# Reproducing Stevens et al. (J. Neuroscience, 2013)

The GCAL model (gain control, adaptation and lateral) is a general purpose cortical developmental model of topographic map formation in the primary visual cortex (V1), driven by the presentation of visual patterns on a retinal sheet. This model accounts for receptive field formation, the emergence and of orientation selectivity across the cortical surface as well as contrast-invariant orientation tuning. The GCAL model is robust to large changes in the contrast of the training patterns that may flucuate between widely varying statistical distributions. The resulting orientation maps are robust and stable yet adaptive development that reflects the statistics of the input.

This notebook replicates the figures in Stevens et al. (2013):

@article{Stevens02102013,
author = {Stevens, Jean-Luc R. and Law, Judith S. and
Antol\'{i}k, J\'{a}n and Bednar, James A.},
title = {Mechanisms for Stable, Robust, and Adaptive
Development of Orientation Maps in the Primary Visual Cortex},
journal = {The Journal of Neuroscience},
volume = {33},
number = {40},
pages = {15747-15766},
year = {2013},
doi = {10.1523/JNEUROSCI.1037-13.2013},
url = {http://www.jneurosci.org/content/33/40/15747.full}
}


### The GCAL model file used in defined in this notebook in under 150 lines of code (ignoring comments and docstrings)

If you can double-click on this text and edit it, you are in a live IPython Notebook environment where you can run the code and explore the model. Otherwise, you are viewing a static (e.g. HTML) copy of the notebook, which allows you to see the precomputed results only. To switch to the live notebook, see the notebook installation instructions.

This notebook uses the GCAL model definition gcal.ty exported from this notebook to launch the simulations and collate the data to plot all the figures published in the paper mentioned above. Collecting all the necessary statistics required to reproduce the figures in the paper requires 842 simulations of about 45 minutes each (four threads on an Intel Xeon E5620 quad-core processor) to achieve publication quality.

You can complete the launch much more quickly by running a subset of all the simulations (covering most of the figures) by setting the simulation parameters and by reducing the simulation quality. By default, this notebook uses double the default cortical density (size of V1) specified in the model file to attain publication quality maps.

Running all 842 high-quality simulations is computationally very expensive but running a single simulation at half cortical density is quick - the GCAL model can be run to completion (20000 iterations) in 20 minutes on an Intel i3 processor. You can launch the Topographica GUI and explore the model from the notebook defining the model.

Note that the gcal.ty model file exported by the notebook may be run without IPython using Topographica as follows:

bash ./topographica -g ./gcal.ty
In [1]:
import os, sys
sys.path += [os.path.abspath(os.path.join('..','..'))]


## Contents

Specifying the parameter space

Global settings
The global parameters used to specify all the necessary simulations in this notebook.
Constants
Constantsused for the published data but which can be changed across simulations.
Meta-parameters
Choosing the figures to generate and the input seeds for the training pattern sequences.
Analysis settings
Simulation and analysis settings
Figure 1
Ferret map stability over time (static).
Figure 2
Ferret map stability (static).
Figure 3
Map quality metric and pi pinwheel density.
Figure 4
Schematic applicable to all four models (static)
Figure 5
Development of maps in the L model.
Figure 6
Analysis of robustness, quality and stability for the L model.
Figure 7
Analysis of robustness, quality and stability for the AL model.
Figure 8
Analysis of robustness, quality and stability for the GCL model.
Figure 9
Analysis of robustness, quality and stability for the GCAL model.
Figure 10
GCAL trained with retinal wave -> natural image patch stimuli
Figure 11
Figure 12
Contrast invariant orientation tuning curves for the GCAL model

# Specifying the GCAL Simulations

Note that by default, all jobs are launch locally. If you wish to launch all the necessary jobs, it is advisable to use a compute cluster if available.

## Global settings

This section defines the global settings as well as the basic definitions (constant) that define the settings for the four basic models (L,AL,GCL and GCAL). Running all 842 high quality simulations is very computationally expensive - you may wish to use a lower simulation quality on a subset of the total number of necessary simulations.

