| Title page | Introduction | Background information | Potential benefits | Concerns | Conclusion | Reference list (written research) | Reference list (graphics) |
Scientists have been working to more accurately observe brain tissue at the cellular level (neuron imaging and analysis, now the study of connectomics) for over a century. What was originally achieved through the use of chemicals to stain neurons, with inconsistent and inexact results, has been vastly improved with modern technology (“Mapping Brain Circuits,” 2009).
One current method is to use fiber optics, in which “data travels at the speed of light” (Steinberg, 183) and is communicated as accurately as it is quickly (ibid). Applying this medium to neuroscience, scientists can use light to trigger a chain reaction of neurons, effectively illuminating a direct line of communication within a neuronal circuit (“Mapping Brain Circuits,” 2009).
Meanwhile, data are analyzed and used more in-depth and efficiently through modeling. As stated in Introduction to Computer Information Systems, “Model-based [decision support systems] depend upon the manipulation of a model or queries that define assumptions about the problem domain [i.e., specific, relevant data being analyzed] under consideration. Models incorporate data and parameters provided by decision-makers to aid in analyzing a situation” (510).
Theoretical modeling can also be done with other methods such as compartmental modeling – using single electrical circuits to represent neurons, since their behavior is analogous. This is less convenient and more time-consuming than virtual modeling, though (“Computational Neuroscience,” 2008).
Three-dimensional modeling – creating spatially accurate digital images representing a scanned object – has, in turn, become an infinitely useful tool. This digitalization of data allows for accurate, minimally invasive study and examination of the brain.
Higher-resolution digital imaging has also enabled significant advances in brain mapping and identifying neural circuitry. By combining these visual techniques with gene-targeting technology and fluorescent, colored proteins, scientists have created brilliant, detailed images known as “brainbows:” color-coded photographs of “structures and connections in the brain” (Frankel, 2008). Above and below: "brainbow" mapped mouse brain, Livet et al., 2007; and illuminated human brainstem, Lichtman/Harvard University, 2008; respectively.
Modeling software and the tools to develop and share (and neuron-simulating platforms to further use) it – “to share neuroimaging results and enable meta-analysis of studies of human brain function and structure” (http://brainmap.org/, 2003-2011) – have become widely available online by using Java (to operate programs across different operating systems) and XML (to share data across different systems) languages and a mix of tools to store, analyze, and organize data (http://www.brainmap.org/, 2003-2011; Howell; UCL, 2012; Perlewitz).
These are just a few instances of open-source software being used in the field of neuroscience to make neuron modeling and data analysis available to a wider range of scientists (and students).
This is part of a larger movement toward sharing software, services, and information, also known as infrastructure convergence – in effect, cloud computing, enabled by increasingly high-bandwidth (i.e., faster and more powerful) Internet connections.
Cloud computing includes Software as a Service (SaaS), Infrastructure as a Service (IaaS), and Platform as a Service (PaaS). The service being provided is utility computing, through which users can bypass traditional hardware.
“Open-source software is critical to the growth of both software-as-a-service and cloud computing, and cloud-based computing in turn is making it easier for open source vendors to lower costs” (Brodkin, 2008).
This self-perpetuating convergence of infrastructure means a greater capacity to immediately share, access, analyze, store, and manage greater amounts of data from different locations.
It is “easier and more affordable to share research data, tools, and computing power….falling prices make it more affordable to link scientific communities with…high-performance computing and to connect distributed databases and other resources…that can be accessed in real-time through a simple and user-friendly interface” (Ellisman, 2007).