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loop_script_morph_develop.py
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# -*- coding: utf-8 -*-
"""
Created on Thu Jul 2 10:32:29 2015
@author: rennocosta
This script is part of the publication Renno-Costa & Tort, 2017, JNeurosci
This script relates to the data presented in the Figure 9c
Will run a single experiment with specific parameters determined below
Output data will be saved in file direction defined at support_filename.py script
"""
import sys, argparse
import numpy as np
from numpy import *
import gzip
import pickle
import support_filename as rfn
import copy
def normalize_weight(www,www_mean):
www /= np.tile(np.mean(www,axis=0),(www.shape[0],1))
return www
def learn_weight(www,activity_pre,activity_pos,lrate):
#www += lrate*(np.tile(activity_pre,(activity_pos.shape[0],1)).transpose()-www) * (np.tile(activity_pos,(activity_pre.shape[0],1)))
www += lrate*(np.tile(activity_pre,(activity_pos.shape[0],1)).transpose()) * (np.tile(activity_pos,(activity_pre.shape[0],1)))
return www
def lec_whichone(lectype,change,ccc,sss):
saida = np.zeros(lectype.shape)
saida[np.logical_and(lectype==1,change<ccc)] = 2
saida[np.logical_and(lectype==1,change>=ccc)] = 1
saida[np.logical_and(lectype==0,change<sss)] = 2
saida[np.logical_and(lectype==0,change>=sss)] = 1
saida[lectype==2] = 1
return saida
def main(argv):
parser = argparse.ArgumentParser(description='Will run a simulation instance.')
# arguments with seeds for the random number generator
parser.add_argument('seed_input', metavar='seed_input', type=int, nargs=1,
help='seed_input number')
parser.add_argument('seed_www', metavar='seed_www', type=int, nargs=1,
help='seed_www')
parser.add_argument('seed_path', metavar='seed_path', type=int, nargs=1,
help='seed_path')
# number of theta cycles, number of full runs for each session and the number of runs before expeirment
parser.add_argument('theta_cycles', metavar='theta_cycles', type=int, nargs=1,
help='theta_cycles')
parser.add_argument('arena_runs', metavar='arena_runs', type=int, nargs=1,
help='arena_runs')
parser.add_argument('pre_runs', metavar='pre_runs', type=int, nargs=1,
help='pre_runs')
# learning rates from place to grid cells, grid to place cells and from lec to place cells...
parser.add_argument('lrate_hpc_mec', metavar='lrate_hpc_mec', type=int, nargs=1,
help='lrate_hpc_mec')
parser.add_argument('lrate_mec_hpc', metavar='lrate_mec_hpc', type=int, nargs=1,
help='lrate_mec_hpc')
parser.add_argument('lrate_lec_hpc', metavar='lrate_lec_hpc', type=int, nargs=1,
help='lrate_lec_hpc')
# relative number of grid cells vs lec cells... relative number of place cells vs recurrent grid cells ...
# sensibility of pattern completion algorithm
parser.add_argument('mec_ratio', metavar='mec_ratio', type=int, nargs=1,
help='MEC ratio (x100)')
parser.add_argument('hpc_ratio', metavar='hpc_ratio', type=int, nargs=1,
help='HPC ratio (x100)')
parser.add_argument('hpc_pcompl_th', metavar='hpc_pcompl_th', type=int, nargs=1,
help='HPC pattern completion th (x100)')
# percentage of correlation after morphing (from 0 to 100)
parser.add_argument('morph_per', metavar='morph_per', type=int, nargs=1,
help='morph_per')
# parser.add_argument('input_noise', metavar='input_noise', type=int, nargs=1,
# help='input_noise')
# parser.add_argument('-w', '--windows',dest='envir',action='store_const',default="default",const="windows")
# parser.add_argument('-u', '--ufrgs',dest='envir',action='store_const',default="default",const="UFRGS")
parser.add_argument('-s', '--cluster',dest='envir',action='store_const',default="default",const="cluster")
parser.add_argument('-c', '--connected',dest='conntype',action='store_const',default="no",const="yes")
parser.add_argument('-a', '--activity',dest='actsave',action='store_const',default="no",const="yes")
parser.add_argument('-k', '--KILL',dest='tokill',action='store_const',default="no",const="yes")
args = parser.parse_args()
envir = args.envir
# conntype = args.conntype
actsave = args.actsave
tokill = args.tokill;
ct = 0
conna = False
# if(conntype=="yes"):
# conna = True
# ct = 1
# else:
# ct = 0
# conna = False
if(actsave=="yes"):
acts = True
else:
acts = False
seed_input = args.seed_input[0]
seed_www = args.seed_www[0]
seed_path = args.seed_path[0]
mec_ratio = float(args.mec_ratio[0])/100
hpc_ratio = float(args.hpc_ratio[0])/100
hpc_pcompl_th = float(args.hpc_pcompl_th[0])/100
morphing_per = float(args.morph_per[0])/100
#hpc_noise = float(args.hpc_noise[0])/100
#mec_noise = float(args.mec_noise[0])/100
#input_noise = float(args.input_noise[0])/100
lrate_hpc_mec = float(args.lrate_hpc_mec[0])/1000
lrate_mec_hpc = float(args.lrate_mec_hpc[0])/1000
lrate_lec_hpc = float(args.lrate_lec_hpc[0])/1000
theta_cycles = args.theta_cycles[0]
arena_runs = args.arena_runs[0]
pre_runs = args.pre_runs[0]
simulation_num = 65;
listofvalues = [ct,args.seed_input[0],args.seed_www[0],args.seed_path[0],args.theta_cycles[0],args.arena_runs[0],args.pre_runs[0],args.lrate_hpc_mec[0],args.lrate_mec_hpc[0],args.lrate_lec_hpc[0],args.mec_ratio[0],args.hpc_ratio[0],args.hpc_pcompl_th[0],args.morph_per[0]]
filenames = rfn.remappingFileNames(envir)
filenames.prepareSimulation(listofvalues,simulation_num)
if (tokill == "no"):
try:
#tosee = 0;
with gzip.open(filenames.fileRunPickle(listofvalues,simulation_num,0)+'z', 'rb') as ff:
print("File exist. Will exit!")