In [2]:
import os
import numpy as np

import topo, param, lancet

from jn13_figures.lib import measurement, misc
from topo.misc.commandline import global_params as jn13rc

run_locally = param.Boolean(default=True,
doc="Whether to use Grid Engine or run the simulations locally"),
doc="Number of threads to use per node (if using a cluster)"),
quality = param.Number(default=1.0,
doc="Quality of the simulations used (default of 50% for speed)."),
seed_count = param.Number(default=10,
doc="Number of random training seeds for Figures 6-9."),
Fig05_06 = param.Boolean(default=False,
doc="Launch L simulations, required for Figures 5 and 6."),
Fig07 = param.Boolean(default=False,
doc="Launch AL simulations, required for Figure 7."),
Fig08 = param.Boolean(default=False,
doc="Launch GCL simulations, required for Figure 8."),
Fig09 = param.Boolean(default=False,
doc="Launch GCAL simulations, required for Figures 9."),
Fig10_12 = param.Boolean(default=False,
doc="Launch GCAL with noisy disks->images for Figure 10 and 12."),
Fig11 = param.Boolean(default=False,
doc="Launch GCAL with biased image patches for Figure 11.")
)


### Figures 10,11 and 12 generated by default to make execution practical on a single desktop machine

In [3]:
# You may enable the generation of more figures here as necessary
jn13rc.quality = 0.5
jn13rc.Fig10_12 = True
jn13rc.Fig11 = True


### Model definitions

The appropriate boolean settings for contrast-gain control (LGN) and homeostatic threshold adaptation (V1) that choose between the L, AL, GCL and GCAL models:

In [4]:
# Analysis common to L, AL, GCL and GCAL
gaussian_figures = ['Fig05_06', 'Fig07', 'Fig08', 'Fig09']
# Figures specific to GCAL (retinal wave -> image patches)
GCAL_figures = ['Fig10_12', 'Fig11']

mechanisms = { # (HOMEOSTASIS, GAIN CONTROL)
'Fig05_06':(False, False),  # The L Model
'Fig07':   (True,  False),  # The AL Model
'Fig08':   (False, True),   # The GCL Model
'Fig09':   (True,  True),   # The GCAL Model
'Fig10_12':(True,  True),   # GCAL (noisy disk -> image patches)
'Fig11':   (True,  True),   # GCAL (biased image patches)
}


### Training pattern definitions

In [5]:
# The first element of the tuple is the dataset, the second is the number of retinal waves
training = {
'Fig05_06':('Gaussian',  0),
'Fig07':   ('Gaussian',  0),
'Fig08':   ('Gaussian',  0),
'Fig09':   ('Gaussian',  0),
'Fig10_12':('Nature',    6000),
'Fig11':   ('VGR',       6000)} # Vertical google rearing


### Output directory

In [6]:
output_directory = os.path.join(os.getcwd(), 'jn13_figures', 'output')
print "OUTPUT DIRECTORY: %r" % output_directory

OUTPUT DIRECTORY: '/home/user/topographica/models/stevens.jn13/jn13_figures/output'



Generate the dataset for vertical goggle rearing:

In [7]:
images_dir = param.resolve_path('images', path_to_file=False)
misc.generate_GR(images_dir, name='VGR')


### Platform specific settings

In [8]:
# Use the local Launcher if jobs to be run locally, else use Grid Engine
Launcher = lancet.Launcher if jn13rc.run_locally else lancet.QLauncher

# Topographica supports OpenMP so one process will use all cores
Launcher.max_concurrency = 1 if jn13rc.run_locally else None

# The qsub options for Grid Engine to use OpenMP with N threads.
qsub_flag_options = dict(b='y',
l='h_rt=05:59:00',
P='inf_ndtc')

launcher_kwargs = {} if jn13rc.run_locally else {'qsub_flag_options':qsub_flag_options}


## Constants

These are the constants define the settings common to all simulations launched.

• Constants regarding the structure of model (sheet densities, cortical sheet area)
• Constants controlling the measurement process (namely for tuning curve measurements)
• Constant determining the frequency of map measurement (every 1000 iterations till 20,000 steps)
In [9]:
# Settings that are constant for all simulations launched
model_constants = lancet.Args(retina_density=24.0, lgn_density=24.0, area=1.5,
cortex_density= 98.0 * jn13rc.quality)

# Simulation times at which to perform measurements
measurement_times = lancet.Args(times=[i*1000 for i in range(21)])

# The number of phases and orientations used in the orientation tuning curve measurement
analysis_constants = lancet.Args( num_orientation=20, num_phase=8 )

print "The analysis and measurement contant settings:"; (model_constants * analysis_constants).dframe

The analysis and measurement contant settings:


