Tag: Brain Science

“As funders of public health research, we need to ensure that research outputs are used to maximize knowledge and potential health benefits. In turn, the populations who participate in research, and the taxpayers who foot the bill, have the right to expect that every last ounce of knowledge will be wrung from the research.”

The above statement concludes the first paragraph of a comment “Sharing research data to improve public health” published January 7, 2011 in the Lancet and signed by 17 major biomedical research funding institutions from across the globe. The published comment goes on to state that data are often treated as private property by investigators “who aim to maximize their publication record at the expense of the widest possible use of the data.”

Note: Excerpts of a joint statement of purpose are published in the comment. The full statement is available online here.

Since taxpayers foot most of the bill for research, the data are really owned by the public. Nevertheless, the competition is intense for the biomedical researcher working to build his or her career. The main determining factor for getting ahead is the number of papers that the individual has published. Funding agencies have an obligation to maximize data use – an obligation to both the people footing the bill and also to the subjects that participate in the research – but they also have an obligation to the research scientists who put so much of their life into time consuming, difficult, and extremely important research.

Meeting all of these apparently contradictory obligations may be less than the impossible task that it seems at first glance. Transition the biomedical research culture – the funders, the university administrations that promote their scientists, etcetera – to appreciate and fund the scientists’ producing solid data and producing the largest impact on their field. Not by counting their direct publications. This is the easiest thing to do but has lead to a massive amount of literature that is redundant or worse. With current technology we can easily quantify the amount of data scientist contributes to online data repositories. We can also easily track the use of those data. Perhaps these measures could help to replace the old measure of the number of papers published.

The best way to understand a theory or a set of data or even a mathematical equation is to play with them. A couple of days ago my blog post “Polychronization: Playing with the Code” began looking at the standard C++ polychronization program spnet.cpp and the newer program poly_spnet.cpp implemented with a more precise algorithm. The poly_spnet.cpp file also includes code for finding polychronous groups. In today’s post I provide a few more bits of information about the code in these two files and their data output that hopefully makes it easier for you to play with them.

Note: C++ and MATLAB versions of the computational model are available from the SenseLab’s ModelDB repository in the “Polychronization: Computation With Spikes (Izhikevich 2005)” record. For more about polychronization and the paper associated with this model please see my earlier blog post “Spontaneously Formed Neuronal Groups Far Exceed the Number of Neurons.”

The spnet.cpp model is set to run for 24 simulation hours or 86,400 seconds (60 seconds/minute * 60 minutes/hour * 24 hours) and the program automatically saves the previous second of simulation data to the spikes.dat file. Each earlier second is overwritten as the latest second is saved.

The poly_spnet.cpp model is set to run for 5 simulation hours or 18,000 seconds (60 seconds/minute * 60 minutes/hour * 5 hours) and the program automatically saves data in five files: all.dat, dat.dat, s.dat, spikes.dat, and polyall.dat. I’ll address the contents of the all.dat and polyall.dat files at a future date. Below the contents of the dat.dat, s.dat, and spikes.dat files are briefly explained.

The first column of the dat.dat file contains the simulation time in seconds. The second column contains the average firing rate of the population of excitatory neurons and the third column seems to contain the average excitatory neuron to excitatory neuron synaptic strength across the population. The forth column of numbers contains the average firing rate of the population of inhibitory neurons. Figure 1 above contains an x-y plot of the first and second columns (bottom plot), which contains 18,000 points with each point representing the excitatory neuron population’s firing rate in Hertz during a particular second of the simulation. The figure also contains an x-y plot of the first and forth columns (top plot), which contains 18,000 points with each point representing the inhibitory neuron population’s firing rate in Hertz during a particular second of the simulation.

The first column of the s.dat file contains the number identifiers for 1,000 simulated neurons. The second column contains the total synaptic weight onto the postsynaptic neuron identified in the first column. Synaptic weights change over time due to synaptic plasticity. In the file we can see the total synaptic weight change for any one neuron. However, for some reason time stamp data are not saved to s.dat. Of course, this can be easily changed in the code.

The spikes.dat file contains the number identifiers for 1,000 simulated neurons in its first column and associated times (in milliseconds) of spike occurrence in its second column. The format is the same as in the spikes.dat output of the spnet.cpp code and so is the way the data are saved. Only the most recent second of data are saved in the file.

Other related blog posts:

Spontaneously Formed Neuronal Groups Far Exceed the Number of Neurons

Polychronization: Playing with the Code