I now hope that my book will be useful to a new generation of neuroscientists!

I was able to include pretty much any figure published in the journals of the consortium.

For all the (legitimate) complaints we have about paying exorbitant fees to them as authors of research articles, they do make cross-licensing of material and disseminating the collective knowledge in their publications quite easy.

Another surprise was the process of reproducing material from other sources (e.g. figures from journal articles). My publisher is an arm of Elsevier, which is part of the STM consortium, as is Springer and most other scientific (for profit) publishers.

No. That’s not how it works. Once you get the editorial go ahead, you can pretty much write whatever you want. Wild!

After authoring research articles over many years, I was wondering how different the process would be for writing a textbook. Biggest difference: after submitting the first draft of the manuscript, I was waiting to get back 200 pages of reviewer comments ripping my points to shreds.

I have been developing this curriculum for my own NSC 3275 course at Vanderbilt and had the opportunity to refine and adjust, over a few iterations.

I wanted a reference for my own students, and students of labs around the world doing similar research, that contains the essentials of brain structure, research methodologies, and the neural basis of cognitive functions

It is not that the information is not available; quite the opposite, it is often overwhelming.

Writing a book in the era of ChatGPT may seem quaint but I decided to do it to address a real need: students interested in brain research, particularly in neurophysiology, arrived with a fragmented knowledge of the topic.

Working Memory Working Memory provides a comprehensive examination of the neural basis of working memory, defined as the cognitive ability to maintain information ov

Very excited to announce that my textbook on “Working Memory” is now available for pre-order!
shop.elsevier.com/books/workin...
A few thoughts

Is that ChatGPT's take on Goya?

A screen capture of AIMBE's congressional letter campaign "Protect U.S. Scientific Leadership and Reject FY2027 Proposed Cuts."

🚨✍️ AIMBE urges the scientific community to take a minute to send our new congressional letter opposing the President’s FY2027 budget proposal: bit.ly/420ig1A

These proposed cuts to our scientific enterprise (e.g., NIH & NSF) would jeopardize training, research, and innovation driving our economy.

In the pantheon of NIH euphemisms and oxymorons, "competitive - not discussed" must be near the top.

Enough is enough

Many thanks to my students and particularly Rye Jaffe (not in social media) who collected these data through a herculean effort. (end/10)

Despite divergent decoding results, PCA revealed increased separation of population response trajectories in neural state space across all tasks, suggesting a generalizable prefrontal learning mechanism. (10/10)

Decoding of task variables again showed variability and task-specific patterns: improved spatial match-nonmatch task decoding, little change in object match-nonmatch task decoding, and disappearance of a saccade bias in the object choose-match task. (9/10)

Specifically, reward/error outcome explained less firing rate variance with training, as subjects shifted from trial-by-trial feedback reliance to internalizing the deterministic task rule (8/10)

What did change consistently was the proportion of firing rate variance unexplained by sensory and motor variables, which increased significantly from novice to expert training across all three tasks (7/10)

Firing rate analysis recapitulated the human studies. There was no consistent change with training beyond a slight trend of increase in baseline; instead, it moved in different directions by task. A significant decrease was present during the choice epoch across tasks (6/10)

We analyzed 3,383 single neurons in three tasks: a spatial match-nonmatch, an object match-nonmatch, and an object choose-match task. (5/10)

We introduce a behavioral metric of progress in task acquisition we term "Drop in Performance" (DIP) at trial reversals. As subjects learned the stimulus-reward rule, DIP magnitude decreased significantly across all subjects and tasks. (4/10)

We therefore set out to examine how rule learning changes prefrontal cortex activity across three spatial and object working memory tasks in four non-human primates implanted with chronic electrode arrays (3/10)

The effects of training to perform a new task on neural activity have been difficult to pin down. A debate in the human fMRI literature has to do with whether after training higher BOLD activity emerges due to higher neural recruitment or lower due to improved efficiency (2/10).

New preprint from the lab!
Prefrontal Mechanisms of Rule Learning
doi.org/10.64898/2026.…
(1/10)

Screenshot of the Artemis II tracker showing the path it will take to get to the Moon and back

You can keep track of Artemis II's progress here: artemistracker.com

Great, continuous submission for NIH applications is over
===

Color Game — How Well Can You Remember Colors? We show you colors. You recreate them from memory. Challenge friends to beat your score. It's harder than you think. Play free at dialed.gg.

Visual #workingmemory peeps, you're going to love this. This is super challenging - might give you pause on how we are measuring color memory...

My color memory is a 40.4/50. Please do worse so I feel better.
dialed.gg?c=3PCHDE

Sources: White House to propose 20 percent cut to NIH funding The White House is expected to ask Congress to cut National Institutes of Health spending by 20 percent in the president’s fiscal 2027 budget request, according to multiple sources with knowledge of t...

🧪⚛️ We are going to have to go through all this again, even as Congress fails to press OMB for the agencies to actually have their appropriations for the present year. rollcall.com/2026/03/27/s...