def learning(task, ncues = 2, trial_n=0, learn=True, debugging=True, debugging_arm=False, debugging_learning = False, duration=duration):
trial(task, ncues=ncues, trial_n=trial_n, learn=learn, debugging=debugging, debugging_arm=debugging_arm, duration=duration)
if debugging_learning:
debug_learning(connections["CTX.cog -> CTX.ass"].weights, connections["CTX.mot -> CTX.ass"].weights,
connections["CTX.cog -> STR.cog"].weights, CUE["value"])
return
def learning(inverse=False,
inverse_all=True,
f=None,
trial_n=0,
debugging=True,
protocol='Piron',
familiar=True,
learn=True,
hist=False,
P=[],
D=[],
mBc=[],
ABC=[],
NoMove=[],
RT=[],
RP=None,
AP=None):
if hist:
histor, time = trial(inverse=inverse,
inverse_all=inverse_all,
f=f,
trial_n=trial_n,
learn=learn,
protocol=protocol,
hist=hist,
familiar=familiar,
debugging=debugging,
P=P,
D=D,
RP=RP,
AP=AP,
mBc=mBc,
ABC=ABC,
NoMove=NoMove)
else:
time = trial(inverse=inverse,
inverse_all=inverse_all,
f=f,
trial_n=trial_n,
learn=learn,
protocol=protocol,
hist=hist,
familiar=familiar,
debugging=debugging,
P=P,
D=D,
RP=RP,
AP=AP,
mBc=mBc,
ABC=ABC,
NoMove=NoMove)
RT.append(time)
if not len(P) == trial_n + 1:
P.append(0)
if hist:
return histor, RT, P, D, RP, AP, mBc, ABC, NoMove
else:
return RT, P, D, RP, AP, mBc, ABC, NoMove
def learning(reverse = False, reverse_all = True, f = None, trial_n = 0, debugging = True, protocol = 'Piron', familiar = True, learn = True, hist = False, P = [], D = [], mBc = [], ABC = [], NoMove = [], RT = [], RP = None, AP = None): if hist: histor, time = trial(reverse = reverse, reverse_all = reverse_all, f = f, trial_n = trial_n, learn = learn, protocol = protocol, hist = hist, familiar = familiar, debugging = debugging, P = P, D = D, RP = RP, AP = AP, mBc = mBc, ABC = ABC, NoMove = NoMove) else: time = trial(reverse = reverse, reverse_all = reverse_all, f = f, trial_n = trial_n, learn = learn, protocol = protocol, hist = hist, familiar = familiar, debugging = debugging, P = P, D = D, RP = RP, AP = AP, mBc = mBc, ABC = ABC, NoMove = NoMove) RT.append(time) if not len(P) == trial_n+1: P.append(0) if hist: return histor, RT, P, D, RP, AP, mBc, ABC, NoMove else: return RT, P, D, RP, AP, mBc, ABC, NoMove
def testing_trials(f=None,
trials=n_trials,
debugging=True,
save=False,
protocol='Piron',
familiar=True,
hist=False):
P, D, mBc, ABC, NoMove, RT = [], [], [], [], [], []
AP = np.zeros(n)
for j in range(trials):
if debugging:
print 'Trial: ', j + 1
if hist:
histor, time = trial(f=f,
trial_n=j,
learn=False,
protocol=protocol,
hist=hist,
familiar=familiar,
debugging=debugging,
P=P,
D=D,
AP=AP,
mBc=mBc,
ABC=ABC,
NoMove=NoMove)
else:
time = trial(f=f,
trial_n=j,
learn=False,
protocol=protocol,
hist=hist,
familiar=familiar,
debugging=debugging,
P=P,
D=D,
AP=AP,
mBc=mBc,
ABC=ABC,
NoMove=NoMove)
RT.append(time)
if not len(P) == j + 1:
P.append(0)
debug(f=f, P=P, D=D, AP=AP, mBc=mBc, ABC=ABC, NoMove=NoMove)
print
if save:
return P, RT, np.array(D).mean(), AP, np.array(mBc).mean(), np.array(
ABC).mean(), len(NoMove) / float(trials)
if hist:
return histor
def learning(task, trial_n = 0, learn = True, debugging = True): time = trial(task, trial_n = trial_n, learn = learn, debugging = debugging) if debugging and learn: debug_learning(connections["CTX.cog -> CTX.ass"].weights, connections["CTX.mot -> CTX.ass"].weights, connections["CTX.cog -> STR.cog"].weights, CUE["value"]) return
def learning_trials(task, trials=n_learning_positions_trials, debugging=True, debug_simulation=False, duration=duration):
if debug_simulation:
steps = trials / 145
print(' Starting ', end=' ')
for i in range(trials):
trial(task, duration=duration, trial_n = i, debugging=debugging, wholeFig=True)
if debug_simulation:
if i % steps == 0:
print('\b.', end=' ')
sys.stdout.flush()
if debug_simulation:
print(' Done!')
