Modeling the Brain with NCS and Brainlab
Computer scientists have been studying artificial neural networks (ANNs) since the 1950s. Although ANNs were inspired by real biological networks like those in your brain, typical ANNs do not model a number of aspects of biology that may turn out to be important. Real neurons, for example, communicate by sending out little spikes of voltage called action potentials (APs). ANNs, however, do not model the timing of these individual APs. Instead, ANNs typically assume that APs are repetitive, and they model only the rate of that repetition. For a while, most researchers believed that modeling the spike rate was enough to capture the interesting behavior of the network. But what if some of the computational power of a biological neural network was derived from the precise timing of the individual APs? Regular ANNs could never model such a possibility.
In 1999, the thought that ANNs were overlooking the reality of individual APs convinced Phil Goodman at the University of Nevada, Reno, to change his focus from ANNs to more realistic spiking neural network models. He started by looking for a program that would allow him to conduct experiments on large networks of spiking neurons. At the time, a couple of excellent open-source research software packages existed that were capable of simulating a few spiking neurons realistically; GENESIS and NEURON were two of the most popular. But these programs were not designed to work with the networks of thousands of spiking neurons that he was envisioning. Goodman believed that with low-cost Linux clustering technology, it should be possible to construct a parallel program that was realistic enough to model the spiking and cellular membrane channel behavior of neurons, while also being efficient enough to allow the construction of large networks of these neurons for study. Goodman launched the NeoCortical Simulator (NCS) Project to create such a program. Starting with a prototype program that Goodman wrote in the proprietary MATLAB environment, a student working with computer science professor Sushil Louis wrote the first parallel version of NCS in C using the MPI parallel library package.
When I joined the research group in 2002, NCS already was undergoing a major rewrite by another student, James Frye, who was working with CS professor Frederick C. Harris, Jr. This time, the goal was to take the system from prototype to streamlined and reliable production software system. I helped with this effort, implementing a number of optimizations that greatly improved performance.
I also set up the first version control for the NCS source code, using the then-new open-source Subversion system. At the time, Subversion still was an alpha project. Nevertheless, I was sold on several features of the system, including the automatic bundling of an entire set of files into a single release. After working with Subversion a bit, the old workhorse CVS seemed cumbersome in comparison. Subversion was evolving quickly then. More than once after a system software upgrade, though, I had to spend hours trying to rebuild a Subversion executable with a certain combination of component library versions that would restore access to our version history. The Subversion user mailing list always was helpful during these recovery efforts. Eager to take advantage of the new features, I willingly paid the price for choosing alpha software. Fortunately, that trade-off is no longer necessary. Subversion now is stable and flexible, and I would not hesitate to choose it for any new project.
As the NCS software matured, our cluster expanded, thanks to several grants from the US Office of Naval Research. The initial Beowulf cluster of 30 dual-processor Pentium III machines grew with the addition of 34 dual-processor Pentium 4s. It grew again recently with the addition of 40 dual-processor Opterons. Linux has been the OS for the cluster from the start, running the Rocks cluster Linux release. The compute nodes are equipped with a full 4GB of system memory to hold the large number of synapse structures in the brain models. Memory capacity was a major motivation for moving to the 64-bit Opterons. Administrative network traffic moves on a 100MB and, later, 1GB Ethernet connection, while a specialized low-latency Myrinet network efficiently passes the millions of AP spike messages that occur in a typical neural network simulation.
With NCS now capable of simulating networks of thousands of spiking neurons and many millions of synapses, students began to use it for actual research. NCS could be quite hard to use effectively in practice, however, as I discovered when I began my own first large-scale simulation experiments. Much of the difficulty in using NCS stemmed from the fact that NCS takes a plain-text file as input. This input file defines the characteristics of the neural network, including neuron and dendrite compartments, synapses, ion channels and more. For a large neural network model, this text file often grows to thousands or even hundreds of thousands of lines.
Although this plain-text file approach allows a great deal of flexibility in model definition, it quickly becomes apparent to anyone doing serious work with NCS that it is not practical to create network models by directly editing the input file in a text editor. If the model contains more than a handful of neural structures, hand-editing is tedious and prone to error. So every student eventually ends up implementing some sort of special purpose macro processor to help construct the input file by repeatedly emitting text chunks with variable substitutions based on a loop or other control structure. Several of these preprocessors were built in the proprietary MATLAB language, because MATLAB also is useful for the post-simulation data analysis and is a popular tool in our lab. Each of these macro processors was implemented hurriedly with one specific network model in mind. No solution was general enough to be used by the next student, therefore, causing a great deal of redundant effort.
I searched for a more general solution, both for my own work and to prevent future students from facing these familiar hurdles as they started to use NCS for large experiments. No templated preprocessing approach seemed up to the task. After a bit of experimentation, I concluded that the best way of specifying a brain model was directly as a program—not as a templated text file that would be parsed by a program, but actually as a program itself.
To understand the problem, consider that our brain models often contain hundreds or thousands of structures called cortical columns, each made up of a hundred or more neurons. These columns have complex, often variable internal structures, and these columns themselves are interconnected by synapses in complex ways. We might want to adjust the patterns of some or all of these connections from run to run. For example, we might want to connect a column to all neighbor columns that lie within a certain distance range, with a certain probability that is a function of the distance. Even this relatively simple connection pattern can't be expressed conveniently in the NCS input file, which permits only a plain list of objects and connections.
But, by storing the brain model itself as a small script that constructs the connections, we could have a model in only a few lines of code instead of thousands of lines of text. This code easily could be modified later for variations of the experiment. All the powerful looping and control constructs, math capabilities and even object orientation of the scripting language could be available directly to the brain modeler. Behind the scenes, the script automatically could convert the script representation of the model into the NCS text input file for actual simulation. No brain modeler ever would be bound by a restrictive parsed template structure again. I gave the generalized script-based modeling environment that I planned to develop the name Brainlab and set to work picking a suitable scripting language for the project.
Practical Task Scheduling Deployment
One of the best things about the UNIX environment (aside from being stable and efficient) is the vast array of software tools available to help you do your job. Traditionally, a UNIX tool does only one thing, but does that one thing very well. For example, grep is very easy to use and can search vast amounts of data quickly. The find tool can find a particular file or files based on all kinds of criteria. It's pretty easy to string these tools together to build even more powerful tools, such as a tool that finds all of the .log files in the /home directory and searches each one for a particular entry. This erector-set mentality allows UNIX system administrators to seem to always have the right tool for the job.
Cron traditionally has been considered another such a tool for job scheduling, but is it enough? This webinar considers that very question. The first part builds on a previous Geek Guide, Beyond Cron, and briefly describes how to know when it might be time to consider upgrading your job scheduling infrastructure. The second part presents an actual planning and implementation framework.
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- SUSE LLC's SUSE Manager
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- Doing for User Space What We Did for Kernel Space
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This ebook takes a look at some of the practical applications of the Linux on Power platform and ways you might bring all the performance power of this open architecture to bear for your organization. There are no smoke and mirrors here—just hard, cold, empirical evidence provided by independent sources. I also consider some innovative ways Linux on Power will be used in the future.Get the Guide