From 9bfed3e1aa8748a18eee970238de3c002fc987b5 Mon Sep 17 00:00:00 2001 From: EnfxcFCb6 Date: Wed, 9 Jul 2025 16:07:32 -0400 Subject: [PATCH 1/2] Standardized README to Markdown format --- README.md | 90 ++++++++++++++++++++++++++++++++++++++++++++++++++++ README.txt | 92 ------------------------------------------------------ 2 files changed, 90 insertions(+), 92 deletions(-) create mode 100644 README.md delete mode 100644 README.txt diff --git a/README.md b/README.md new file mode 100644 index 0000000..a3c4a35 --- /dev/null +++ b/README.md @@ -0,0 +1,90 @@ +# GCAL: Gain Control, Adaptation, Laterally connected + +Simple but robust single-population V1 model orientation map from: + +Jean-Luc R. Stevens, Judith S. Law, Jan Antolik, and James A. Bednar. +Mechanisms for stable, robust, and adaptive development of orientation +maps in the primary visual cortex. +*Journal of Neuroscience*, 33:15747-15766, 2013. + +Development of orientation maps in ferret and cat primary visual +cortex (V1) has been shown to be stable, in that the earliest +measurable maps are similar in form to the eventual adult map, +robust, in that similar maps develop in both dark rearing and in a +variety of normal visual environments, and yet adaptive, in that +the final map pattern reflects the statistics of the specific +visual environment. How can these three properties be reconciled? +Using mechanistic models of the development of neural connectivity +in V1, we show for the first time that realistic stable, robust, +and adaptive map development can be achieved by including two +low-level mechanisms originally motivated from single-neuron +results. Specifically, contrast-gain control in the retinal +ganglion cells and the lateral geniculate nucleus reduces variation +in the presynaptic drive due to differences in input patterns, +while homeostatic plasticity of V1 neuron excitability reduces the +postsynaptic variability in firing rates. Together these two +mechanisms, thought to be applicable across sensory systems in +general, lead to biological maps that develop stably and robustly, +yet adapt to the visual environment. The modeling results suggest +that topographic map stability is a natural outcome of low-level +processes of adaptation and normalization. The resulting model is +more realistic, simpler, and far more robust, and is thus a good +starting point for future studies of cortical map development. + +@article{Stevens02102013, + author = {Stevens, Jean-Luc R. and Law, Judith S. and Antolik, + Jan 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](http://www.jneurosci.org/content/33/40/15747.full) +} + +# Running the model + +This model may be run with the Topographica simulator as follow: + + ./topographica -g gcal.ty + +A suitable version of the Topographica simulator may be obtained using +git: + + git clone https://github.com/ioam/topographica.git + cd topographica + git submodule update --init + git checkout GCALModelDB + +After 10000 iterations (~ 15 minutes on a quad-core 3Ghz machine), the +orientation map corresponding to the condition shown in Figure 9F can +be measured (GCAL model at 100% contrast). In the GUI, this may be +viewed by opening the Orientation Preference window (Plots -> +Preference Maps -> Orientation Preference). + +The default settings are appropriate for quick simulations, suitable +for regular exploratory work. An exact match to Figure 9E requires a +slower simulation: + + ./topographica -p cortex_density=98 -p area=1.5 -g gcal.ty + +The linear density of cortical units is doubled for a cortical area of +1.5 x 1.5. Making the cortical area 2.25 times larger allows border +effects to be eliminated from the central 1.0 x 1.0 area. With four +times more cortical units due to the higher density, the overall +network is nine times larger. + +# Reproducing all published figures + +An IPython Notebook detailing how to reproduce the entire paper +(including all figures and simulations) may be viewed or run from +here: + +[http://topographica.org/_static/stevens_jn13.html](http://topographica.org/_static/stevens_jn13.html) + +--- + +2025-07-09: Converted README to Markdown. \ No newline at end of file diff --git a/README.txt b/README.txt deleted file mode 100644 index 36bde9c..0000000 --- a/README.txt +++ /dev/null @@ -1,92 +0,0 @@ -GCAL: Gain Control, Adaptation, Laterally connected - -Simple but robust single-population V1 model orientation map from: - - Jean-Luc R. Stevens, Judith S. Law, Jan Antolik, and James A. Bednar. - Mechanisms for stable, robust, and adaptive development of orientation - maps in the primary visual cortex. - Journal of Neuroscience, 33:15747-15766, 2013. - - Development of orientation maps in ferret and