ModelDBRepository · rsakai10 · Jul 9, 2025 · Jul 10, 2025
diff --git a/index.html → README.md b/index.html → README.md
@@ -1,40 +1,42 @@
-<html><pre>
-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
+## 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 
 aperiodic currents that cross the threshold.
 
 Brian simulator models are available at this web page:
 
-<a href="http://briansimulator.org/docs/examples-frompapers_Brette_Guigon_2003.html">http://briansimulator.org/docs/examples-frompapers_Brette_Guigon_2003.html</a>
+[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:
 
-<img src="./screenshot.png" alt="screenshot" width="550">
+![screenshot](./screenshot.png)
 
-This simulation requires Brian which can be downloaded and installed
-from the instructions available at <a href="http://www.briansimulator.org/">http://www.briansimulator.org/</a>
+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 <a href="https://groups.google.com/group/briansupport">https://groups.google.com/group/briansupport</a>.
-</pre></html>
+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.