Out[9]:
area cortex_density lgn_density num_orientation num_phase retina_density
0 1.5 49 24 20 8 24
In [10]:
constants = (model_constants * analysis_constants * measurement_times)


## Meta-parameters

Metaparameters allow parameterization over sets of runs that allows different behaviour for different sets of batches. In this paper, the four models are first run with 10 different seeds using Gaussian training patterns (Figures 5-9) before GCAL is tested using different input statistics (Figures 10-12). First define a pool of random seeds to be used in both cases:

In [11]:
np.random.seed(42)
random_seeds = [np.random.randint(0,1000) for i in range(jn13rc.seed_count)]


### Gaussian training condition

Choose the models to analyse (Figures 6-9) and how many runs will be used for each:

• The basic analysis of robustness for each model requires a set of simulations generated by running it over a range of contrasts.
• To determine variability, this fundamental analysis must be repeated using a number of training seeds.
• This analysis is then repeated for each of the different models (L,AL,GCL,GCAL)
• The analysis must therefore be performed (seed_count * model count) times.
In [12]:
# The different models that need to be training by published figure number
gauss_figures = lancet.List('figure', [fig for fig in gaussian_figures if getattr(jn13rc, fig)])
# Defining <seed_count> random training seeds
input_seeds = lancet.List('input_seed', random_seeds)
gauss_metaparams = (gauss_figures * input_seeds)


### GCAL noisy disk to image training conditions

Figures 10 and 11 each require a single GCAL simulation but under a different training condition. Only a single seed is required to reproduce these figures.

In [13]:
gcal_figures = lancet.List('figure', [fig for fig in GCAL_figures if getattr(jn13rc, fig)])
input_seed = lancet.Args(input_seed=random_seeds[0]) # Use the first random seed
gcal_metaparams = gcal_figures * input_seed


### Displaying the first few metaparameters

In [14]:
metaparameters = gauss_metaparams + gcal_metaparams
print "The first 12 metaparameters:"

The first 12 metaparameters:


Out[14]:
0 1
figure Fig10_12 Fig11
input_seed 102 102

## Simulation and analysis settings

In [15]:
from topo.misc.lancext import RunBatchCommand, Analysis, topo_metadata

lancet.review_and_launch.output_directory = output_directory

@lancet.review_and_launch(launch_args=metaparameters)
def jn13_figures(figure=None, input_seed=None):
"""

"""
analysis = Analysis(metadata=['contrast', 'input_seed', 'times'], paths=[os.getcwd()])

# Simulation settings
homeostasis, gain_control =   mechanisms[figure]
dataset, retinal_waves    =   training[figure]

common_settings = lancet.Args(dataset=dataset,
homeostasis=homeostasis,
gain_control = gain_control,
retinal_waves=retinal_waves,
input_seed=input_seed,
figure=figure)

# The "Nature" and goggle rearing conditions use GCAL measured close to the 'sweet spot'
# while the rest of the simulations span 0-100% for Figures 6-9 (includes Fig 5)
contrasts = (lancet.Args(contrast=20) if figure in ['Fig10_12', 'Fig11']
else lancet.Range("contrast", 0, 100, 21, fp_precision=2))

if figure =='Fig10_12':
elif figure == 'Fig11':
else:

batch_arguments = contrasts * constants * common_settings
runbatch_cmd = RunBatchCommand('./gcal.ty', analysis)

batch_name = '%s_seed%d' % (figure, input_seed)
# Capture version control and platform specific metadata for reproducibility
return Launcher(batch_name, batch_arguments, runbatch_cmd, subdir=[figure],


# Launch all specified simulations

Matching the scientific results of Stevens et al. (2013) is possible with any version of Topographica; this notebook may be run with any recent version of the simulator. The final GCAL model presented is stable and robust, which allows development to proceed consistently even if there are small changes in the numerical accuracy of the simulation. In contrast, the L and AL models are unstable, especially in the high contrast regime (Figures 7F, 8F) making the results generated by these models sensitive to implementation details of the Topographica version used.

To match the published example maps in figures 7F and 8F exactly, a small patch needs to be applied to Topographica as described here which reverts weight normalization from 64 to 32bit precision. The precise versions of the software used to generate this notebook version are shown below where the two Topographica files containing uncommited changes are a result of applying the patch.