cat primary visual - cortex (V1) has been shown to be stable, in that the earliest - measurable maps are similar in form to the eventual adult map, - robust, in that similar maps develop in both dark rearing and in a - variety of normal visual environments, and yet adaptive, in that - the final map pattern reflects the statistics of the specific - visual environment. How can these three properties be reconciled? - Using mechanistic models of the development of neural connectivity - in V1, we show for the first time that realistic stable, robust, - and adaptive map development can be achieved by including two - low-level mechanisms originally motivated from single-neuron - results. Specifically, contrast-gain control in the retinal - ganglion cells and the lateral geniculate nucleus reduces variation - in the presynaptic drive due to differences in input patterns, - while homeostatic plasticity of V1 neuron excitability reduces the - postsyn- aptic variability in firing rates. Together these two - mechanisms, thought to be applicable across sensory systems in - general, lead to biological maps that develop stably and robustly, - yet adapt to the visual environment. The modeling results suggest - that topographic map stability is a natural outcome of low-level - processes of adaptation and normalization. The resulting model is - more realistic, simpler, and far more robust, and is thus a good - starting point for future studies of cortical map development. - - - @article{Stevens02102013, - author = {Stevens, Jean-Luc R. and Law, Judith S. and Antolik, - Jan 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} - } - -================= -Running the model -================= - -This model may be run with the Topographica simulator as follow: - -./topographica -g gcal.ty - -A suitable version of the Topographica simulator may be obtained using -git: - -git clone https://github.com/ioam/topographica.git -cd topographica -git submodule update --init -git checkout GCALModelDB - -After 10000 iterations (~ 15 minutes on a quad-core 3Ghz machine), the -orientation map corresponding to the condition shown in Figure 9F can -be measured (GCAL model at 100% contrast). In the GUI, this may be -viewed by opening the Orientation Preference window (Plots -> -Preference Maps -> Orientation Preference). - -The default settings are appropriate for quick simulations, suitable -for regular exploratory work. An exact match to Figure 9E requires a -slower simulation: - -./topographica -p cortex_density=98 -p area=1.5 -g gcal.ty - -The linear density of cortical units is doubled for a cortical area of -1.5 x 1.5. Making the cortical area 2.25 times larger allows border -effects to be eliminated from the central 1.0 x 1.0 area. With four -times more cortical units due to the higher density, the overall -network is nine times larger. - - -================================= -Reproducing all published figures -================================= - -An IPython Notebook detailing how to reproduce the entire paper -(including all figures and simulations) may be viewed or run from -here: - -http://topographica.org/_static/stevens_jn13.html From 4a6425d762baf8f2f4eac2d235d2b92f72583e9b Mon Sep 17 00:00:00 2001 From: rsakai Date: Thu, 10 Jul 2025 14:59:03 -0400 Subject: [PATCH 2/2] Update README.md --- README.md | 78 +++++++++++++++++++++++++++---------------------------- 1 file changed, 39 insertions(+), 39 deletions(-) diff --git a/README.md b/README.md index a3c4a35..e7e462b 100644 --- a/README.md +++ b/README.md @@ -2,33 +2,33 @@ Simple but robust single-population V1 model orientation map from: -Jean-Luc R. Stevens, Judith S. Law, Jan Antolik, and James A. Bednar. -Mechanisms for stable, robust, and adaptive development of orientation -maps in the primary visual cortex. +Jean-Luc R. Stevens, Judith S. Law, Jan Antolik, and James A. Bednar. +Mechanisms for stable, robust, and adaptive development of orientation +maps in the primary visual cortex. *Journal of Neuroscience*, 33:15747-15766, 2013. -Development of orientation maps in ferret and cat primary visual -cortex (V1) has been shown to be stable, in that the earliest -measurable maps are similar in form to the eventual adult map, -robust, in that similar maps develop in both dark rearing and in a -variety of normal visual environments, and yet adaptive, in that -the final map pattern reflects the statistics of the specific -visual environment. How can these three properties be reconciled? -Using mechanistic models of the development of neural connectivity -in V1, we show for the first time that realistic stable, robust, -and adaptive map development can be achieved by including two -low-level mechanisms originally