Cerebellar gain and timing control model (Yamazaki & Tanaka 2007)(Yamazaki & Nagao 2012)

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Accession:144416
This paper proposes a hypothetical computational mechanism for unified gain and timing control in the cerebellum. The hypothesis is justified by computer simulations of a large-scale spiking network model of the cerebellum.
References:
1 . Yamazaki T, Tanaka S (2007) A spiking network model for passage-of-time representation in the cerebellum. Eur J Neurosci 26:2279-92 [PubMed]
2 . Yamazaki T, Nagao S (2012) A computational mechanism for unified gain and timing control in the cerebellum. PLoS One 7:e33319-9 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Cerebellum;
Cell Type(s): Cerebellum Purkinje cell; Cerebellum interneuron granule cell; Cerebellum golgi cell; Cerebellum deep nucleus neuron;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: C or C++ program;
Model Concept(s): Spatio-temporal Activity Patterns; Detailed Neuronal Models; Learning; Sensory processing;
Implementer(s): Yamazaki, Tadashi ;
Search NeuronDB for information about:  Cerebellum Purkinje cell; Cerebellum interneuron granule cell;
README
======

What is this program?
---------------------

This is the source code of our simulation program used in the
following article:

Yamazaki T, Nagao S (2012)
A computational mechanism for unified gain and timing control in the
cerebellum
PLoS ONE 7(3): e33319. doi:10.1371/ journal.pone.0033319
http://dx.plos.org/10.1371/journal.pone.0033319

What files are included?
------------------------

The following files are included:
* `Makefile`: Makefile
* `okr.c`: The simulation program
* `mt19937ar-cok.c`: [The Mersenne Twister][MT] pseudo random number
  generator
* `pfinit.c`: Program to generate the initial synaptic weights for
  parallel fibers on Purkinje cells ($w_{ij}$)
* `si.c`: Program to calculate similarity index
* `ri.c`: Program to calculate reproducibility index
* `wdist.c`: Program to calculate the synaptic weight distribution
* `select.rb`: Program to analyze data
* `sum.rb`: Program to analyze data
* `psth.rb`: Program to analyze data
* `plot_pkj.rb`: Program to analyze data
* `plot_vn.rb`: Program to analyze data
* `gainchange.rb`: Program to analyze data
* `fit.gp`: Program to analyze data

What do we need to use the program?
-----------------------------------

The following libraries are necessary to compile the program
successfully:
* [GNU Scientific Library (GSL)][GSL] for the ODE solver
* [GD Graphics Library][GD] for generating a matrix of similarity
  index

The following software are necessary to use the program:
* `gnuplot`, a graph plotting software for data fitting and plotting
* Ruby programming language for data analysis

How can we carry out the simulation?
------------------------------------

1. Compile the program by simply typing `make`. The following
executable files will be generated:
* `okr` from `okr.c`
* `pfinit` from `pfinit.c`
* `si` from `si.c`
* `ri` from `ri.c`
* `wdist` from `wdist.c`

2. Type `./pfinit w.0` to create the initial synaptic weight file for
parallel fibers on Purkinje cells. A file `w.0` will be created.

3. Type `./okr` to start a computer simulation of OKR adaptation for
300 trials. The number of trials is defined in the source code,
specifically the variable `n_trials`. A typical simulation takes 9
hours on my Opteron PC.

4. You will obtain files `gr.spk.aN`, `gr.spk.N`, `go.spk.N`,
'pkjvnio.spk.N` and `w.N`, where N is the cycle number of OKR
simulation. These files are:
* `gr.spk.aN`: Spike data of all granule cells. Each line denotes the
  spike time in ms (0...2000) and the neuron number (0...102,400)
  respectively.
* `gr.spk.N`: Spike data of 1,024 representative granule cells from
  each granule-cell clusters. Each line denotes the spike time and the
  neuron number (0...1024), respectively.
* `go.spk.N`: Spike data of all Golgi cells. Conventions as in
  `gr.spk.N`.
* `pkjvnio.spk.N`: Spike data of Purkinje, basket, vestibular nuclear,
  and inferior olivary cells. Each line denotes the spike time and the
  neuron number, respectively, where the neuron number is (0...16) for
  Pukinje cells, 16 for the nucleus, 17 for the inferior olive, and
  (18...34) for basket cells.
* `w.N`: synaptic weight data for parallel fibers on Purkinje
  cells. The format is:
  	 for i=pkj0...pkj16
	   for j=gr0...gr102400
	     puts w[i][j]
	   end
	 end
  Thus, each file contains 1638400 lines.