return
def testing_trials(f = None, trials = n_trials, debugging = True, save = False, protocol = 'Piron', familiar = True, hist = False): P, D, mBc, ABC, NoMove, RT = [], [], [], [], [], [] AP = np.zeros(n) for j in range(trials): if debugging: print 'Trial: ', j + 1 if hist: histor, time = trial(f = f, trial_n = j, learn = False, protocol = protocol, hist = hist, familiar = familiar, debugging = debugging, P = P, D = D, AP = AP, mBc = mBc, ABC = ABC, NoMove = NoMove) else: time = trial(f = f, trial_n = j, learn = False, protocol = protocol, hist = hist, familiar = familiar, debugging = debugging, P = P, D = D, AP = AP, mBc = mBc, ABC = ABC, NoMove = NoMove) RT.append(time) if not len(P) == j+1: P.append(0) debug(f = f, P = P, D = D, AP = AP, mBc = mBc, ABC = ABC, NoMove = NoMove) print if save: return P, RT, np.array(D).mean(), AP, np.array(mBc).mean(), np.array(ABC).mean(), len(NoMove)/float(trials) if hist: return histor
def learning(reverse = False, reverse_all = True, f = None, trial_n = 0, debugging = True, protocol = 'Piron', familiar = True, learn = True, hist = False, P = [], RT = [], ncues = 2):
if hist:
histor, time = trial(reverse = reverse, reverse_all = reverse_all, f = f, trial_n = trial_n, learn = learn, protocol = protocol, hist = hist, familiar = familiar, debugging = debugging, P = P, ncues = ncues)
else:
time = trial(reverse = reverse, reverse_all = reverse_all, f = f, trial_n = trial_n, learn = learn, protocol = protocol, hist = hist, familiar = familiar, debugging = debugging, P = P, ncues = ncues)
RT.append(time)
if not len(P) == trial_n+1:
P.append(0)
if hist:
print("Mean Cognitive Striatal activity: [", end=' ')
for i in range(n):
print(np.amax(histor["STR"]["cog"][:,i]), end=' ')
#print histor["STR"]["cog"][np.nonzero(histor["STR"]["cog"][:,i]),i].mean(),
#print histor["STR"]["cog"][:,i].mean(),
print("]")
print("Mean Motor Striatal activity: [", end=' ')
for i in range(n):
print(histor["STR"]["mot"][np.nonzero(histor["STR"]["mot"][:,i]),i].mean(), end=' ')
#print histor["STR"]["mot"][:,i].mean(),
print("]")
return histor, RT, P
else:
return RT, P
# ----------------------------------------------------------------------------- # Copyright (c) 2015, Meropi Topalidou # Distributed under the (new) BSD License. # # Contributors: Meropi Topalidou ([email protected]) # ----------------------------------------------------------------------------- # Evolution of single trial with Piron protocol # ----------------------------------------------------------------------------- if __name__ == "__main__": temp = '../cython/' import sys sys.path.append(temp) from model import * from display import * from trial import * reset(protocol = 'OneChoice') # Make GPi lesion #connections["GPI.cog -> THL.cog"].active = False #connections["GPI.mot -> THL.mot"].active = False global cues_cog, cues_mot cues_cog, cues_mot = trials_cues(protocol = 'OneChoice') #print cues_cog result = results(n_trials = 1) histor, time = trial(result, hist = True, debugging = True, protocol = 'OneChoice', wholeFig = True) if 1: display_ctx(histor, 3.0) if 0: display_ctx(histor, 3.0, "single-trial-NoBG.pdf") if 0: display_all(histor, 3.0)#, "single-trial-all.pdf")
temp = '../cython/'
import sys
sys.path.append(temp)
# model file build the structures and initialize the model
from model import *
from display import *
from trial import *
from task_a import Task_A
# 1 if there is presentation of cues else 0
cues_pres = 1
# Initialize the system
reset()
# Define the shapes and the positions that we'll be used to each trial
# n should be multiple of 6 because there are 6 valuable combinations of shapes and positions
task = Task_A(n=6)
# Compute a single trial
time = trial(task, cues_pres=cues_pres, wholeFig=True)
#retrieve the activity history of the structures
histor = history()
# Display cortical activity during the single trial
if 1: display_ctx(histor, 3.0)#, "single-trial.pdf")
# Display activity from all structures during the single trial
if 0: display_all(histor, 3.0) # , "single-trial-all.pdf")
try:
num_trials_input = input("Enter Num of trials or 'quit' to exit:")
if num_trials_input == 'quit':
break
else:
try:
num_trials = Num_trials(num_trials_input).num_trials
break
except InvalidInput:
print('Invalid Input!')