motivated from single-neuron -results. Specifically, contrast-gain control in the retinal -ganglion cells and the lateral geniculate nucleus reduces variation -in the presynaptic drive due to differences in input patterns, -while homeostatic plasticity of V1 neuron excitability reduces the -postsynaptic variability in firing rates. Together these two -mechanisms, thought to be applicable across sensory systems in -general, lead to biological maps that develop stably and robustly, -yet adapt to the visual environment. The modeling results suggest -that topographic map stability is a natural outcome of low-level -processes of adaptation and normalization. The resulting model is -more realistic, simpler, and far more robust, and is thus a good +Development of orientation maps in ferret and cat primary visual +cortex (V1) has been shown to be stable, in that the earliest +measurable maps are similar in form to the eventual adult map, +robust, in that similar maps develop in both dark rearing and in a +variety of normal visual environments, and yet adaptive, in that +the final map pattern reflects the statistics of the specific +visual environment. How can these three properties be reconciled? +Using mechanistic models of the development of neural connectivity +in V1, we show for the first time that realistic stable, robust, +and adaptive map development can be achieved by including two +low-level mechanisms originally motivated from single-neuron +results. Specifically, contrast-gain control in the retinal +ganglion cells and the lateral geniculate nucleus reduces variation +in the presynaptic drive due to differences in input patterns, +while homeostatic plasticity of V1 neuron excitability reduces the +postsynaptic variability in firing rates. Together these two +mechanisms, thought to be applicable across sensory systems in +general, lead to biological maps that develop stably and robustly, +yet adapt to the visual environment. The modeling results suggest +that topographic map stability is a natural outcome of low-level +processes of adaptation and normalization. The resulting model is +more realistic, simpler, and far more robust, and is thus a good starting point for future studies of cortical map development. @article{Stevens02102013, @@ -51,7 +51,7 @@ This model may be run with the Topographica simulator as follow: ./topographica -g gcal.ty -A suitable version of the Topographica simulator may be obtained using +A suitable version of the Topographica simulator may be obtained using git: git clone https://github.com/ioam/topographica.git @@ -59,32 +59,32 @@ git: git submodule update --init git checkout GCALModelDB -After 10000 iterations (~ 15 minutes on a quad-core 3Ghz machine), the -orientation map corresponding to the condition shown in Figure 9F can -be measured (GCAL model at 100% contrast). In the GUI, this may be -viewed by opening the Orientation Preference window (Plots -> +After 10000 iterations (~ 15 minutes on a quad-core 3Ghz machine), the +orientation map corresponding to the condition shown in Figure 9F can +be measured (GCAL model at 100% contrast). In the GUI, this may be +viewed by opening the Orientation Preference window (Plots -> Preference Maps -> Orientation Preference). -The default settings are appropriate for quick simulations, suitable -for regular exploratory work. An exact match to Figure 9E requires a +The default settings are appropriate for quick simulations, suitable +for regular exploratory work. An exact match to Figure 9E requires a slower simulation: ./topographica -p cortex_density=98 -p area=1.5 -g gcal.ty -The linear density of cortical units is doubled for a cortical area of -1.5 x 1.5. Making the cortical area 2.25 times larger allows border -effects to be eliminated from the central 1.0 x 1.0 area. With four -times more cortical units due to the higher density, the overall +The linear density of cortical units is doubled for a cortical area of +1.5 x 1.5. Making the cortical area 2.25 times larger allows border +effects to be eliminated from the central 1.0 x 1.0 area. With four +times more cortical units due to the higher density, the overall network is nine times larger. # Reproducing all published figures -An IPython Notebook detailing how to reproduce the entire paper -(including all figures and simulations) may be viewed or run from +An IPython Notebook detailing how to reproduce the entire paper +(including all figures and simulations) may be viewed or run from here: [http://topographica.org/_static/stevens_jn13.html](http://topographica.org/_static/stevens_jn13.html) --- -2025-07-09: Converted README to Markdown. \ No newline at end of file +2025-07-09: Converted README to Markdown.