<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>week 7 HW: genetic circuits part II :: 2026a-devorah-wertheimer</title><link>https://pages.htgaa.org/2026a/devorah-wertheimer/homework/week-07-hw-genetic-circuits-part-ii/index.html</link><description>Part 1: Intracellular Artificial Neural Networks (IANNs) What advantages do IANNs have over traditional genetic circuits, whose input/output behaviors are Boolean functions? Traditional genetic circuits can only read a signal as ON/OFF, even though molecules inside a cell exist at all kinds of intermediate concentrations. To build something complex out of ON/OFF switches, you have to layer many of them together, and each added layer introduces new opportunities for components to accidentally influence each other or fall out of sync. IANNs instead pass graded responses between nodes. Each node receives an actual concentration value, weighs it, and passes a continuous output forward. This means a single node carries far more information than an ON/OFF switch, so you need fewer of them to represent something complex, and there are fewer points at which things can go wrong.</description><generator>Hugo</generator><language>en</language><atom:link href="https://pages.htgaa.org/2026a/devorah-wertheimer/homework/week-07-hw-genetic-circuits-part-ii/index.xml" rel="self" type="application/rss+xml"/></channel></rss>