How can we reproduce figures in the article?
--------------------------------------------

Now we are ready to reproduce figures. I will explain how to reproduce
Figs.~2,3. Figures~4,5 can be reproduced similarly.

Figure 2A:
1. If you plot `gr.spk.1` using a graph plotting software such as
`gnuplot`, you will obtain the top panel of Fig.~2A.
2. Type `./sum.rb` to generate the data of active cell ratio as in the
bottom panel of Fig.~2A from `gr.spk.a1`. If you plot the data using
bezier smoothing as `gnuplot> plot 'sum.dat' smooth bezier`, then you
will obtain the figure.

Figure 2B:
Type `./select.rb` to generate the spike data of 5 representative
granule cells with IDs 136, 18, 86, 117, 94 as in Fig.~2B. The outputs
files are `gr.136`, `gr.18`, `gr.86`, `gr.117`, `gr.94`, `h.136`,
`h.18`, `h.86`, `h.117`, `h.94`. `h.N` are the average histograms. If
you plot the spike data and the histogram with bezier smoothing, you
will obtain Fig.~2B.

Figure 2C:
Type `./si gr.spk.a1 gr.spk.a1 c11` to generate the similarity index
for `gr.spk.a1`, the spike patterns of granule cells at the 1 st
cycle. Plot `c11.ac` to obtain Fig.~2C.

Figure 2D:
Type `./ri gr.spk.a2 gr.spk.a2 c12` to generate the reproducibility
index between `gr.spk.a1` and `gr.spk.a2`. You will obtain
`c12.d`. Repeat it until you obtain `c34.d`, `c56.d`, ..., and so on,
and average them to produce Fig.~2D.

Figure 3A:
Type `./psth.rb 0 10` to generate `0_10.dat`, which is PSTH of
Purkinje cell 10 at the initial cycle. Similarly, type `./psth.rb 100
10`, `./psth.rb 200 10`, `./psth.rb 30 10` to produce `100_10.dat`,
`200_10.dat`, `300_10.dat`, which are PSTHs at 100th, 200th and 300th
cycles. Then, type `gnuplot plot_pkj.gp` to fit all data with cosine
functions and plot Fig.~3A.

Figure 3B:
Type `./psth.rb 0 16` to generate `0_16.dat`, which is PSTH of the VN
neuron at the initial cycle. Similarly, type `./psth.rb 100 16`,
`./psth.rb 200 16`, `./psth.rb 30 16` to produce `100_16.dat`,
`200_16.dat`, `300_16.dat`, which are PSTHs at the 100th, 200th and
300th cycles. Then, type `gnuplot plot_vn.gp` to fit all data with
cosine functions and plot Fig.~3B.

Figure 3C:
Type `./gainchange.rb` to generate `gainchange.dat`, the data of gain
change as in Fig.~3C. The program calculates the modulation amplitude
(`a`) for each 20 steps divided by the initial amplitude.

Figure 3D:
Type `wdist w.300 gr.spk.a1 wdist.dat` to generate `wdist.dat` from
`w.300` and `gr.spk.a1` . Plot the file to obtain Fig.~3D.

Figure 3E:
You can reproduce the figure by carrying out computer simulations with
different peak firing rate of MFs by resetting the value of
`MF_maxfreq` and obtaining the modulation amplitude using `fit.rb`.

Figure 3F:
You can reproduce the figure by carrying out computer simulations with
different peak firing rate of CFs by resetting the value of
`CF_maxfreq` and obtaining the modulation amplitude using `fit.rb`.

License
-------
`okr.c` is licenses under GPLv2, because `okr.c` uses the ODE solver
in GSL. The other programs except `mt19937ar-cok.c` are under **public
domain**.

Contact
-------

If you have questions and/or comments on this program, please let me
know:

YAMAZAKI, Tadashi
RIKEN Brain Science Institute
pone11@neuralgorithm.org
http://neuralgorithm.org/

References
----------

[MT]:
http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/MT2002/CODES/MTARCOK/mt19937ar-cok.c
[GSL]: http://www.gnu.org/software/gsl/
[GD]: http://www.boutell.com/gd/

History
-------

* 20120310: Initial version
* 20120317: Final version
* 20120318: Release

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