PB2B

Part1 journals:

1.Parekh, R., J. Yang, and V. Honavar. "Constructive Neural-Network Learning Algorithms for Pattern Classification." IEEE Transactions on Neural Networks, vol. 11, no. 2, 2000, pp. 436-451.

2.Marks, Stefan. "Immersive Visualisation of 3-Dimensional Spiking Neural Networks." Evolving Systems, vol. 8, no. 3, 2017, pp. 193-201.

Part 2: 

Article 1 Is older and written by the professor i aim to study under. it focuses more on neural-networks and studying patterns through algorithms. they're called "learning algorithms" and are used for mapping and are supposed to demonstrate how well they're able to classify patterns with close to zero error rate. Article 2 is more recent and thus features much newer information not mentioned in the previous article and its about using/improving 3-D games based off of neural-networking. both are aspects of artificial intelligence. its funny, because the information gathered in article 1 is the cold, hard, math behind neural networking with different formulas and not so much geared to using it in devices but more so improving the algorithms and having a better idea of the algorithms & data itself within networks, this data can most likely be found in the products/ reasoning within article 2. Article 2 is geared more towards a game and virtual reality, sorta like the oculus rift, and how to improve that game and aspects of it.
The question article 1 posed was whether or not constructive algorithms are a good option for neural networks and their success rate. when studying this question, since it was hard math they actually put these algorithms through tests and provided many examples and charts to analyze and model how successful they were or were not,  then drawing a conclusion based off of those calculations. Article 2 questioned the capabilities and performance of a gaming system in 2-D when technology has progressed significantly, which brought on more challenges when using a device to visualize a 3-D Neural network. article 2 argues that the device, NeoCube when paired with NeuVis, gives the best and most optimal performance platform when exploring, and analyzing larger scale neural networks.

Part 3:

Article 1: focuses on the brain and learning of an artificial intelligence program through algorithms.

• affordances: gives you the actua math behind such data and technology, a particular region
• constraint: math isn't tangible to someone outside of field, and limited. 

Article 2: focuses on a gaming system and analyzing neural networks through 3-D Modeling

• affordances: more recent technology and indulging article, easier to read
• constraints: technology is not for purchase, not perfected, 

both:

• conventions/rhetoric: proofs, formatting, brain images, jargon, technology, math
• author's purpose: to delve into this specific and booming aspect of artificial intelligence in computer engineering and gather more data.
• writing style: persuasive/ explanatory/ promotional
• audience: computer engineering, computer scientists... etc
• because they are both littered with jargon, and mathematic equations + imagery that's intangible to somebody outside the majors. 
• organizational: it is very structured, and starts off with what they plan to do, and both in a way begin to describe some terms, or where they got started. and i think that makes sense to follow a similar structure, the way they did, because if a different audience is reading this, they can have a chance at understanding what is being said.