==================================================================

***** Multi-Timescale Adaptive Threshold (MAT) Model ***** 

------------------------------------------------------------------

Kobayashi, R., Tsubo, Y., & Shinomoto, S. (2009). Made-to-order
spiking neuron model equipped with a multi-timescale adaptive
threshold. Frontiers in computational neuroscience, 3, 9.
http://journal.frontiersin.org/article/10.3389/neuro.10.009.2009/full

Note: The implementation has been kept consistent with the Matlab
version found at:
http://research.nii.ac.jp/~r-koba/applications/pred.html

This NEURON implementation was contributed by Shailesh Appukuttan
==================================================================

This is a NEURON implementation (using Python) of the MAT model
published by Kobayashi et al. (2009). Fig. 5B from the article has been
reproduced. This illustrates the operation of the model for different
firing patterns.

The files included here are:

1) MAT_model.mod
- the NMODL file for the MAT model; default parameters are set for
regular spiking (RS)

2) MAT_Neuron_StepCurrent.py
- this file reproduces Fig. 5B from the article

3) fig_5.png
- Fig. 5B from the article reproduced using MAT_Neuron_StepCurrent.py


4) MAT_Neuron_VaryCurrent.py
- demo showing input of an arbitrary current to the model

5) noisyCurrent.png
- sample output from MAT_Neuron_VaryCurrent.py


==================================================================

Implemented by: Shailesh Appukuttan
Email: shailesh.appukuttan at unic.cnrs-gif.fr / appukuttan.shailesh
at gmail.com

Please inform me if you find any inconsistency in the model
implementation.  I shall try to resolve the same.

==================================================================