torun = 0;
#with gzip.open(filenames.fileRunPickle(listofvalues,simulation_num,1)+'z', 'rb') as ff:
# corrVectMECGRID1,corrVectHPCGRID1,corrVectMEC1,corrVectHPC1,corrVectMECvsGRID1 = pickle.load(ff)
#with gzip.open(filenames.fileRunPickle(listofvalues,simulation_num,2)+'z', 'rb') as ff:
# tosee = tosee + 1
#with gzip.open(filenames.fileRunPickle(listofvalues,simulation_num,3)+'z', 'rb') as ff:
# tosee = tosee + 1
#with gzip.open(filenames.fileRunPickle(listofvalues,simulation_num,4)+'z', 'rb') as ff:
# tosee = tosee + 1
#with gzip.open(filenames.fileRunPickle(listofvalues,simulation_num,5)+'z', 'rb') as ff:
# tosee = tosee + 1
#if(corrVectHPC1[-1]!=-1):
# print("File exist. Will exit!")
# torun = 0;
#else:
# print("File incomplete. Will do!")
# torun = 1;
except:
print("File does not existe. Will do!")
print("... %s" % (filenames.fileRunPickle(listofvalues,simulation_num,0)))
torun = 1;
else:
print("Will do anyway!")
torun = 0;
if(torun == 0):
sys.exit();
# %%
arena_binsize = [4,4]
context_per = 0
#morphing_per = 0
lec_numcells = 500
#mec_blocks = 50
hpc_numcells = 5000
np.random.seed(seed_input)
lec_numcells = 500
lec_activity = []
lec_activity.append(pow(np.random.uniform(0,1,(lec_numcells,arena_binsize[0],arena_binsize[1])),2))
lec_activity.append(pow(np.random.uniform(0,1,(lec_numcells,arena_binsize[0],arena_binsize[1])),2))
lec_type = np.random.uniform(0,1,(lec_numcells,arena_binsize[0],arena_binsize[1]))
lec_type[lec_type>(1-context_per)] = 1
lec_type[lec_type<morphing_per] = 0
lec_type[np.logical_and(lec_type<=(1-context_per),lec_type>=morphing_per)]=2
lec_change = np.random.uniform(0,1,(lec_numcells,arena_binsize[0],arena_binsize[1]))
hpc_memories = []
mec_blocksize = [2,4,6,8,10,12,14,16]
mec_blocks = len(mec_blocksize)
mec_numcells = np.sum(np.power(mec_blocksize,2))
mec_indexlist = []
init_val = 0
for ii in arange(mec_blocks):
mec_indexlist.append((init_val+arange(pow(mec_blocksize[ii],2))).reshape((mec_blocksize[ii],mec_blocksize[ii])))
init_val = np.max(mec_indexlist[ii])+1
del(init_val)
#lec_noise_activity = pow(np.random.uniform(0,1,(lec_numcells,arena_binsize[0],arena_binsize[1])),2)
#lec_noise_change = np.random.uniform(0,1,(lec_numcells,arena_binsize[0],arena_binsize[1]))
np.random.seed(seed_path)
xxx,yyy = np.meshgrid(arange(arena_binsize[0]),arange(arena_binsize[1]))
xxx = xxx.ravel()
popo = []
for ii in arange(100):
popo.append(np.random.permutation(len(xxx)))
for ii in arange(100):
while(popo[ii][0]==5 | popo[ii][0]==6 | popo[ii][0]==9 | popo[ii][0]==10 ):
ttt = popo[ii][0]
ttt2 = np.random.randint(len(xxx))
popo[ii][0] = popo[ii][ttt2]
popo[ii][ttt2] = ttt
np.random.seed(seed_www)
lec_hpc_weights_mean = 1
#lec_hpc_weights = np.random.uniform(0,1,(lec_numcells,hpc_numcells))
lec_hpc_weights = np.random.lognormal(1.0,1.0,(lec_numcells,hpc_numcells))
lec_hpc_weights[lec_hpc_weights<0] = 0
lec_hpc_weights = normalize_weight(lec_hpc_weights,lec_hpc_weights_mean)
mec_hpc_weights_mean = 1
#mec_hpc_weights = np.random.uniform(0,1,(mec_numcells,hpc_numcells))
mec_hpc_weights = np.random.lognormal(1.0,1.0,(mec_numcells,hpc_numcells))
mec_hpc_weights[mec_hpc_weights<0] = 0
mec_hpc_weights = normalize_weight(mec_hpc_weights,mec_hpc_weights_mean)
hpc_mec_weights_mean = 1
hpc_mec_weights = np.random.lognormal(1.0,1.0,(hpc_numcells,mec_numcells))
#hpc_mec_weights = np.random.uniform(0,1,(hpc_numcells,mec_numcells))
hpc_mec_weights[hpc_mec_weights<0] = 0
hpc_mec_weights = normalize_weight(hpc_mec_weights,hpc_mec_weights_mean)
current_emax = 0.90
current_emax_plast = 0
current_lrate_hpc_mec = lrate_hpc_mec
current_lrate_mec_hpc = lrate_mec_hpc
current_lrate_lec_hpc = lrate_lec_hpc
lec_hpc_weights_mean = 1
mec_hpc_weights_mean = 1