continue
except KeyboardInterrupt:
print('User Interrupt')
except EOFError:
print('Input Error')
f = open("results.txt","w")
for position in positions:
t = trial(position, num_trials)
t.plot_hist()
mean = t.mean_std()[0]
std = t.mean_std()[1]
f.write("Position = {}: mean = {:.4f}, std = {:.4f} \n".format(position, mean, std))
f.close()
from display import *
from trial import *
from task_b import Task_B
# 1 if there is presentation of cues else 0
cues_pres = 1
# Initialize the system
reset()
# Define the shapes and the positions that we'll be used to each trial
# n should be multiple of 6 because there are 6 valuable combinations of positions
task = Task_B(n=6)
GPi_lesion = False
# Make GPi lesion
if GPi_lesion:
connections["GPI.cog -> THL.cog"].active = False
connections["GPI.mot -> THL.mot"].active = False
# Compute a single trial
trial(task, debugging=True, cues_pres=cues_pres, wholeFig=True)
# Retrieve the activity history of the structures
histor = history()
# Display cortical activity during the single trial
if 1: display_ctx(histor, 3.0)#, "single-trial.pdf"), "single-trial-NoBG.pdf")#
# Display activity from all structures during the single trial
if 0: display_all(histor, 3.0) # , "single-trial-all.pdf")
# -*- coding: utf-8 -*- # ----------------------------------------------------------------------------- # Copyright (c) 2015, Meropi Topalidou # Distributed under the (new) BSD License. # # Contributors: Meropi Topalidou ([email protected]) # Nicolas Rougier ([email protected]) # ----------------------------------------------------------------------------- # Evolution of single trial with Guthrie protocol # ----------------------------------------------------------------------------- if __name__ == "__main__": from model import * from display import * from trial import * cues_pres = input('\nDo you want to present cues?\nChoose 1 for True or 0 for False\n') reset(protocol = 'Guthrie') # Make GPi lesion # connections["GPI.cog -> THL.cog"].active = False # connections["GPI.mot -> THL.mot"].active = False histor, time = trial(hist = True, debugging = True, protocol = 'Guthrie', cues_pres = cues_pres, wholeFig = True) if 1: display_ctx(histor, 3.0) if 0: display_ctx(histor, 3.0, "single-trial-NoBG.pdf") if 1: display_all(histor, 3.0)#, "single-trial-all.pdf")
def single_trial():
# 1 if there is presentation of cues else 0
cues_pres = 1
trials = 1
# Define the shapes and the positions that we'll be used to each trial
# n should be multiple of 6 because there are 6 valuable combinations of shapes and positions
task = Task_1ch(n=81*4)
# Compute a single trial
time = trial(task, cues_pres=cues_pres, ncues=1, duration=duration_learning_positions,
debugging=True, wholeFig=True) # trial_continuous_move #
print(("Moves : ", task.records[0]["moves"]))
print(("Time : ", time))
# retrieve the activity history of the structures
# histor = history()
# mot = histor["CTX"]["mot"][:time]
# pfc1 = histor["PFC"]["theta1"][:time]
# pfc2 = histor["PFC"]["theta2"][:time]
# sma1 = histor["SMA"]["theta1"][:time]
# sma2 = histor["SMA"]["theta2"][:time]
# arm1 = histor["ARM"]["theta1"][:time]
# arm2 = histor["ARM"]["theta2"][:time]
# ppc1 = histor["PPC"]["theta1"][:time]
# ppc2 = histor["PPC"]["theta2"][:time]
#
# plt.figure()
# plt.plot(mot)
# plt.title('Mot')
#
# plt.figure()
# plt.plot(arm1)
# plt.title('Arm1')
#
# plt.figure()
# plt.plot(arm2)
# plt.title('Arm2')
#
# plt.figure()
# plt.plot(sma1)
# plt.title('SMA1')
# plt.figure()
# plt.plot(sma2)
# plt.title('SMA2')
#
# plt.figure()
# plt.plot(pfc1)
# plt.title('PFC1')
# plt.figure()
# plt.plot(pfc2)
# plt.title('PFC2')
#
# plt.figure()
# plt.plot(ppc1)
# plt.title('PPC1')
#
# plt.figure()
# plt.plot(ppc2)
# plt.title('PPC2')
# plt.show()
# Display cortical activity during the single trial
if 0: display_ctx(histor, 3.0) # , "single-trial.pdf")
