Biology 2299 - Inquiries in Biological Sciences: Section 10:

The Neural Control of Real-World Behavior

Instructor: Jonathan Samir Matthis
Email: j [dot] matthis [at] northeastern [dot] edu
Location: Snell 125
Class Time: Mon, Weds 2:50-4:30
Github Repo Link: https://github.com/Biol2299-Matthis-Fall2022/Biol2299-Matthis-Fall2022
Zotero Group Library Link: https://www.zotero.org/groups/4793329/fall2022_biol2299_neurorealworld Gitter Chat Room Link: https://gitter.im/Biol2299-Matthis-Fall2022-chat/community (rendundant to the chat with gitter badge above)

Office Hours:

• Book a Zoom meeting with me - https://calendly.com/jonmatthis/30min

• Book an in-person meeting - https://calendly.com/jonmatthis/biol2299-in-person

Schedule

Week	Monday	Topic	Wednesday	Topic
1	2022-09-05	NO CLASS - LABOR DAY ✅	2022-09-07	First day of class - Setup and Introductions ✅
2	2022-09-12	Alissa Link - Library Research Session ✅	2022-09-14	Intro to The Situation: Philosophy of Science ✅
3	2022-09-19	SI Units and Center of Mass analysis of human movement ✅	2022-09-21	Intro to Zotero and Structure of Scientific Papers ✅
4	2022-09-26	Zotero Group - Building our shared bibliography ✅	2022-09-28	Center of Mass - Data Collection (1 of 3) ✅
5	2022-10-03	Center of Mass - Analysis (2 of 3) ✅	2022-10-05	Center of Mass - Analysis (3 of 3) Lit Review ✅
6	2022-10-10	NO CLASS - Indigenous People’s Day	2022-10-12	Literature Review and rest-of-class planning ✅
7	2022-10-17	TBD ✅	2022-10-19	Exam #1 ✅
8	2022-10-24	-Poster Prep - Project Planning-	2022-10-26	-Poster Preparation-
9	2022-10-31	-Poster Preparation-	2022-11-02	-Poster Preparation-
10	2022-11-07	-Poster Preparation-	2022-11-09	-Poster Preparation-
11	2022-11-14	POSTER DAY 1	2022-11-16	POSTER DAY 2
12	2022-11-21	Aaron Lecture?	2022-11-23	NO CLASS - THANKSGIVING
13	2022-11-28	Human Evolution and Blender Z-Anatomy	2022-11-30	Central Pattern generators and cures for paralysis
14	2022-12-05	Dragonfly Neuroscience	2022-12-07	Class Wrap-Up

Course description

Understanding the neural underpinnings of real-world natural behavior is (one of) the primary desiderata of neuroscience, but technological and methodological constrains limit the vast majority of research to highly artificial and constrained laboratory environments. This course will consider the study of natural behavior from a variety of angles -

• Philosophical - What is the relationship between the (imperfect) measurements of our scientific tools and the actual True Facts of the Universe?

• Technological - What tools exist for the study of real-world behavior? What new technologies may arise to assist with this study (and who will own them)?

• Scientific - What insights can be gained from existing lab-based research from the various disciplines included in the broad, vague umbrella of “natural behavior” (e.g. neuroscience, biology, biomechanics, robotics, etc)

Students will gain significant experience with the process of locating and efficiently interpreting the key results of primary research articles on a wide range of cross-disciplinary topics, as well as an introduction to emerging open-source/open-science methodologies and Python-based data analysis.

Class discussions will include a through-line of recurring, intersectional analysis of the ways that structural inequities inherent to our existing instructions of science and technology have shaped the past, present, and (if we don’t do something about it) future of scientific inquiry.

Learning Outcomes

⭐Navigating scientific literature:

• Locate a scientific paper from a citation, DOI, or PMID

• Identify critical information about it

• Rapidly extract the key findings and insights

• Evaluate figures to understand how research draw conclusions from data

• Differentiate between primary research articles and review articles

• Enter the publication into a citation management software

  • Software: Zotero

• Generate citations in written documents

Technical skill development:

• Markdown text editing

  • Software: Obsidian.md

  • Web-based tool: hackmd.io

• Basic Github navigation

  • Web-based tool: github.com

  • Cloning local copies of repositories

  • Installing dependencies

  • Commits

  • Pull requests

• Basic Python data analysis

  • Web-based tool: Google Colab

  • Software: PyCharm

Grading

Your grade from this class will be out of 400 points:

• Exam #1 - 100pts

• Exam #2 - 100pts

• Poster Presentation - 200pts

In addition, extra credit points will be awarded liberally for in and out of class participation.

These points can be applied to Exams at your discretion. Normal participation will be enough to allow you to skip Exam#2