hpc_mec_weights_mean = 1
lec_noise = 0
mec_noise = 0
hpc_noise = 0
# %% PREPARE-PRE-LEARN
lllf = [1.0,1.0]
mooo = [mec_ratio,mec_ratio]
shape_vec = [0.0,1.0]
context_vec = [0.0,0.0]
pzzz = [0,0]
# %% PRE-LEARN
for sessions in arange(pre_runs):
print("session %d of %d" % (sessions,pre_runs))
hhhr = hpc_ratio * (sessions/(pre_runs-1))
lllf[0] = 1.0
lllf[1] = 1.0
pzzz[0] = sessions + 16
pzzz[1] = sessions + 16 + pre_runs
lec_act_vect = []
mec_act_vect = []
hpc_act_vect = []
mec_inact_vect = np.zeros((len(shape_vec),mec_numcells,arena_binsize[0],arena_binsize[1]))
hpc_inact_vect = np.zeros((len(shape_vec),hpc_numcells,arena_binsize[0],arena_binsize[1]))
lec_inact_vect = np.zeros((len(shape_vec),lec_numcells,arena_binsize[0],arena_binsize[1]))
for ii in arange(len(shape_vec)):
print("shape %d of %d" % (ii,len(shape_vec)))
mec_ratio = mooo[ii]
lec_act = zeros((lec_numcells,arena_binsize[0],arena_binsize[1]))
hpc_act = zeros((hpc_numcells,arena_binsize[0],arena_binsize[1]))
mec_act = zeros((mec_numcells,arena_binsize[0],arena_binsize[1]))
xxx,yyy = np.meshgrid(arange(arena_binsize[0]),arange(arena_binsize[0]))
xxx = xxx.ravel()
yyy = yyy.ravel()
#ppp = np.random.permutation(len(xxx))
ppp = popo[pzzz[ii]]
xxx = xxx[ppp]
yyy = yyy[ppp]
#
# xxx = xxx + 4
if (lllf[ii]>0):
xxxr = []
yyyr = []
for arena_runss in arange(arena_runs):
xxxr = concatenate([xxx,xxxr])
yyyr = concatenate([yyy,yyyr])
xxx = xxxr
yyy = yyyr
if((conna == True) and (ii > 0)):
current_pos = current_pos - array((4,0))
else:
current_pos = array((xxx[0],yyy[0]))
current_hpc_activity = np.zeros(hpc_numcells)
if ((ii<1) or (conna == False)):
current_mec_activity = np.zeros(mec_numcells)
current_context = context_vec[ii]
current_shape = shape_vec[ii]
current_vector = lec_whichone(lec_type,lec_change,current_context,current_shape)
base_lec = np.zeros(current_vector.shape)
base_lec = lec_activity[0].copy()
base_lec[current_vector==2] = lec_activity[1][current_vector==2]
for pp in arange(len(xxx)):
print("aaa %d of %d" % (pp,len(xxx)))
current_pos_old = current_pos
current_pos = array((xxx[pp],yyy[pp]))
current_speed = current_pos - current_pos_old
current_lec_activity = base_lec[:,np.int0(current_pos[0]),np.int0(current_pos[1])]
current_lec_noise = np.random.uniform(0.0,lec_noise,current_lec_activity.shape)
current_mec_noise = np.random.uniform(0.0,mec_noise,current_mec_activity.shape)
current_hpc_noise = np.random.uniform(0.0,hpc_noise,current_hpc_activity.shape)
lec_inact_vect[ii,:,np.int0(xxx[pp]),np.int0(yyy[pp])] = current_lec_activity
for kk in arange(theta_cycles):
if (kk>0): current_speed = array((0,0))
current_mec_input = (current_mec_activity+current_mec_noise)
if (mec_ratio>0.0):
for jj in arange(mec_blocks):
gxx,gyy = meshgrid(arange(mec_blocksize[jj])+(-1)*current_speed[0],arange(mec_blocksize[jj])+(-1)*current_speed[1])
gyy[mod(divide(gxx-mod(gxx,mec_blocksize[jj]),mec_blocksize[jj]),2)>0] = gyy[mod(divide(gxx-mod(gxx,mec_blocksize[jj]),mec_blocksize[jj]),2)>0] + floor(mec_blocksize[jj]/2)
gxx = int0(mod(gxx,mec_blocksize[jj]))
gyy = int0(mod(gyy,mec_blocksize[jj]))
current_mec_input[mec_indexlist[jj]] = current_mec_input[mec_indexlist[jj]][gyy,gxx]
#mec_input_vect[ii,kk,mec_indexlist[jj],xxx[pp],yyy[pp]] = current_mec_input[mec_indexlist[jj]]
h_h = np.dot(current_hpc_activity+current_hpc_noise,hpc_mec_weights)
if(np.max(h_h)>0.0):
h_h = h_h/np.max(h_h)
h_h[isnan(h_h)] = 0.0
current_mec_input = (1-mec_ratio)*h_h + mec_ratio*current_mec_input
current_lec_noise = np.random.uniform(0.0,lec_noise,current_lec_activity.shape)
current_mec_noise = np.random.uniform(0.0,mec_noise,current_mec_activity.shape)
current_hpc_noise = np.random.uniform(0.0,hpc_noise,current_hpc_activity.shape)