# Define the shapes and the positions that we'll be used to each trial
# n should be multiple of 6 because there are 6 valuable combinations of shapes and positions
task = Task_A(n=6)
folder = "../Results/Learn_Positions"
f = folder + "/Records.npy"
temp = np.load(f)
connections["PPC.theta1 -> PFC.theta1"].weights = temp["Wppc_pfc1"][-1]
connections["PFC.theta1 -> STR_PFC_PPC.theta1"].weights = temp["Wpfc_str1"][-1]
connections["PPC.theta1 -> STR_PFC_PPC.theta1"].weights = temp["Wppc_str1"][-1]
connections["PPC.theta2 -> PFC.theta2"].weights = temp["Wppc_pfc2"][-1]
connections["PFC.theta2 -> STR_PFC_PPC.theta2"].weights = temp["Wpfc_str2"][-1]
connections["PPC.theta2 -> STR_PFC_PPC.theta2"].weights = temp["Wppc_str1"][-1]
# Compute a single trial
time = trial(task, wholeFig=True, debugging=True)
print " Moves : ", task.records[0]["moves"]
# retrieve the activity history of the structures
histor = history()
ctx = histor["CTX"]["mot"][:time]
arm1 = histor["ARM"]["theta1"][:time]
arm2 = histor["ARM"]["theta2"][:time]
plt.figure()
plt.plot(arm1)
plt.title("Arm1")
plt.figure()
plt.plot(arm2)
plt.title("Arm2")
import sys
sys.path.append(
'/Users/mtopalid/Desktop/PhD/Models/Basal_Ganglia/Piron-et-al-2014/cython/'
)
from model import *
from display import *
from trial import *
reset(protocol='Piron')
#connections["CTX.cog -> STR.cog"].weights = np.array([ 0.75, 0.4761739, 0.45899682, 0.46005381])
histor, time = trial(hist=True, debugging=True, protocol='Piron')
if 1: display_ctx(histor, 3.0)
if 0: display_ctx(histor, 3.0, "single-trial.pdf")
if 0:
display_all(histor, 3.0, "single-trial-all.pdf")
sys.path.append(temp)
# model file build the structures and initialize the model
from model import *
from display import *
from trial import *
from task_1ch import Task_1ch
# 1 if there is presentation of cues else 0
cues_pres = 1
trials = 1
# Define the shapes and the positions that we'll be used to each trial
# n should be multiple of 6 because there are 6 valuable combinations of shapes and positions
task = Task_1ch(n=6)
# Compute a single trial
time = trial(task, cues_pres=cues_pres, ncues=1, duration=duration_learning_positions, debugging=True, wholeFig=True)
print "Moves : ", task.records[0]["moves"]
print time
# retrieve the activity history of the structures
histor = history()
pfc1 = histor["PFC"]["theta1"][:time]
pfc2 = histor["PFC"]["theta2"][:time]
sma1 = histor["SMA"]["theta1"][:time]
sma2 = histor["SMA"]["theta2"][:time]
arm1 = histor["ARM"]["theta1"][:time]
arm2 = histor["ARM"]["theta2"][:time]
ppc1 = histor["PPC"]["theta1"][:time]
ppc2 = histor["PPC"]["theta2"][:time]
plt.figure()
import sys
sys.path.append('/Users/mtopalid/Desktop/PhD/Models/Basal_Ganglia/Topalidou-et-al-2015/cython/')
from model import *
from display import *
from trial import *
reset(protocol = 'Guthrie')
#connections["CTX.cog -> STR.cog"].weights = np.array([ 0.75, 0.4761739, 0.45899682, 0.46005381])
