Either open a terminal and type "python CBGTC.py" or open Spyder and run the CBGTC.py file.
The program will simulate the contracting basal ganglia model (CBG) in three tests:
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The first one replicates the test used in (Gurney et al 2001b) to generate their fig. 2a, also used in (Girard et al 2008) to generate fig 3. The terminal output should provide information about the performance of the program in each of the five steps. To generate new versions of the aforementioned figures, log files have to be used...
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The second one successively provides the model with 200 random input vectors, and computes 5 different scores:
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the first one reflects how frequently the maximum input is the disinhibited one after convergence,
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the second one computes the selection contrast between the channel with maximum input and its strongest competitor,
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the third one computes the amplification in the frontal cortex of the channel with maximum input with regard to its raw input,
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the fourth one compares the amplification of the winning channel with its strongest competitor's amplification (amplification contrast),
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the last one is the average time needed to reach the convergence of selection process.
This test writes on the terminal the average value of each of these scores.
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The third one replicates (Prescott et al 2006) systematic salience search used to generate their figure 7, as well as (Girard et al 2008) fig. 4. Only efficiency of channels 1 and 2 is computed with the provided code, using them to compute efficiency and distortion is straightforward (eqns. 7 and 8 in the Prescott et al paper).
The corresponding log files are stored in the log/ directory:
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BG and ThFC respectively contain the activity of the each neuron of the basal ganglia and thalamo-cortical modules at each time step. The GPi/SNr output used to generate (Girard et al 2008) fig. 3 has to be read in columns 26, 27 and 28 of BG_CBG.
With gnuplot, this should do the trick : plot 'BG_GPR' using 26 with lines, '' u 27 w l, '' u 28 w l
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e1 and e2 contain the matrices of the efficiency scores of channel 1 and 2 for the third test.
The program will try to use the psycho library to increase computation speed, if not available, a 'Psyco not available.' message will be displayed, but the program should compute without problem.
The basalganglia.py and thalamusFC.py respectively define the basalganglia and thalamusFC classes. The basalganglia corresponds to the intrinsic basal ganglia circuitry (Striatum, GPe, GPi, SNr), the thalamusFC to the thalamo-cortical loop (Th, TRN and FC).
The CBGTC.py defines the CBGTC class, which contains one instance of the basalganglia object and one instance of the thalamusFC, allowing the simulation of a complete cortico-baso-thalamo-cortical loop. It contains the various test functions allowing the replication of some of the papers figures.
The program contains the code to also run the GPR model (Gurney et al 2001a), just create a CBGTC object with the model set to 'GPR' (just look at the main() code in CBGTC.py).
You can also use some custom CBG model, i.e. using lPDS neurons and the CBG structure, but with your own parameters. Create a CBGTC object with model set to 'CBGcustom', and with a vector containing the 25 parameters (once again, look at the main() in CBGTC.py).
These three classes (basalganglia, thalamusFC, CBGTC) have the same generic architecture:
stateReset() sets all neurons to 0
paramInit(opt_param) defines the synaptic weights and the neuron biases, opt_parameter being used for the CBGcustom type of model only.
stepCompute(dt,inputs) updates the model state, integrating over timestep "dt" and salience input "salience", using the (very) basic Euler method.
logAll() obviously logs the internal state of the model in the dedicated log files in the log/ directory.
The programs are commented (at least a bit), do not hesitate to dig into them to get more details about the other functions.