Brains, Behavior, and Complexity Curriculum
We have developed a meta-curriculum that is meant to complement the education students might receive from curricula and resources from organizations such as Neuromatch Academy (NMA), International Society for Artificial Life (ISAL), the Santa Fe Institute, and UC Berkeley Data Science.
Topical Areas:
Computational Neuroscience and Deep Learning
You can participate in NMA by participating as an observer for both the Computational Neuroscience and Deep Learning tracks, and following the syllabus (Computational Neuroscience, Deep Learning) If you are interested in gaining competencies in the areas of Deep Learning, Computational Neuroscience, and Artificial Intelligence, please view the resources hosted in the Precourse Github repository and Tutorial Github repository. These resources are essential for getting normalized in terms of Python programming, Math skills (linear algebra, stats, and calculus), and basic Neuroscience.
Artificial Life
Check out the ISAL Summer School 2021, which includes a series of lectures and discussions on special topics in Artificial Life. The Bibites YouTube channel also features hands-on content related to evolving digital organisms.
Complexity Theory
Another excellent set of educational materials comes from Complexity Explorer (sponsored by the Santa Fe Institute). Their collection of tutorials and full-length courses include Differential Equations, Information Theory, Agent-based Modeling using NetLogo, and Game Theory. You can also draw upon past lectures and events at the Santa Fe Institute going back to 2017. Dirk Brockmann hosts a set of tutorials called Complexity Explorables, which provide interactive visualizations that help make concepts related to complexity understandable. The Principles of Biodesign course from the Complex Systems Lab, University of Pompeu Fabra explores the intersection of biology and complex systems.
Network Science
As a subset of complexity theory, you will also need to learn more about the methods associated with complex networks. NetSciEd has also prepared a set of materials on network literacy, covering core concepts and ideas. A counterpart to this resource is the Atlas for the Aspiring Network Scientist from Michele Coscia. If you are interested in more in-depth methods, here is a resource on network science measurements from Lars Juhl Jensen. In addition, Aaron Clauset teaches two short virtual courses: one on Network Science, and the other on Learning from Data.
Data Science and Machine Learning
A third set of learning materials (for data science) can be found through the UC Berkeley Foundations for Data Science Course. At Data 8, you will syllabi from semesters past on a number of topics, including cause and effect, basic statistics, and data manipulation. More in the Machine Learning vein, Open World Lifelong Learning (OWLL) also offers course materials on the various facets of continual learning. Check out Continual AI for a good set of community resources on the topic.
Active Inference
We also recommend the resources of the Active Inference Lab. Their live streams, model streams, and guest streams cover many topics related to active inference, the free energy principle, allostatic control, and computational models of brain and behavior more generally.
Neurosimulation
We recommend the following resources: Terence Stewart's Simulating Neurobiological Systems course at University of Waterloo (YouTube), Uri Alon's (Weizmann Institute) courses on YouTube, including Hormone Circuits and Systems Biology, and a list of computational neuroscience resources from the Neural Reckoning blog.
Neuroevolution
Check out The Braining Club on YouTube, and their discussion group series on "Brains in Time". More general evolution resources can be found at Scitable (run by Nature Education).
Soft Materials, Organoids, and In vitro Models
We recommend a number of resources in this area. The first is an interview with Madeline Lancaster, where she discusses how to get started in the world of 3-D Brain Organoids. If you are interested in soft materials and their applicability to living systems, please check out the SLAAM (Soft Living Active Adaptive Matter) and Cell Migration seminar series. Also be sure to look over the CompuCell3D seminar series on YouTube.
Open-source Education and Hackathons
Open-source education begins with participation. Neurohackademy offers a data-science summer school focused on participation, and NeuroTechX offers similar resources in the areas of Neuroscience and Neuroengineering.
Participatory Education and Mentorship:
Neuromatch Academy
If a student wants to take this a step further and engage with some of the topics, they can create a study artifact and push it to Orthogonal Research and Education Lab's Neuromatch Precourse Github repository. These study artifacts include, but are not limited to: creating a "Hello World" example in a CoLab notebook, collections of Neuroscience references, or sharing resource lists for specialized math instructional tutorials. You can also create explainer videos for a topic (short tutorials; contact us for more information). Creating study artifacts will assist you in learning through practice.
We also have a series of tutorials that might help you understand concepts in the primary NMA course materials, or additional parallel concepts. The first table includes a general set of resources providing quick refreshers on quantitative/computational methods and open science methods. This also includes topics in machine learning from the DevoWormML short course. The second table includes several supplemental topics (various Computational and Neurobiological topics) covered during our Saturday Morning NeuroSim meetings last summer.