for jj in arange(mec_blocks):
current_mec_activity[mec_indexlist[jj]] = (current_mec_input[mec_indexlist[jj]] - current_emax*np.max(current_mec_input[mec_indexlist[jj]]))
current_mec_activity[current_mec_activity<0] = 0.0
current_mec_activity[mec_indexlist[jj]] /= np.max(current_mec_activity[mec_indexlist[jj]])
current_mec_activity[isnan(current_mec_activity)] = 0.0
mec_inact_vect[ii,mec_indexlist[jj],np.int0(xxx[pp]),np.int0(yyy[pp])] = current_mec_activity[mec_indexlist[jj]]
h_l = np.dot(current_lec_activity+current_lec_noise,lec_hpc_weights)
h_l = h_l/np.max(h_l)
h_l[isnan(h_l)] = 0.0
if(hhhr>0):
h_m = np.dot(current_mec_activity+current_mec_noise,mec_hpc_weights)
if(np.max(h_m)>0.0):
h_m = h_m/np.max(h_m)
h_m[isnan(h_m)] = 0.0
current_hpc_input = (1-hhhr)*h_l + hhhr*h_m
else:
current_hpc_input = h_l;
#hpc_input_vect[ii,kk,:,xxx[pp],yyy[pp]] = current_hpc_input
#hpc_input_vect[ii,kk,:,xxx[pp],yyy[pp]] = current_hpc_input
if (kk>0):
ddd = current_hpc_activity * 0
for mm in arange(len(hpc_memories)):
ccc = corrcoef(hpc_memories[mm],current_hpc_activity+current_hpc_noise)[0][1]
if ccc<hpc_pcompl_th:
ccc=0
else:
ddd += hpc_memories[mm] #ccc=1
# - current_hpc_activity
#ddd[ddd<0] = 0
#current_hpc_activity += ddd * ccc
if (np.max(ddd) > 0):
ddd = ddd/np.max(ddd)
ddd[isnan(ddd)] = 0.0
current_hpc_input = (1-mec_ratio)*current_hpc_input + mec_ratio*ddd
current_hpc_activity = (current_hpc_input - current_emax*np.max(current_hpc_input))
current_hpc_activity[current_hpc_activity<0] = 0.0
current_hpc_activity /= np.max(current_hpc_activity)
current_hpc_activity[current_hpc_activity<current_emax_plast] = 0
# for mm in arange(len(hpc_memories)):
# ccc = corrcoef(hpc_memories[mm],current_hpc_activity+current_hpc_noise)[0][1]
# if ccc<hpc_pcompl_th:
# ccc=0
# else:
# ccc=1
# ddd = hpc_memories[mm] - current_hpc_activity
# ddd[ddd<0] = 0
# current_hpc_activity += ddd * ccc
hpc_inact_vect[ii,:,np.int0(xxx[pp]),np.int0(yyy[pp])] = current_hpc_activity
#hpc_inact_vect[ii,kk,:,xxx[pp],yyy[pp]] = current_hpc_activity
if (lllf[ii]>0):
#hpc_mec_weights_t = copy.copy(hpc_mec_weights)
#mec_hpc_weights_t = copy.copy(mec_hpc_weights)
#lec_hpc_weights_t = copy.copy(lec_hpc_weights)
lec_hpc_weights = normalize_weight(learn_weight(lec_hpc_weights,current_lec_activity+current_lec_noise,current_hpc_activity+current_hpc_noise,current_lrate_lec_hpc),lec_hpc_weights_mean)
mec_hpc_weights = normalize_weight(learn_weight(mec_hpc_weights,current_mec_activity+current_mec_noise,current_hpc_activity+current_hpc_noise,current_lrate_mec_hpc),mec_hpc_weights_mean)
hpc_mec_weights = normalize_weight(learn_weight(hpc_mec_weights,current_hpc_activity+current_hpc_noise,current_mec_activity+current_mec_noise,current_lrate_hpc_mec),hpc_mec_weights_mean)
#lec_hpc_weights = normalize_weight(learn_weight(lec_hpc_weights,current_lec_activity+current_lec_noise,current_hpc_activity+current_hpc_noise,current_lrate_lec_hpc),lec_hpc_weights_mean)
#paaa = 0
#pbbb = 0
#pccc = 0
#for pll in arange(400):
# paaa += np.corrcoef(mec_hpc_weights_t[:,pll],mec_hpc_weights[:,pll])[0,1]
# pbbb += np.corrcoef(hpc_mec_weights_t[:,pll],hpc_mec_weights[:,pll])[0,1]
# pccc += np.corrcoef(lec_hpc_weights_t[:,pll],lec_hpc_weights[:,pll])[0,1]
#mec_hpc_www_vect[ii,kk,pp] = paaa/400 #np.corrcoef(mec_hpc_weights_t.ravel(),mec_hpc_weights.ravel())[0,1] #np.mean(np.abs(mec_hpc_weights_t - mec_hpc_weights))
#hpc_mec_www_vect[ii,kk,pp] = pbbb/400#np.corrcoef(hpc_mec_weights_t.ravel(),hpc_mec_weights.ravel())[0,1] #np.mean(np.abs(hpc_mec_weights_t - hpc_mec_weights))
#lec_hpc_www_vect[ii,kk,pp] = pccc/400#np.corrcoef(lec_hpc_weights_t.ravel(),lec_hpc_weights.ravel())[0,1] #np.mean(np.abs(lec_hpc_weights_t - lec_hpc_weights))