In [16]:
metadata.summary()

Topographica version control summary:

Topographica: 37a7113 measure_or_tuning can no longer be imported from command.pylabplot
[2 files have uncommited changes as captured by git diff]
Param:        c1a042b Fixed helper function for ParameterizedFunction.__reduce__; incorporates Chris ...
Imagen:       fec785e Updated ProjectionGrid methods
Lancet:       52b5e8d The vcs_metadata function can now accept commands as keyword argument
Numpy:        ca07bce Version 1.6.2 (release)


In [17]:
# Run all the specified simulations
jn13_figures()


==========================
| Meta Arguments Summary |
==========================

Items: 2
Varying Keys: 'figure'
Constant Items: input_seed=102

List(
key='figure',
values=[]
) * List(
key='input_seed',
values=[102, 435, 860, 270, 106, 71, 700, 20, 614, 121]
) + List(
key='figure',
values=['Fig10_12', 'Fig11']
) * Args(
input_seed=102
)

Show available argument specifier entries? [y, N, quit]:

=====================
| Arguments Summary |
=====================

Items: 1
Varying Keys:
Constant Items: area=1.5, contrast=20, cortex_density=49.0, dataset='Nature', figure='Fig10_12', gain_control=True, homeostasis=True, input_seed=102, lgn_density=24.0, num_orientation=20, num_phase=8, retina_density=24.0, retinal_waves=6000, times=[0, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 11000, 12000, 13000, 14000, 15000, 16000, 17000, 18000, 19000, 20000]

Show available argument specifier entries? [y, N, quit]:

===========================
| RunBatchCommand Summary |
===========================

Command executable: /home/user/topographica/topographica
Analysis functions:

0. measure_shouval(times, num_orientation, num_phase)
1. stability_analysis(figure, times)
2. afferent_CFs(times)

Show available command entries? [y, N, quit, save]:

====================
| Launcher Summary |
====================

Type: Launcher
Batch Name: 'Fig10_12_seed102'
Root directory: '/home/user/topographica/models/stevens.jn13/jn13_figures/output/Fig10_12/2013-12-20_1611-Fig10_12_seed102'
Maximum concurrency: 1

Show complete launch repr? [Y, n]:

Launcher(
batch_name='Fig10_12_seed102',
args=Args(
contrast=20
) * Args(
cortex_density=49.0,
lgn_density=24.0,
retina_density=24.0,
area=1.5
) * Args(
num_phase=8,
num_orientation=20
) * Args(
times=[0, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 11000, 12000, 13000, 14000, 15000, 16000, 17000, 18000, 19000, 20000]
) * Args(
retinal_waves=6000,
figure='Fig10_12',
gain_control=True,
input_seed=102,
dataset='Nature',
homeostasis=True
),
command=RunBatchCommand(
executable='/home/user/topographica/topographica',
tyfile='./gcal.ty',
analysis=Analysis(
paths=['/home/user/topographica/models/stevens.jn13'],
analysis_fns=[
AnalysisFn(jn13_figures.lib.measurement.measure_shouval),
AnalysisFn(jn13_figures.lib.measurement.stability_analysis),
AnalysisFn(jn13_figures.lib.measurement.afferent_CFs)]
)
),
output_directory='/home/user/topographica/models/stevens.jn13/jn13_figures/output'
)

Skip remaining reviews? [Y, n, quit]:
Execute? [y, N]:


Out[17]:
True


# Figures

Once all the files have been generated by launching the simulations defined above, the figures may now now be generated.

### To disable dynamic SVG figure generation and show PNG placeholders instead, set show_raster to True

In [18]:
show_raster = False

In [19]:
build_dir = './jn13_figures/output/build_dir'
template_dir = './jn13_figures/templates'

settings = dict(
build_dir = build_dir, # The directory where the template will be applied
template_dir = template_dir,     # The SVG templates directory
size_factor=1.0,
show_raster = show_raster
)

In [20]:
import lancet
import jn13_figures
from jn13_figures import Display
from IPython.display import SVG


## Figure 1 - Ferret data (Chapman et al, 1996)

In [21]:
Display(template_dir=template_dir, snapshot='fig01')

Out[21]:

## Figure 2 - Chapman analysis

This figure shows analysis of experimental data only, replotted from Chapman et al 1996. For this reason, a static SVG in the templates directory is shown. A notebook demonstrating the analysis of the experimental data (Kaschube 2010, Chapman 1996) will be included in the near future.

In [22]:
Display(template_dir=template_dir, snapshot='fig02')

Out[22]: