Regarding Phase III, there are interesting results for the number of firing excitatory neurons in a network k=656. We have finally found that a network with 1000000 cellular automata neurons can display a sustained non-zero activity. The superimposed fluctuations should correspond to the electroencephalogram.
This is WU #668548 and has been computed by user Matthew Muresan http://falua.cesfelipesegundo.com/Neurona/show_user.php?userid=216.
In the second case (k=1030) the activity finally fades off at t=1000 but several large peaks are found before the extinction. We will continue with the analysis of the parameter space to determine the regions where the neural network behave as a real brain concerning the average behaviour of the collective of neurons.
This is WU #1052202 and has been computed by user www.car-dump.pl http://falua.cesfelipesegundo.com/Neurona/show_user.php?userid=178.
That's good news, so lets keep crunching!
PD: Edited to add information about WU id and users
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That's fascinating news. I'm glad to see that this project is returning promising results like this. It's always good to see volunteers contributing to science.
/ Congratulations!!
/ Could you explain briefly what the vertical axis is measured in, and how the higher number is significant? I'm assuming the horizontal axis is time in seconds... Thanks. :)
/ Could you explain briefly what the vertical axis is measured in, and how the higher number is significant? I'm assuming the horizontal axis is time in seconds... Thanks. :)
The vertical axis stands for the number of firing or active excitatory neurons in the network, we expect this number to fluctuate in time so both peaks and valleys are a consequence of this global statistics behaviour. The same occurs in real brain where these oscillations are called electroencephalogram (EEG) which are measured with electrodes attached to the scalp.
Once all useful solutions hare found, we will compare the network behaviour with real EEG in order to check the reliability of the model. The horizontal axis is measured in units of time of the digital model, after comparison with real data we will determine the time-scale but we can anticipate it to be in the millisecond range.
/ Thanks for the clarification. I like to have at least a general idea of what a project's results mean, and this certainly helped.
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