R Kiani, TD Hanks, & MN Shadlen, JNS 2008.
http://dx.doi.org/10.1523/JNEUROSCI.4761-07.2008
"""
from __future__ import division
import numpy as np
from pyrl import tasktools
# Time step
dt = 1
# Inputs
inputs = tasktools.to_map('FIXATION', 'LEFT', 'RIGHT')
# Actions
actions = tasktools.to_map('FIXATE', 'CHOOSE-LEFT', 'CHOOSE-RIGHT')
# Trial conditions
left_rights = [-1, 1]
cohs = [0, 6.4, 12.8, 25.6, 51.2]
n_conditions = len(left_rights)*len(cohs)
# Training
n_gradient = n_conditions
n_validation = 100*n_conditions
# Input noise
sigma = np.sqrt(2*100*0.01)
A parametric working memory task, based on
Neuronal population coding of parametric working memory.
O. Barak, M. Tsodyks, & R. Romo, JNS 2010.
http://dx.doi.org/10.1523/JNEUROSCI.1875-10.2010
"""
from __future__ import division
import numpy as np
from pyrl import tasktools
# Inputs
inputs = tasktools.to_map('FIXATION', 'F-POS', 'F-NEG')
# Actions
actions = tasktools.to_map('FIXATE', '>', '<')
# Trial conditions
gt_lts = ['>', '<']
fpairs = [(18, 10), (22, 14), (26, 18), (30, 22), (34, 26)]
n_conditions = len(gt_lts) * len(fpairs)
# Training
n_gradient = n_conditions
n_validation = 20*n_conditions
# Slow down the learning
lr = 0.002
Representation of confidence associated with a decision by
neurons in the parietal cortex.
R. Kiani & M. N. Shadlen, Science 2009.
http://dx.doi.org/10.1126/science.1169405
"""
from __future__ import division
import numpy as np
from pyrl import tasktools
# Inputs
inputs = tasktools.to_map('FIXATION', 'LEFT', 'RIGHT', 'SURE')
# Actions
actions = tasktools.to_map('FIXATE', 'CHOOSE-LEFT', 'CHOOSE-RIGHT', 'CHOOSE-SURE')
# Trial conditions
wagers = [True, False]
left_rights = [-1, 1]
cohs = [0, 3.2, 6.4, 12.8, 25.6, 51.2]
n_conditions = len(wagers) * len(left_rights) * len(cohs)
# Training
n_gradient = n_conditions
n_validation = 50*n_conditions
# Input noise
Economic choice task, based on
Neurons in the orbitofrontal cortex encode economic value.
C Padoa-Schioppa & JA Assad, Nature 2006.
http://dx.doi.org/10.1038/nature04676
"""
from __future__ import division
import numpy as np
from pyrl import tasktools
# Inputs
inputs = tasktools.to_map('FIXATION', 'L-A', 'L-B', 'R-A', 'R-B', 'N-L', 'N-R')
# Actions
actions = tasktools.to_map('FIXATE', 'CHOOSE-LEFT', 'CHOOSE-RIGHT')
# Trial conditions
A_to_B = 4.1
juices = [('A', 'B'), ('B', 'A')]
offers = [(0, 1), (1, 2), (1, 1), (2, 1), (3, 1),
(4, 1), (6, 1), (10, 1), (3, 0)]
n_conditions = len(juices) * len(offers)
# Training
n_gradient = n_conditions
n_validation = 50*n_conditions
Multisensory integration, based on
A category-free neural population supports evolving demands during decision-making.
D Raposo, MT Kaufman, & AK Churchland, Nature Neurosci. 2014.
http://dx.doi.org/10.1038/nn.3865
"""
from __future__ import division
import numpy as np
from pyrl import tasktools
# Inputs
inputs = tasktools.to_map('FIXATION', 'VISUAL-P', 'VISUAL-N', 'AUDITORY-P',
'AUDITORY-N')
# Actions
actions = tasktools.to_map('FIXATE', 'CHOOSE-LOW', 'CHOOSE-HIGH')
# Trial conditions
mods = ['v', 'a', 'va']
freqs = range(9, 16 + 1)
n_conditions = len(mods) * len(freqs)
# Discrimination boundary
boundary = 12.5
# Training
n_gradient = n_conditions
n_validation = 50 * n_conditions
https://github.com/frsong/pyrl
and add it to your PYTHONPATH, then run the code below. Requires Theano 0.8.2.
"""
import numpy as np
import gym
from pyrl import tasktools
# Environment
env = gym.make('CartPole-v0')
# Inputs
inputs = tasktools.to_map(range(env.observation_space.high.size))
# Actions
actions = tasktools.to_map(range(env.action_space.n))
# Baseline bias
baseline_bout = 0
# Don't treat the last time point as special
abort_on_last_t = False
# Training
n_gradient = 1
n_validation = 0
# Network structure
Economic choice task, based on
Neurons in the orbitofrontal cortex encode economic value.
