Bridging the Gap between Neuroscience and Programming

Hello, my name is Lane M. Lasarsky and I am currently enrolled at Loyola University of Chicago. This concept is fascinating in the fact that it is bridging the gap between computer and man. This is the technological advancement that neuroscience is a part of as well, that might not get as much of attention, but is just as important. On the week of September 26^th, I had attended a neuroscience seminar with guest speaker, Mark Albert, PhD. His lecture was on the concept of computational neuroscience and the areas surrounding it, as well as the implication of how important it can be to upcoming neuroscientists. Which leads me to the article in which I will be referencing called "libNeuroML and PyLEMS: using Python to combine procedural and declarative modeling approaches in computational neuroscience" (Vella et al. 2014).

This article reminded me of the future he had spoken about during his lecture, and that this field is growing ever increasingly. The terms referenced within the article such as,  NeuroML and LEMS, are both languages in programming  that help develop the understanding of neural networks through mammalian species. They are useful in the mapping of intrinsic pathways that help scientists understand the facilitated path by which particular stimulus induces. However, in comparison to one another they are rather specialized by themselves and they tend to not be as user-friendly. That is why Python is a deliberate way to connect both of these languages as a powerful too and an  easy-to-use format. It is crucial in making these languages as clear as possible and allow for data to be transferred from either language. This allows for the success rate and efficiency in neuroscience research trials to be increased, due to the homogeneity Python brings to the tools of the neuroscientist.

(Fig 1.)

In (Figure 1., Vella et al. 2014), it displays the property that Python has in order to compress information from recorded data. It can group neuro-biological sub types into an array network, in order to help understand the path in which specific neurons are stimulated. This is one of the many visual insights into the power that Python holds for the future of neuroscience and the benefits within the medical field to come. 

(Fig 8.)

Within the article, it shows testing of the python program, through the use of monitoring presynaptic (green) and postsynaptic (red) neurons and their individual pathways of stimulus in a live worm  (Figure 8., Vella et al. 2014). This information of electrical gradients is processed and condensed into usable data. Then Python can use that data and consolidate into a graphical demonstration of the neuronal networks at hand. This is one of the many powers that such a language as Python, can hold for the future of neuroscience, let alone the medical field. Programming is becoming ever more useful to the understanding of biological systems and can help retrieve data much faster than previous methods. 

We can see how in the health industry it is growing tremendously with bioinformatics and with the advancement of radiological machinery. This will change people’s lives and is important in understanding the connection between the growth of both fields being neuroscience and computer science.  

Works Cited

Albert, M. (2017). Speech at a Neuroscience Seminar, Loyola University Chicago.

Vella, Michael, et al. “LibNeuroML and PyLEMS: using Python to combine procedural and declarative modeling approaches in computational neuroscience.” Frontiers in Neuroinformatics, vol. 8, 2014, doi:10.3389/fninf.2014.00038.