if ((lllf[ii]>0) and (hpc_pcompl_th<1.0)):
hpc_memories.append(current_hpc_activity)
lec_act[:,np.int0(xxx[pp]),np.int0(yyy[pp])] = current_lec_activity
mec_act[:,np.int0(xxx[pp]),np.int0(yyy[pp])] = current_mec_activity
hpc_act[:,np.int0(xxx[pp]),np.int0(yyy[pp])] = current_hpc_activity
mec_act_vect.append(mec_act)
lec_act_vect.append(lec_act)
hpc_act_vect.append(hpc_act)
# %%
# 0 : learn #1
# 1 : learn #2
# 2-17 : try #1 grid cells
# 18-33 : try #1 regular
# 34-49 : try #2 grid cells
# 50-65 : try #1 regular
# 66-86 : morphing
# 87-107 : morphing-meclesion
# 108-128 : morphing input nois 1%
# 129-149 : morphing input nois 5%
# 150-170 : morphing input nois 10%
# 171-191 : morphing input nois 20%
# mooo = 0.9999 * np.ones((108)) #[0.9999,0.9999,0.9999,0.9999,0.9999,0.9999,0.9999,0.9999]
# hooo = hpc_ratio * np.ones((108)) #[0.9999,0.9999,0.9999,0.9999,0.9999,0.9999,0.9999,0.9999]
# shape_vec = 0.0 * np.ones((108)) # [0.0,0.0, 0.0,0.0,0.0,1.0,1.0,1.0 ]
# context_vec = 0.0 * np.ones((108)) #[0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.0,0.5]
# lllf = 0.0 * np.ones((108))#[1,1, 0,0,0,0,0,0,]
totaaa = 32
mooo = mec_ratio * np.ones((totaaa)) #[0.9999,0.9999,0.9999,0.9999,0.9999,0.9999,0.9999,0.9999]
hooo = hpc_ratio * np.ones((totaaa)) #[0.9999,0.9999,0.9999,0.9999,0.9999,0.9999,0.9999,0.9999]
shape_vec = 0.0 * np.ones((totaaa)) # [0.0,0.0, 0.0,0.0,0.0,1.0,1.0,1.0 ]
context_vec = 0.0 * np.ones((totaaa)) #[0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.0,0.5]
lllf = 0.0 * np.ones((totaaa))#[1,1, 0,0,0,0,0,0,]
pzzz = np.concatenate((np.arange(0,16),np.arange(0,16)))
#lllf[0] = 0.0
#mooo[0] = mec_ratio
#mooo[18:34] = mec_ratio
#lllf[1] = 0.0
#mooo[1] = mec_ratio
#mooo[50:67] = mec_ratio
#mooo[66:] = mec_ratio
#hooo[87:108] = 0.0
shape_vec[0:16] = 0.0
shape_vec[16:32] = 1.0
#shape_vec[66:87]=np.linspace(0.0,1.0,21)
#shape_vec[87:108]=np.linspace(0.0,1.0,21)
#shape_vec[108:129]=np.linspace(0.0,1.0,21)
#shape_vec[129:150]=np.linspace(0.0,1.0,21)
#shape_vec[150:171]=np.linspace(0.0,1.0,21)
#shape_vec[171:192]=np.linspace(0.0,1.0,21)
#nono = 0.0 * np.ones((totaaa))
#nono[108:129] = 0.01
#nono[129:150] = 0.05
#nono[150:171] = 0.10
#nono[171:192] = 0.20
# %%
#input_noise_vect = [0.0,0.05,0.10,0.15,0.20,0.25,0.30,0.35,0.40,0.45,0.50,0.55,0.60,0.65,0.7,0.75,0.8,0.85,0.9,0.95,0.99]
input_noise_vect = [0.30,0.35,0.40,0.45,0.50,0.55,0.60,0.65,0.7,0.75,0.8]
diffsa = [0.1,0.15,0.2]
input_noise_vect = input_noise_vect#[::-1]
#num_runsss = 0
input_noise_vect_cn = []
input_noise_vect_ed = []
for sessions in np.arange(len(diffsa)):
#num_runsss += (sessions+1)
input_noise_vect_ed.append((input_noise_vect))
input_noise_vect_cn.append((input_noise_vect+np.ones(len(input_noise_vect))*diffsa[sessions]))
input_noise_vect_ed = np.concatenate(input_noise_vect_ed)#[::-1]
input_noise_vect_cn = np.concatenate(input_noise_vect_cn)#[::-1]
#input_noise_vect_ed = [input_noise_vect[0:(ii+1)] in for ii in np.arange(len(input_noise_vect)]
#touse = np.where(np.mod(input_noise_vect_ed*100,10)!=5)
#input_noise_vect_ed = input_noise_vect_ed[touse]
#input_noise_vect_cn = input_noise_vect_cn[touse]
#input_noise_vect_cn = [0.0,0.10,0.20,0.30,0.40,0.50,0.60,0.7,0.8,0.9,0.99]
# retirar o morphing pra ir mais rapdio... considerar todos os tipos de variaçnao do input
num_runsss = len(input_noise_vect_ed)
#for sessions in np.arange(len(input_noise_vect_ed)):
# num_runsss += (sessions+1)
stbCeHPC = -1* np.ones(num_runsss)
stbEdHPC = -1* np.ones(num_runsss)
stbAlHPC = -1* np.ones(num_runsss)
stbCeMEC = -1* np.ones(num_runsss)
stbEdMEC = -1* np.ones(num_runsss)
stbAlMEC = -1* np.ones(num_runsss)
stbCeLEC = -1* np.ones(num_runsss)
stbEdLEC = -1* np.ones(num_runsss)
stbAlLEC = -1* np.ones(num_runsss)