histor, time = trial(hist = True, debugging = True, protocol = 'Guthrie')
if 0: display_ctx(histor, 3.0)
if 1: display_all(histor, 3.0)
if 0: display_ctx(histor, 3.0, "single-trial.pdf")
if 0: display_all(histor, 3.0, "single-trial-all.pdf")
def single_trial():
# 1 if there is presentation of cues else 0
# cues_pres = 1
# trials = 1
# Define the shapes and the positions that we'll be used to each trial
# n should be multiple of 6 because there are 6 valuable combinations of shapes and positions
task = Task()
# Compute a single trial
time = trial(task, duration=duration, debugging=True,
wholeFig=True)
# print "Moves : ", task.records[0]["moves"]
# print "Time : ", time
# retrieve the activity history of the structures
histor = history(dur=time)
ppc = histor["PPC"]["str"]#[:time]
sma = histor["SMA"]["str"]#[:time]
trg = histor["TARGET"]["str"]#[:time]
ism = histor["ISM"]["str"]#[:time]
m1in = histor["M1_in"]["str"]#[:time]
m1out = histor["M1_out"]["str"]#[:time]
str = histor["STR"]["str"]#[:time]
# stn = histor["STN"]["str"][:time]
thl = histor["THL"]["str"]#[:time]
# gpi = histor["GPI"]["str"][:time]
plt.figure(figsize=(18, 9))
plt.subplot(331)
plt.plot(ppc)
plt.ylim(ppc.min() - 10, ppc.max() + 10)
plt.xlim(-10, +5010)
plt.title('PPC')
plt.subplot(334)
plt.plot(sma[:, 0], c='b', label="Up")
plt.plot(sma[:, 1], c='r', label="Down")
plt.plot(sma[:, 2], c='g', label="Stay")
plt.plot(sma[:, 3], c='m', label="Right")
plt.plot(sma[:, 4], c='c', label="Left")
plt.legend()
plt.ylim(sma.min() - 10, sma.max() + 10)
# plt.xlim(-100, +5100)
plt.title('SMA')
plt.subplot(336)
plt.plot(trg[:, 0], c='b', label=1)
plt.plot(trg[:, 1], c='r', label=2)
plt.plot(trg[:, 2], c='g', label=3)
plt.plot(trg[:, 3], c='m', label=4)
plt.plot(trg[:, 4], c='c', label=5)
plt.plot(trg[:, 5], c='y', label=6)
plt.plot(trg[:, 6], c='k', label=7)
plt.plot(trg[:, 7], 'bo', label=8)
plt.plot(trg[:, 8], 'r*', label=9)
# plt.legend()
plt.title('Target')
#
#
plt.subplot(332)
plt.plot(m1in)
plt.title('M1in')
plt.subplot(335)
plt.plot(m1out[:, 0], c='b', label=0)
plt.plot(m1out[:, 1], c='r', label=1)
plt.plot(m1out[:, 2], c='g', label=2)
plt.plot(m1out[:, 3], c='m', label=3)
plt.plot(m1out[:, 4], c='c', label=4)
plt.plot(m1out[:, 5], c='y', label=5)
plt.plot(m1out[:, 6], c='k', label=6)
plt.plot(m1out[:, 7], 'bo', label=7)
plt.plot(m1out[:, 8], 'r*', label=8)
plt.title('M1out')
#
#
#
plt.subplot(333)
plt.plot(ism[:, 0], c='b', label=1)
plt.plot(ism[:, 1], c='r', label=2)
plt.plot(ism[:, 2], c='g', label=3)
plt.plot(ism[:, 3], c='m', label=4)
plt.plot(ism[:, 4], c='c', label=5)
plt.plot(ism[:, 5], c='y', label=6)
plt.plot(ism[:, 6], c='k', label=7)
plt.plot(ism[:, 7], 'bo', label=8)
plt.plot(ism[:, 8], 'r*', label=9)
plt.title('ISM')
plt.subplot(337)
init_pos = task.trials[0]["initial_pos_arm"]
prop = np.ones(trg.shape[0]) * init_pos
plt.plot(prop)
plt.title('Propioceptors')
# plt.subplot(337)
# plt.plot(stn)
# plt.title('stn')
# plt.ylim(stn.min()-10, stn.max()+10)
# plt.xlim(-100, +5100)
plt.subplot(338)
plt.plot(str)
plt.title('str')
plt.ylim(str.min() - 10, str.max() + 10)
# plt.xlim(-100, +5100)
plt.subplot(339)
plt.plot(thl)
plt.title('Thalamus')
plt.ylim(thl.min() - 10, thl.max() + 10)
# plt.xlim(-100, +5100)
# plt.subplot(335)
# plt.plot(gpi)
# plt.title('GPi')
# plt.ylim(gpi.min()-100, gpi.max()+100)
# plt.xlim(-100, +5100)
plt.show()
