From 35c56ec49091a09445231184ba69678ccf106593 Mon Sep 17 00:00:00 2001 From: EnfxcFCb6 Date: Wed, 9 Jul 2025 16:14:32 -0400 Subject: [PATCH 1/2] Standardized README to Markdown format --- index.html => README.md | 60 +++++++++++++++++++++-------------------- 1 file changed, 31 insertions(+), 29 deletions(-) rename index.html => README.md (53%) diff --git a/index.html b/README.md similarity index 53% rename from index.html rename to README.md index 0cec0c1..659158c 100644 --- a/index.html +++ b/README.md @@ -1,40 +1,42 @@ -
 For the paper:
 
-Brette R, Guigon E (2003) Reliability of spike timing is a general
-property of spiking model neurons. Neural Comput 15:279-308
-
-Abstract:
-
-The responses of neurons to time-varying injected currents are
-reproducible on a trial-by-trial basis in vitro, but when a constant
-current is injected, small variances in interspike intervals across
-trials add up, eventually leading to a high variance in spike
-timing. It is unclear whether this difference is due to the nature of
-the input currents or the intrinsic properties of the neurons. Neuron
-responses can fail to be reproducible in two ways: dynamical noise can
-accumulate over time and lead to a desynchronization over trials, or
-several stable responses can exist, depending on the initial
-condition. Here we show, through simulations and theoretical
-considerations, that for a general class of spiking neuron models,
-which includes, in particular, the leaky integrate-and-fire model as
-well as nonlinear spiking models, aperiodic currents, contrary to
-periodic currents, induce reproducible responses, which are stable
-under noise, change in initial conditions and deterministic
-perturbations of the input. We provide a theoretical explanation for
+Brette R, Guigon E (2003) Reliability of spike timing is a general  
+property of spiking model neurons. *Neural Comput* 15:279-308
+
+## Abstract
+
+The responses of neurons to time-varying injected currents are  
+reproducible on a trial-by-trial basis in vitro, but when a constant  
+current is injected, small variances in interspike intervals across  
+trials add up, eventually leading to a high variance in spike  
+timing. It is unclear whether this difference is due to the nature of  
+the input currents or the intrinsic properties of the neurons. Neuron  
+responses can fail to be reproducible in two ways: dynamical noise can  
+accumulate over time and lead to a desynchronization over trials, or  
+several stable responses can exist, depending on the initial  
+condition. Here we show, through simulations and theoretical  
+considerations, that for a general class of spiking neuron models,  
+which includes, in particular, the leaky integrate-and-fire model as  
+well as nonlinear spiking models, aperiodic currents, contrary to  
+periodic currents, induce reproducible responses, which are stable  
+under noise, change in initial conditions and deterministic  
+perturbations of the input. We provide a theoretical explanation for  
 aperiodic currents that cross the threshold.
 
 Brian simulator models are available at this web page:
 
-http://briansimulator.org/docs/examples-frompapers_Brette_Guigon_2003.html
+[http://briansimulator.org/docs/examples-frompapers_Brette_Guigon_2003.html](http://briansimulator.org/docs/examples-frompapers_Brette_Guigon_2003.html)
 
 The simulation reproduces Fig. 10D,E:
 
-screenshot
+![screenshot](./screenshot.png)
 
-This simulation requires Brian which can be downloaded and installed
-from the instructions available at http://www.briansimulator.org/
+This simulation requires Brian which can be downloaded and installed  
+from the instructions available at [http://www.briansimulator.org/](http://www.briansimulator.org/)
 
-For support on installing and using Brian simulations there is a
-support group at https://groups.google.com/group/briansupport.
-
+For support on installing and using Brian simulations there is a +support group at [https://groups.google.com/group/briansupport](https://groups.google.com/group/briansupport). + +--- + +2025-07-09: Converted README to Markdown. \ No newline at end of file From 163198bb02cb12725781584b8b90f7344c0e93da Mon Sep 17 00:00:00 2001 From: rsakai Date: Thu, 10 Jul 2025 15:51:40 -0400 Subject: [PATCH 2/2] Update README.md --- README.md | 42 +++++++++++++++++++++--------------------- 1 file changed, 21 insertions(+), 21 deletions(-) diff --git a/README.md b/README.md index 659158c..bf52752 100644 --- a/README.md +++ b/README.md @@ -1,26 +1,26 @@ -For the paper: +## For the paper: -Brette R, Guigon E (2003) Reliability of spike timing is a general +Brette R, Guigon E (2003) Reliability of spike timing is a general property of spiking model neurons. *Neural Comput* 15:279-308 ## Abstract -The responses of neurons to time-varying injected currents are -reproducible on a trial-by-trial basis in vitro, but when a constant -current is injected, small variances in interspike intervals across -trials add up, eventually leading to a high variance in spike -timing. It is unclear whether this difference is due to the nature of -the input currents or the intrinsic properties of the neurons. Neuron -responses can fail to be reproducible in two ways: dynamical noise can -accumulate over time and lead to a desynchronization over trials, or -several stable responses can exist, depending on the initial -condition. Here we show, through simulations and theoretical -considerations, that for a general class of spiking neuron models, -which includes, in particular, the leaky integrate-and-fire model as -well as nonlinear spiking models, aperiodic currents, contrary to -periodic currents, induce reproducible responses, which are stable -under noise, change in initial conditions and deterministic -perturbations of the input. We provide a theoretical explanation for +The responses of neurons to time-varying injected currents are +reproducible on a trial-by-trial basis in vitro, but when a constant +current is injected, small variances in interspike intervals across +trials add up, eventually leading to a high variance in spike +timing. It is unclear whether this difference is due to the nature of +the input currents or the intrinsic properties of the neurons. Neuron +responses can fail to be reproducible in two ways: dynamical noise can +accumulate over time and lead to a desynchronization over trials, or +several stable responses can exist, depending on the initial +condition. Here we show, through simulations and theoretical +considerations, that for a general class of spiking neuron models, +which includes, in particular, the leaky integrate-and-fire model as +well as nonlinear spiking models, aperiodic currents, contrary to +periodic currents, induce reproducible responses, which are stable +under noise, change in initial conditions and deterministic +perturbations of the input. We provide a theoretical explanation for aperiodic currents that cross the threshold. Brian simulator models are available at this web page: @@ -31,12 +31,12 @@ The simulation reproduces Fig. 10D,E: ![screenshot](./screenshot.png) -This simulation requires Brian which can be downloaded and installed +This simulation requires Brian which can be downloaded and installed from the instructions available at [http://www.briansimulator.org/](http://www.briansimulator.org/) -For support on installing and using Brian simulations there is a +For support on installing and using Brian simulations there is a support group at [https://groups.google.com/group/briansupport](https://groups.google.com/group/briansupport). --- -2025-07-09: Converted README to Markdown. \ No newline at end of file +2025-07-09: Converted README to Markdown.