Topical Areas:
Computational Neuroscience and Deep Learning
You can participate in NMA by participating as an observer for both the Computational Neuroscience and Deep Learning tracks, and following the syllabus (Computational Neuroscience, Deep Learning) If you are interested in gaining competencies in the areas of Deep Learning, Computational Neuroscience, and Artificial Intelligence, please view the resources hosted in the Precourse Github repository and Tutorial Github repository. These resources are essential for getting normalized in terms of Python programming, Math skills (linear algebra, stats, and calculus), and basic Neuroscience.
Artificial Life
Check out the ISAL Summer School 2021, which includes a series of lectures and discussions on special topics in Artificial Life. The Bibites YouTube channel also features hands-on content related to evolving digital organisms.
Complexity Theory
Another excellent set of educational materials comes from Complexity Explorer (sponsored by the Santa Fe Institute). Their collection of tutorials and full-length courses include Differential Equations, Information Theory, Agent-based Modeling using NetLogo, and Game Theory. You can also draw upon past lectures and events at the Santa Fe Institute going back to 2017. Dirk Brockmann hosts a set of tutorials called Complexity Explorables, which provide interactive visualizations that help make concepts related to complexity understandable. The Principles of Biodesign course from the Complex Systems Lab, University of Pompeu Fabra explores the intersection of biology and complex systems.
Network Science
As a subset of complexity theory, you will also need to learn more about the methods associated with complex networks. NetSciEd has also prepared a set of materials on network literacy, covering core concepts and ideas. A counterpart to this resource is the Atlas for the Aspiring Network Scientist from Michele Coscia. If you are interested in more in-depth methods, here is a resource on network science measurements from Lars Juhl Jensen. In addition, Aaron Clauset teaches two short virtual courses: one on Network Science, and the other on Learning from Data.
Data Science and Machine Learning
A third set of learning materials (for data science) can be found through the UC Berkeley Foundations for Data Science Course. At Data 8, you will syllabi from semesters past on a number of topics, including cause and effect, basic statistics, and data manipulation. More in the Machine Learning vein, Open World Lifelong Learning (OWLL) also offers course materials on the various facets of continual learning. Check out Continual AI for a good set of community resources on the topic.
Active Inference
We also recommend the resources of the Active Inference Lab. Their live streams, model streams, and guest streams cover many topics related to active inference, the free energy principle, allostatic control, and computational models of brain and behavior more generally.
Neurosimulation
We recommend the following resources: Terence Stewart's Simulating Neurobiological Systems course at University of Waterloo (YouTube), Uri Alon's (Weizmann Institute) courses on YouTube, including Hormone Circuits and Systems Biology, and a list of computational neuroscience resources from the Neural Reckoning blog.
Neuroevolution
Check out The Braining Club on YouTube, and their discussion group series on "Brains in Time". More general evolution resources can be found at Scitable (run by Nature Education).
Soft Materials, Organoids, and In vitro Models
We recommend a number of resources in this area. The first is an interview with Madeline Lancaster, where she discusses how to get started in the world of 3-D Brain Organoids. If you are interested in soft materials and their applicability to living systems, please check out the SLAAM (Soft Living Active Adaptive Matter) and Cell Migration seminar series. Also be sure to look over the CompuCell3D seminar series on YouTube.
Open-source Education and Hackathons
Open-source education begins with participation. Neurohackademy offers a data-science summer school focused on participation, and NeuroTechX offers similar resources in the areas of Neuroscience and Neuroengineering.
Participatory Education and Mentorship:
Neuromatch Academy
If a student wants to take this a step further and engage with some of the topics, they can create a study artifact and push it to Orthogonal Research and Education Lab's Neuromatch Precourse Github repository. These study artifacts include, but are not limited to: creating a "Hello World" example in a CoLab notebook, collections of Neuroscience references, or sharing resource lists for specialized math instructional tutorials. You can also create explainer videos for a topic (short tutorials; contact us for more information). Creating study artifacts will assist you in learning through practice.
We also have a series of tutorials that might help you understand concepts in the primary NMA course materials, or additional parallel concepts. The first table includes a general set of resources providing quick refreshers on quantitative/computational methods and open science methods. This also includes topics in machine learning from the DevoWormML short course. The second table includes several supplemental topics (various Computational and Neurobiological topics) covered during our Saturday Morning NeuroSim meetings last summer.
Image credit: "Binary Sunset" by India Mayes