Margeaux Walter
Illustrator
From this contributor
How to teach programming in the age of AI
Scientists and educators are concerned about students using artificial intelligence to shortcut their learning. But there are also opportunities, especially when it comes to teaching neuroscience students how to code.
How to teach programming in the age of AI
How to teach this paper: ‘Neurotoxic reactive astrocytes are induced by activated microglia,’ by Liddelow et al. (2017)
Shane Liddelow and his collaborators identified the factors that transform astrocytes from their helpful to harmful form. Their work is a great choice if you want to teach students about glial cell types, cell culture, gene expression or protein measurement.
How to teach students about science funding
As researchers reel over the uncertain state of U.S. federal funding, educating students on the business of science is more important than ever.
How to teach students about science funding
How to teach this paper: ‘Coordination of entorhinal-hippocampal ensemble activity during associative learning,’ by Igarashi et al. (2014)
Kei Igarashi and his colleagues established an important foundation in memory research: the premise that brain regions oscillate together to form synaptic connections and, ultimately, memories.
How to teach this paper: ‘Behavioral time scale synaptic plasticity underlies CA1 place fields,’ by Bittner and Milstein et al. (2017)
Katie Bittner, Aaron Milstein and their colleagues found that cellular learning can happen over longer timescales than Hebb’s rule predicts. How long should we wait to teach students about this phenomenon?
Explore more from The Transmitter
Single-gene systems-level effects, and more
Here is a roundup of autism-related news and research spotted around the web for the week of 6 April.
Single-gene systems-level effects, and more
Here is a roundup of autism-related news and research spotted around the web for the week of 6 April.
‘The Brain, In Theory,’ an excerpt
In his new book, Brette pushes back against theories that describe the brain as a “biological computer.” In this excerpt from Chapter 4, he challenges equating brain evolution with programming, and the universality of neural network models.
‘The Brain, In Theory,’ an excerpt
In his new book, Brette pushes back against theories that describe the brain as a “biological computer.” In this excerpt from Chapter 4, he challenges equating brain evolution with programming, and the universality of neural network models.
Computational neuroscientist Keith Hengen explains his work through illustrations
The images help him communicate the “big-picture ideas” behind the mathematical principles of neuronal networks.
Computational neuroscientist Keith Hengen explains his work through illustrations
The images help him communicate the “big-picture ideas” behind the mathematical principles of neuronal networks.