C Padoa-Schioppa & JA Assad, Nature 2006.
http://dx.doi.org/10.1038/nature04676
"""
from __future__ import division
import numpy as np
from pyrl import tasktools
# Inputs
inputs = tasktools.to_map('FIXATION', 'L-A', 'L-B', 'R-A', 'R-B', 'N-L', 'N-R')
# Actions
actions = tasktools.to_map('FIXATE', 'CHOOSE-LEFT', 'CHOOSE-RIGHT')
# Trial conditions
A_to_B = 4.1
juices = [('A', 'B'), ('B', 'A')]
offers = [(0, 1), (1, 2), (1, 1), (2, 1), (3, 1),
(4, 1), (6, 1), (10, 1), (3, 0)]
n_conditions = len(juices) * len(offers)
# Training
n_gradient = n_conditions
n_validation = 50*n_conditions
Context-dependent integration task, based on
Context-dependent computation by recurrent dynamics in prefrontal cortex.
V Mante, D Sussillo, KV Shinoy, & WT Newsome, Nature 2013.
http://dx.doi.org/10.1038/nature12742
"""
from __future__ import division
import numpy as np
from pyrl import tasktools
# Inputs
inputs = tasktools.to_map('MOTION', 'COLOR', 'MOTION-LEFT', 'MOTION-RIGHT',
'COLOR-LEFT', 'COLOR-RIGHT')
# Actions
actions = tasktools.to_map('FIXATE', 'CHOOSE-LEFT', 'CHOOSE-RIGHT')
# Trial conditions
contexts = ['m', 'c']
left_rights = [-1, 1]
cohs = [5, 15, 50]
n_conditions = len(contexts) * (len(left_rights) * len(cohs))**2
# Training
n_gradient = n_conditions
n_validation = 50 * n_conditions
# Input noise
"""
matching pennies task, based on
"""
from __future__ import division
import numpy as np
from pyrl import tasktools
import scipy.stats
# Inputs
inputs = tasktools.to_map('GO', 'L-R', 'R-R', 'L-N', 'R-N')
"""
input situations:
1. go cue
2. left reward
3. right reward
4. left no reward
5. right no reward
"""
# Actions
actions = tasktools.to_map('HOLD', 'CHOOSE-LEFT', 'CHOOSE-RIGHT')
# Trial conditions
#determine what to put here later
#A_to_B = 2.2
#juices = [('A', 'B'), ('B', 'A')]
A parametric working memory task, based on
Neuronal population coding of parametric working memory.
O. Barak, M. Tsodyks, & R. Romo, JNS 2010.
http://dx.doi.org/10.1523/JNEUROSCI.1875-10.2010
"""
from __future__ import division
import numpy as np
from pyrl import tasktools
# Inputs
inputs = tasktools.to_map('FIXATION', 'F-POS', 'F-NEG')
# Actions
actions = tasktools.to_map('FIXATE', '>', '<')
# Trial conditions
gt_lts = ['>', '<']
fpairs = [(18, 10), (22, 14), (26, 18), (30, 22), (34, 26)]
n_conditions = len(gt_lts) * len(fpairs)
# Training
n_gradient = n_conditions
n_validation = 20 * n_conditions
# Slow down the learning
lr = 0.002
https://github.com/frsong/pyrl
and add it to your PYTHONPATH, then run the code below. Requires Theano 0.8.2.
"""
import numpy as np
import gym
from pyrl import tasktools
# Environment
env = gym.make('CartPole-v0')
# Inputs
inputs = tasktools.to_map(range(env.observation_space.high.size))
# Actions
actions = tasktools.to_map(range(env.action_space.n))
# Baseline bias
baseline_bout = 0
# Don't treat the last time point as special
abort_on_last_t = False
# Training
n_gradient = 1
n_validation = 0
# Network structure
Context-dependent computation by recurrent dynamics in prefrontal cortex.
V Mante, D Sussillo, KV Shinoy, & WT Newsome, Nature 2013.
http://dx.doi.org/10.1038/nature12742
"""
from __future__ import division
import numpy as np
from pyrl import tasktools
# Inputs
inputs = tasktools.to_map('MOTION', 'COLOR',
'MOTION-LEFT', 'MOTION-RIGHT',
'COLOR-LEFT', 'COLOR-RIGHT')
# Actions
actions = tasktools.to_map('FIXATE', 'CHOOSE-LEFT', 'CHOOSE-RIGHT')
# Trial conditions
contexts = ['m', 'c']
left_rights = [-1, 1]
cohs = [5, 15, 50]
n_conditions = len(contexts) * (len(left_rights)*len(cohs))**2
# Training
n_gradient = n_conditions
n_validation = 50*n_conditions
Multisensory integration, based on
A category-free neural population supports evolving demands during decision-making.
D Raposo, MT Kaufman, & AK Churchland, Nature Neurosci. 2014.
http://dx.doi.org/10.1038/nn.3865
"""
from __future__ import division
import numpy as np
from pyrl import tasktools
# Inputs
inputs = tasktools.to_map('FIXATION', 'VISUAL-P', 'VISUAL-N', 'AUDITORY-P', 'AUDITORY-N')
# Actions
actions = tasktools.to_map('FIXATE', 'CHOOSE-LOW', 'CHOOSE-HIGH')
# Trial conditions
mods = ['v', 'a', 'va']
freqs = range(9, 16+1)
n_conditions = len(mods) * len(freqs)
# Discrimination boundary
boundary = 12.5
# Training
n_gradient = n_conditions
n_validation = 50*n_conditions