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To figure the working of the brain will offer rewards far from mere building of smarter computers. The knowledge-gain obtained from brain study may resultantly pay dividends to the same human brain. To understand the brain working methodology means that engineers will simulate its activities, and the outcome being deeper insights regarding the ‘how’ and the ‘why’ of the brain working and failure. These simulations will act as more avenues for precision test potential biotechnology solutions methods with respect to brain disorders, like neural implants and drugs. Disorders based on neurology may in future be bypassed by innovation technology that permit wiring of foreign new materials into our human bodies to do the work of lost and damaged nerve cells. The implanted electronic gadgets might assist insanity victims to remember, crippled individuals to walk, as well as make blind persons to see.

In the next few paragraphs I will compare and contrast the reverse engineering of the brain from two journal articles. First article is by Roberta Friedman (2008) with a title of Reverse Engineering the Brain. The second article is by Jayram et al. and its title is Towards Reverse Engineering the Brain: Modeling Abstractions and Simulations Frameworks. Using these two articles I will focus on the NAE Grand Challenge in terms of problem identification, proposed solution, methods, data, conclusions, and future work.

Regarding problem identification, Roberta states that there is need for scientists to develop highly efficient simulated brains to assist produce computers that will both think and simultaneously accelerating neuroscience. This is a grand challenge. That the proposed solution in the final brain simulations is promised capability to study drug effects and that of disease and further assist in designing new therapeutic strategies. Similarly, Jayram et al expresses the challenge that to understand the brain processing principles is the biggest challenge in the present age especially by reverse engineering the neural circuits and that of computational modeling. Unlike Roberta’s case, the proposed solutions in the Jayram et al article include navigation, decision-making, and vision.

According to Roberta study, the suitable brain reverse engineering method is the top-down approach with respect to global networking brain properties as well as mathematical formulas so as to have known neuron behavior types. In addition, it focuses on accurate detailed structure and molecule to model a specific brain piece. This study contrasts with that of Jayram et al who applies the approach of VSLI to study brain circuits. Unique in the latter author is the usage of many competitive hypotheses to find explanation regarding coding nature, and so on are absent in Roberta’s.

Data from Jayram et al is gathered through latest developments in multi-electrode recording and that of brain mapping techniques such as fMRI. The data is used to understand various numerous brain-circuitry aspects. In contrast, Roberta had the data collected through use of a technique known as multiple unit patch clamp. This technique recorded many neutron ion signals at once. Another collection of data was regarding neuron-based gene activity (Rueckl, 2012).

In conclusion, Roberta gives his future work that involves research concerning simulation of the frontal eye fields of the monkey to help researchers to understand the function of perspective feedback circuitry. Jayram et al, on the other hand, states that their future study regards large-scale SNNs development on the basis of cortical vision models in varied applications. Furthermore, they will research on the work of neuromodulation in learning and that of attention tasks.

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