#pvCorrCeHPC = -1*ones((num_runsss))
#pvCorrEdHPC = -1*ones((num_runsss))
#pvCorrAlHPC = -1*ones((num_runsss))
#pvCorrelationCurveMEC1 = -1*ones((num_runsss))
#pvCorrelationCurveHPC2 = -1*ones((num_runsss))
#pvCorrelationCurveMEC2 = -1*ones((num_runsss))
#pvCorrelationCurveHPC = -1*ones((num_runsss))
#pvCorrelationCurveMEC = -1*ones((num_runsss))
for sessions in np.arange(num_runsss):
nono_cn = input_noise_vect_cn[sessions] * np.ones((totaaa))
nono_ed = input_noise_vect_ed[sessions] * np.ones((totaaa))
print("session %d of %d" % (sessions,num_runsss))
#if(sessions==0):
# lllf[0] = 0.0
# lllf[1] = 0.0
#else:
# lllf[0] = 1.0
# lllf[1] = 1.0
#pzzz[0] = sessions + 16 + pre_runs
#pzzz[1] = sessions + 16 + pre_runs + num_runsss
#pzzz[0] = 16 + pre_runs
#pzzz[1] = 16 + pre_runs + 1
lec_act_vect = []
mec_act_vect = []
hpc_act_vect = []
mec_inact_vect = np.zeros((len(shape_vec),mec_numcells,arena_binsize[0],arena_binsize[1]))
hpc_inact_vect = np.zeros((len(shape_vec),hpc_numcells,arena_binsize[0],arena_binsize[1]))
lec_inact_vect = np.zeros((len(shape_vec),lec_numcells,arena_binsize[0],arena_binsize[1]))
for ii in arange(len(shape_vec)):
print("shape %d of %d" % (ii,len(shape_vec)))
mec_ratio = mooo[ii]
hpc_ratio = hooo[ii]
lec_act = zeros((lec_numcells,arena_binsize[0],arena_binsize[1]))
mec_act = zeros((mec_numcells,arena_binsize[0],arena_binsize[1]))
hpc_act = zeros((hpc_numcells,arena_binsize[0],arena_binsize[1]))
xxx,yyy = np.meshgrid(arange(arena_binsize[0]),arange(arena_binsize[0]))
xxx = xxx.ravel()
yyy = yyy.ravel()
#ppp = np.random.permutation(len(xxx))
ppp = popo[pzzz[ii]]
xxx = xxx[ppp]
yyy = yyy[ppp]
if((conna == True) and (ii == 1)):
current_pos = current_pos - array((5,0))
else:
current_pos = array((xxx[0],yyy[0]))
current_hpc_activity = np.zeros(hpc_numcells)
if ((ii!=1) or (conna == False)):
current_mec_activity = np.zeros(mec_numcells)
#current_pos = array((xxx[0],yyy[0]))
current_hpc_activity = np.zeros(hpc_numcells)
current_mec_activity = np.zeros(mec_numcells)
current_context = context_vec[ii]
current_shape = shape_vec[ii]
current_vector = lec_whichone(lec_type,lec_change,current_context,current_shape)
base_lec = np.zeros(current_vector.shape)
base_lec = lec_activity[0].copy()
base_lec[current_vector==2] = lec_activity[1][current_vector==2]
#set the random seed
#np.random.seed(seed_path+shape_vec[ii])
np.random.seed(seed_path+ii)
#if (nono[ii]>0.0):
#lec_noise_change = np.random.uniform(0,1,(lec_numcells,arena_binsize[0],arena_binsize[1]))
for pp in arange(len(xxx)):
print("aaa %d of %d" % (pp,len(xxx)))
current_pos_old = current_pos
current_pos = array((xxx[pp],yyy[pp]))
current_speed = current_pos - current_pos_old
current_lec_activity = base_lec[:,current_pos[0],current_pos[1]]
if((current_pos[0]>0) & (current_pos[0]<3) & (current_pos[1]>0) & (current_pos[1]<3)):
ttt = floor(lec_numcells*nono_cn[ii])
else:
ttt = floor(lec_numcells*nono_ed[ii])
dela = np.random.permutation(len(current_lec_activity))
current_lec_activity[dela[:ttt]] = pow(np.random.uniform(0,1,(ttt)),2)
#base_lec[:ttt,:,:] = pow(np.random.uniform(0,1,(ttt,arena_binsize[0],arena_binsize[1])),2)
current_lec_noise = np.random.uniform(0.0,lec_noise,current_lec_activity.shape)
current_mec_noise = np.random.uniform(0.0,mec_noise,current_mec_activity.shape)
current_hpc_noise = np.random.uniform(0.0,hpc_noise,current_hpc_activity.shape)
lec_inact_vect[ii,:,xxx[pp],yyy[pp]] = current_lec_activity
for kk in arange(theta_cycles):
if (kk>0): current_speed = array((0,0))
current_mec_input = (current_mec_activity+current_mec_noise)
if (mec_ratio>0.0):
for jj in arange(mec_blocks):
gxx,gyy = meshgrid(arange(mec_blocksize[jj])+(-1)*current_speed[0],arange(mec_blocksize[jj])+(-1)*current_speed[1])
