_proprioceptive, "a_sigma_window_head", a_sigma_proprioceptive_window_head, TIME_LENGTH, s, ct);
  644. save_generated_sequence(str_path_generation_proprioceptive, "wkld_window_head", wkl_proprioceptive_window_head, TIME_LENGTH, s, ct);
  646. save_generated_sequence(str_path_generation_interoceptive, "in_p_mu_window_head", in_p_mu_interoceptive_window_head, TIME_LENGTH, s, ct);
  647. save_generated_sequence(str_path_generation_interoceptive, "in_p_sigma_window_head", in_p_sigma_interoceptive_window_head, TIME_LENGTH, s, ct);
  648. save_generated_sequence(str_path_generation_interoceptive, "a_mu_window_head", a_mu_interoceptive_window_head, TIME_LENGTH, s, ct);
  649. save_generated_sequence(str_path_generation_interoceptive, "a_sigma_window_head", a_sigma_interoceptive_window_head, TIME_LENGTH, s, ct);
  650. save_generated_sequence(str_path_generation_interoceptive, "wkld_window_head", wkl_interoceptive_window_head, TIME_LENGTH, s, ct);
  652. save_generated_sequence(str_path_generation_out_extero, "output_window_head", output_extero_window_head, TIME_LENGTH, s, ct);
  653. save_generated_sequence(str_path_generation_out_proprio, "output_window_head", output_proprio_window_head, TIME_LENGTH, s, ct);
  654. save_generated_sequence(str_path_generation_out_intero, "output_window_head", output_intero_window_head, TIME_LENGTH, s, ct);
  656. save_generated_sequence(str_path_generation_out_nm, "output_window_head", sensory_sigma_window_head, TIME_LENGTH, s, ct);
  658. save_generated_sequence(str_path_generation_out_extero, "pe_window_head", pe_extero_window_head, TIME_LENGTH, s, ct);
  659. save_generated_sequence(str_path_generation_out_proprio, "pe_window_head", pe_proprio_window_head, TIME_LENGTH, s, ct);
  660. save_generated_sequence(str_path_generation_out_intero, "pe_window_head", pe_intero_window_head, TIME_LENGTH, s, ct);
  662. save_generated_sequence(str_path_generation_fe, "fe_past_window_head", fe_past_window_head, TIME_LENGTH, s, ct);
  663. save_generated_sequence(str_path_generation_fe, "fe_future_window_head", fe_future_window_head, TIME_LENGTH, s, ct);
  664. }
  665. }
  667. printf("Final Interoceptive state: %lf \n", interoceptive_state);
  669. gettimeofday(&end, NULL);
  670. float delta = end.tv_sec - start.tv_sec + (float)(end.tv_usec - start.tv_usec) / 1000000;
  671. printf("time %lf[s]\n", delta);
  672. }
  673. }
  674. }
  675. }
  676. }
  677. return 0;
  678. }

error_regression_allostasis.cpp at commit 1501fca, under Apache-2.0 · at the source

Overview

Authors: Hayato Idei1, Keisuke Suzuki2, Yuichi Yamashita1
ORCID iDs: Hayato Idei
  1. Department of Information Medicine, National Institute of Neuroscience, National Center of Neurology and Psychiatry, 4-1-1 Ogawa-Higashi, Kodaira, Tokyo 187-8502, Japan
  2. Center for Human Nature, Artificial Intelligence, and Neuroscience (CHAIN), Hokkaido University, Kita 12 Nishi 7, Kita-ku, Sapporo, Hokkaido 060-0812, Japan
Institutions: National Center of Neurology and Psychiatry (Japan); Hokkaido University (Japan)
Journal: Neuroscience of consciousness, volume 2026, issue 1, article niag046
Dates: received 14 October 2025; accepted 21 July 2026; published online 5 September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/nc/niag046 · PMID 42701647 · PMCID PMC13545998 · OpenAlex W4402948919
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.31234/osf.io/stxu6
Status: code verified
Categories: computational modeling (no new data) (modality), none (in silico) (organism)
Methods: Statistics, Machine learning
Keywords: computational neurophenomenology, mindfulness, mind-wandering, allostasis, interoception, variational recurrent neural network
Topic: Mind wandering and attention (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Intramural Research Grant (6-9, 4-6); JSPS KAKENHI (JP24K00499, JP24H00076, JP20H00625); JST Moonshot R&D (JPMJMS2031); AMED Multidisciplinary Frontier Brain and Neuroscience Discoveries (JP24wm0625407); JST, CREST (JPMJCR21P4); JST ACT-X (JPMJAX24C2); JSPS Research Fellows (JP22KJ3167, JP22J01708)
Citations: not cited yet (Europe PMC); 53 references in the paper

