Jon Han
Illustrator
From this contributor
The challenge of defining a neural population
Our current approach is largely arbitrary. We need new methods for grouping cells, ideally by their dynamics.
The challenge of defining a neural population
The limits of neuroscience
Truly understanding the brain requires a set of conditions we’re unlikely to meet: that knowledge about the brain is finite, and that we have both access to that knowledge and the means to understand it.
The Transmitter’s favorite essays and columns of 2024
From sex differences in Alzheimer’s disease to enduring citation bias, experts weighed in on important scientific and practical issues in neuroscience.
The Transmitter’s favorite essays and columns of 2024
Imagining the ultimate systems neuroscience paper
A growing body of papers on systems neuroscience and on giant simulations of neural circuits involves data beyond the point that anyone can reasonably understand end to end. Looking ahead, “paper-bots” could solve that problem.
Imagining the ultimate systems neuroscience paper
What makes memories last—dynamic ensembles or static synapses?
Teasing out how different subfields conceptualize central terms might help move this long-standing debate forward. I asked eight scientists to weigh in.
What makes memories last—dynamic ensembles or static synapses?
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.