gyy[mod(divide(gxx-mod(gxx,mec_blocksize[jj]),mec_blocksize[jj]),2)>0] = gyy[mod(divide(gxx-mod(gxx,mec_blocksize[jj]),mec_blocksize[jj]),2)>0] + floor(mec_blocksize[jj]/2)
gxx = int0(mod(gxx,mec_blocksize[jj]))
gyy = int0(mod(gyy,mec_blocksize[jj]))
current_mec_input[mec_indexlist[jj]] = current_mec_input[mec_indexlist[jj]][gyy,gxx]
#mec_input_vect[ii,kk,mec_indexlist[jj],xxx[pp],yyy[pp]] = current_mec_input[mec_indexlist[jj]]
h_h = np.dot(current_hpc_activity+current_hpc_noise,hpc_mec_weights)
if(np.max(h_h)>0.0):
h_h = h_h/np.max(h_h)
h_h[isnan(h_h)] = 0.0
current_mec_input = (1-mec_ratio)*h_h + mec_ratio*current_mec_input
current_lec_noise = np.random.uniform(0.0,lec_noise,current_lec_activity.shape)
current_mec_noise = np.random.uniform(0.0,mec_noise,current_mec_activity.shape)
current_hpc_noise = np.random.uniform(0.0,hpc_noise,current_hpc_activity.shape)
for jj in arange(mec_blocks):
current_mec_activity[mec_indexlist[jj]] = (current_mec_input[mec_indexlist[jj]] - current_emax*np.max(current_mec_input[mec_indexlist[jj]]))
current_mec_activity[current_mec_activity<0] = 0.0
current_mec_activity[mec_indexlist[jj]] /= np.max(current_mec_activity[mec_indexlist[jj]])
current_mec_activity[isnan(current_mec_activity)] = 0.0
mec_inact_vect[ii,mec_indexlist[jj],xxx[pp],yyy[pp]] = current_mec_activity[mec_indexlist[jj]]
h_l = np.dot(current_lec_activity+current_lec_noise,lec_hpc_weights)
h_l = h_l/np.max(h_l)
h_l[isnan(h_l)] = 0.0
h_m = np.dot(current_mec_activity+current_mec_noise,mec_hpc_weights)
if(np.max(h_m)>0.0):
h_m = h_m/np.max(h_m)
h_m[isnan(h_m)] = 0.0
current_hpc_input = (1-hpc_ratio)*h_l + hpc_ratio*h_m
#hpc_input_vect[ii,kk,:,xxx[pp],yyy[pp]] = current_hpc_input
current_hpc_activity = (current_hpc_input - current_emax*np.max(current_hpc_input))
current_hpc_activity[current_hpc_activity<0] = 0.0
current_hpc_activity /= np.max(current_hpc_activity)
current_hpc_activity[current_hpc_activity<current_emax_plast] = 0
#hpc_input_vect[ii,kk,:,xxx[pp],yyy[pp]] = current_hpc_input
if (kk>0):
ddd = current_hpc_activity * 0
for mm in arange(len(hpc_memories)):
ccc = corrcoef(hpc_memories[mm],current_hpc_activity+current_hpc_noise)[0][1]
if ccc<hpc_pcompl_th:
ccc=0
else:
ddd += hpc_memories[mm] #ccc=1
# - current_hpc_activity
#ddd[ddd<0] = 0
#current_hpc_activity += ddd * ccc
if (np.max(ddd) > 0):
ddd = ddd/np.max(ddd)
ddd[isnan(ddd)] = 0.0
current_hpc_input = (1-mec_ratio)*current_hpc_input + mec_ratio*ddd
# for mm in arange(len(hpc_memories)):
# ccc = corrcoef(hpc_memories[mm],current_hpc_activity+current_hpc_noise)[0][1]
# if ccc<hpc_pcompl_th:
# ccc=0
# else:
# ccc=1
# ddd = hpc_memories[mm] - current_hpc_activity
# ddd[ddd<0] = 0
# current_hpc_activity += ddd * ccc
hpc_inact_vect[ii,:,xxx[pp],yyy[pp]] = current_hpc_activity
#hpc_inact_vect[ii,kk,:,xxx[pp],yyy[pp]] = current_hpc_activity
#if (lllf[ii]>0):
#hpc_mec_weights_t = copy.copy(hpc_mec_weights)
#mec_hpc_weights_t = copy.copy(mec_hpc_weights)
#lec_hpc_weights_t = copy.copy(lec_hpc_weights)
# lec_hpc_weights = normalize_weight(learn_weight(lec_hpc_weights,current_lec_activity+current_lec_noise,current_hpc_activity+current_hpc_noise,current_lrate_lec_hpc),lec_hpc_weights_mean)
# mec_hpc_weights = normalize_weight(learn_weight(mec_hpc_weights,current_mec_activity+current_mec_noise,current_hpc_activity+current_hpc_noise,current_lrate_mec_hpc),mec_hpc_weights_mean)
# hpc_mec_weights = normalize_weight(learn_weight(hpc_mec_weights,current_hpc_activity+current_hpc_noise,current_mec_activity+current_mec_noise,current_lrate_hpc_mec),hpc_mec_weights_mean)
#lec_hpc_weights = normalize_weight(learn_weight(lec_hpc_weights,current_lec_activity+current_lec_noise,current_hpc_activity+current_hpc_noise,current_lrate_lec_hpc),lec_hpc_weights_mean)