Abstract

Mindfulness has established psychological benefits, such as stress reduction and emotional regulation; however, the underlying computational mechanisms, particularly in relation to mind-wandering, remain unclear. This study aimed to present a hierarchical recurrent neural network-based agent model grounded in the free energy principle to explore these processes within the framework of allostasis. The model integrates interoceptive, proprioceptive, and exteroceptive predictive processing, which minimizes the variational free energy throughout past and future contexts. Simulations of the resting state showed that inferred (mentally simulated) homeostatic context modulated attentional shifts between a ‘being’ mode (focused on present interoceptive perception) and a ‘doing’ mode (involving mentally simulated proprioceptive and exteroceptive sensorimotor behaviors). Further, we identified a meta-attentional parameter that controls these shifts by influencing cognitive attitudes toward internally generated beliefs across network modules spanning from the past to the future. By manipulating this parameter, we replicated mindfulness states (‘deep-being’ mode) in which attention remained focused on the present interoceptive state, despite minimal sensory prediction errors. This study offers insights into the computational dynamics of mindfulness and mind-wandering, laying the groundwork for the future exploration of consciousness and pure awareness, which is a state of consciousness devoid of content.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above.

h-idei/mbhrnn

License: Apache-2.0
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 1501fca90602149585e34266b81cfa9513d5108a, 3 April 2024
Languages: C++ (2), Python (2)
Size: 12 files, 4 scripts
Software Heritage: archived
Found in: “Data availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (2 files), NumPy (2 files)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
6 files
At the source: github.com/h-idei/mbhrnn.git

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 4 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability

All data are available in the manuscript and supplementary material. Computer code for the MBH-RNN was written using C++ and is available at https://github.com/h-idei/mbhrnn.git.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 6 keywords, 7 funders, 52 references.

Cite

This paper

Idei, H., Suzuki, K., & Yamashita, Y. (2026). Awareness of being: a computational neurophenomenological model of mindfulness, mind-wandering, and meta-attentional control. Neuroscience of consciousness, 2026(1), niag046. https://doi.org/10.1093/nc/niag046

BibTeX

@article{idei2026awareness,
author = {Idei, Hayato and Suzuki, Keisuke and Yamashita, Yuichi},
title = {{Awareness of being: a computational neurophenomenological model of mindfulness, mind-wandering, and meta-attentional control}},
journal = {Neuroscience of consciousness},
year = {2026},
month = sep,
volume = {2026},
number = {1},
pages = {niag046},
publisher = {Oxford University Press},
issn = {2057-2107},
doi = {10.1093/nc/niag046},
url = {https://doi.org/10.1093/nc/niag046},
pmid = {42701647},
pmcid = {PMC13545998}
}

RIS

TY - JOUR
AU - Idei, Hayato
AU - Suzuki, Keisuke
AU - Yamashita, Yuichi
TI - Awareness of being: a computational neurophenomenological model of mindfulness, mind-wandering, and meta-attentional control
T2 - Neuroscience of consciousness
J2 - Neurosci Conscious
PY - 2026
DA - 2026/09/05
VL - 2026
IS - 1
SP - niag046
SN - 2057-2107
PB - Oxford University Press
DO - 10.1093/nc/niag046
UR - https://doi.org/10.1093/nc/niag046
LA - en
ER -

CSL-JSON

{
"id": "10.1093/nc/niag046",
"type": "article-journal",
"title": "Awareness of being: a computational neurophenomenological model of mindfulness, mind-wandering, and meta-attentional control",
"container-title": "Neuroscience of consciousness",
"author": [
{
"family": "Idei",
"given": "Hayato"
},
{
"family": "Suzuki",
"given": "Keisuke"
},
{
"family": "Yamashita",
"given": "Yuichi"
}
],
"container-title-short": "Neurosci Conscious",
"volume": "2026",
"issue": "1",
"page": "niag046",
"DOI": "10.1093/nc/niag046",
"PMID": "42701647",
"PMCID": "PMC13545998",
"ISSN": "2057-2107",
"publisher": "Oxford University Press",
"URL": "https://doi.org/10.1093/nc/niag046",
"language": "en",
"issued": {
"date-parts": [
[
2026,
9,
5
]
]
}
}

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