#paaa = 0
#pbbb = 0
#pccc = 0
#for pll in arange(400):
# paaa += np.corrcoef(mec_hpc_weights_t[:,pll],mec_hpc_weights[:,pll])[0,1]
# pbbb += np.corrcoef(hpc_mec_weights_t[:,pll],hpc_mec_weights[:,pll])[0,1]
# pccc += np.corrcoef(lec_hpc_weights_t[:,pll],lec_hpc_weights[:,pll])[0,1]
#mec_hpc_www_vect[ii,kk,pp] = paaa/400 #np.corrcoef(mec_hpc_weights_t.ravel(),mec_hpc_weights.ravel())[0,1] #np.mean(np.abs(mec_hpc_weights_t - mec_hpc_weights))
#hpc_mec_www_vect[ii,kk,pp] = pbbb/400#np.corrcoef(hpc_mec_weights_t.ravel(),hpc_mec_weights.ravel())[0,1] #np.mean(np.abs(hpc_mec_weights_t - hpc_mec_weights))
#lec_hpc_www_vect[ii,kk,pp] = pccc/400#np.corrcoef(lec_hpc_weights_t.ravel(),lec_hpc_weights.ravel())[0,1] #np.mean(np.abs(lec_hpc_weights_t - lec_hpc_weights))
#if ((lllf[ii]>0) and (hpc_pcompl_th<1.0)):
# hpc_memories.append(current_hpc_activity)
lec_act[:,xxx[pp],yyy[pp]] = current_lec_activity
mec_act[:,xxx[pp],yyy[pp]] = current_mec_activity
hpc_act[:,xxx[pp],yyy[pp]] = current_hpc_activity
mec_act_vect.append(mec_act)
lec_act_vect.append(lec_act)
hpc_act_vect.append(hpc_act)
# 0 : learn #1
# 1 : learn #2
# 2-17 : try #1 grid cells
# 18-33 : try #1 regular
# 34-49 : try #2 grid cells
# 50-65 : try #1 regular
#oooCa = np.zeros((16,16))
#oooEa = np.zeros((16,16))
#oooAa = np.zeros((16,16))
#oooCb = np.zeros((16,16))
#oooEb = np.zeros((16,16))
#oooAb = np.zeros((16,16))
oooC = []
oooE = []
oooA = []
#oooC = []
#oooE = []
#oooA = []
for xx in arange(16):
for yy in arange(xx,16):
#pfdist1 = pfdist1 + np.histogram(np.sum(np.sum(hpc_inact_vect[xx+18,:,:,:]>0,axis=1),axis=1),arange(17))[0]
#pfdist2 = pfdist2 + np.histogram(np.sum(np.sum(hpc_inact_vect[xx+50,:,:,:]>0,axis=1),axis=1),arange(17))[0]
vvvC = []
vvvE = []
for ii in arange(arena_binsize[0]):
for jj in arange(arena_binsize[1]):
if((ii>0) & (ii<3) & (jj>0) & (jj<3)):
vvvC.append(np.corrcoef(hpc_inact_vect[xx,:,ii,jj],hpc_inact_vect[yy,:,ii,jj])[0,1])
vvvC.append(np.corrcoef(hpc_inact_vect[xx+16,:,ii,jj],hpc_inact_vect[yy+16,:,ii,jj])[0,1])
else:
vvvE.append(np.corrcoef(hpc_inact_vect[xx,:,ii,jj],hpc_inact_vect[yy,:,ii,jj])[0,1])
vvvE.append(np.corrcoef(hpc_inact_vect[xx+16,:,ii,jj],hpc_inact_vect[yy+16,:,ii,jj])[0,1])
#vvvC = np.concatenate(vvvC)
#vvvE = np.concatenate(vvvE)
vvvA = np.concatenate((vvvE,vvvC))
oooC.append(np.mean(vvvC))
oooE.append(np.mean(vvvE))
oooA.append(np.mean(vvvA))
stbCeHPC[sessions] = np.mean(oooC)
stbEdHPC[sessions] = np.mean(oooE)
stbAlHPC[sessions] = np.mean(oooA)
oooC = []
oooE = []
oooA = []
#oooC = []
#oooE = []
#oooA = []
for xx in arange(16):
for yy in arange(xx,16):
#pfdist1 = pfdist1 + np.histogram(np.sum(np.sum(hpc_inact_vect[xx+18,:,:,:]>0,axis=1),axis=1),arange(17))[0]
#pfdist2 = pfdist2 + np.histogram(np.sum(np.sum(hpc_inact_vect[xx+50,:,:,:]>0,axis=1),axis=1),arange(17))[0]
vvvC = []
vvvE = []
for ii in arange(arena_binsize[0]):
for jj in arange(arena_binsize[1]):
if((ii>0) & (ii<3) & (jj>0) & (jj<3)):
vvvC.append(np.corrcoef(mec_inact_vect[xx,:,ii,jj],mec_inact_vect[yy,:,ii,jj])[0,1])
vvvC.append(np.corrcoef(mec_inact_vect[xx+16,:,ii,jj],mec_inact_vect[yy+16,:,ii,jj])[0,1])
else:
vvvE.append(np.corrcoef(mec_inact_vect[xx,:,ii,jj],mec_inact_vect[yy,:,ii,jj])[0,1])
vvvE.append(np.corrcoef(mec_inact_vect[xx+16,:,ii,jj],mec_inact_vect[yy+16,:,ii,jj])[0,1])
#vvvC = np.concatenate(vvvC)
#vvvE = np.concatenate(vvvE)
vvvA = np.concatenate((vvvE,vvvC))
oooC.append(np.mean(vvvC))
oooE.append(np.mean(vvvE))
oooA.append(np.mean(vvvA))
stbCeMEC[sessions] = np.mean(oooC)
stbEdMEC[sessions] = np.mean(oooE)
stbAlMEC[sessions] = np.mean(oooA)
for xx in arange(16):
for yy in arange(xx,16):