New work w/ Zach Kelso and @madeleinecsnyder.bsky.social
www.biorxiv.org/content/10.6...
Our negative results on classical conditioning in planarian flatworms. This was surprising, given the long history of work (including sensational findings of memory transfer and retention through decapitation).

To understand decision-making, we need to truly challenge lab animals Complex, multidimensional tasks that unfold over time could reveal how different brain areas work together to support decisions.

By designing decision-making tasks that vary along multiple dimensions and truly challenge our animals, we might finally understand how multiple brain areas coordinate to drive decisions, writes @chandlab.bsky.social.

#neuroskyence

www.thetransmitter.org/decision-mak...

1/2) Just out from @alexkwan.bsky.social's group, a study showing that psilocybin silences SST interneurons but activates PV interneurons:

www.biorxiv.org/content/10.6...

#neuroscience 🧪

Scientists find a new critical point in supercooled water – Physics World X-ray experiments provide further evidence that water exists in two distinct phases

By supercooling liquid water to -63 C and probing it with ultrafast laser pulses before it could freeze, scientists at Stockholm University found that it undergoes a transition between two different liquid phases – a long-predicted liquid-liquid critical point. physicsworld.com/a/scientists... ⚛️🧪

𝗣𝘂𝗿𝗽𝗼𝘀𝗲 𝗶𝗻 𝗯𝗶𝗼𝗹𝗼𝗴𝘆
Excellent paper about purposiveness in biology. Important critique of the dynamical systems approach as a "simple" solution to the computationalism approach.
Highly relevant to the enactive approach too.
doi.org/10.1177/1059...

Emergence of multifrequency activity in a laminar neural mass model Author summary Understanding how the brain generates and coordinates rhythms across different layers is essential for uncovering the mechanisms underlying perception, memory, and cognition. In this wo...

A brain model that mimics how different cortical layers interact, producing the mix of slow and fast rhythms seen in real brains.

Emergence of multifrequency activity in a laminar neural mass model
doi.org/10.1371/jour...
#neurocience

Why neural foundation models work, and what they might—and might not—teach us about the brain These models can partly generalize across species, brain regions and tasks, suggesting that a set of machine-learnable rules govern neural population activity. But will we be able to understand them?

These models can partly generalize across species, brain regions and tasks, suggesting that a set of machine-learnable rules govern neural population activity, writes @juangallego.bsky.social. But will we be able to understand them? #neuroskyence

www.thetransmitter.org/neuroai/why-...

Schematic diagram of Kempner AI Cluster

Ever wondered how we've networked together 1,144 GPUs — including A100s, H100s, H200s, & RTX Pro 6000s — into one of the most powerful academic supercomputers for advancing #AI & #neuroscience research?

Take a closer look at the #KempnerInstitute AI cluster: bit.ly/3QcRb8M

In theory, the brain is a prediction computation representation information machine.

In reality, the brain is an anticipatory collective of dynamic living cells.

Do our metaphors matter for understanding brains?
Romain @romainbrette.bsky.social says yes...

braininspired.co/podcast/235/

More evidence for the role of alpha/beta oscillations in top-down control.
Sustained alpha oscillations serve attentional prioritization in working memory, not maintenance
doi.org/10.1162/IMAG...
#neuroscience

New article with @oudietted.bsky.social and the @dreamteamicm.bsky.social

Dream-like mental states can occur during wakefulness

Published now in @cp-cellreports.bsky.social

www.cell.com/cell-reports...

Congrats to Nicolas Decat!

‘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…

In his new book, @romainbrette.bsky.social pushes back against theories that describe the brain as a “biological computer.” In this excerpt, he challenges equating brain evolution with programming, and the universality of neural network models. #neuroskyence

www.thetransmitter.org/theoretical-...

Adult brains operate near a “critical point” where excitation and inhibition are balanced, enabling more coordinated rhythms and greater efficiency.

Brain criticality emerges with developmental shifts in frequency-specific excitation-inhibition balance
doi.org/10.64898/202...
#neuroscience

‘Digital sphinx’ raises questions about connectome models The sphinx, with a worm’s brain and a fly’s body, illustrates the potential pitfalls of using deep-learning techniques to model biological processes.

A new preprint shows that deep learning can make a fly walk realistically using a worm’s brain — exposing a fundamental problem with how connectome models are being built and sold to investors.

By @natmesanash.bsky.social

#neuroskyence

www.thetransmitter.org/systems-neur...

Unstructured transcription factor interactions enable emergent specificity How intrinsically disordered regions (IDRs) influence chromatin binding and nuclear organization of transcription factors (TFs) remains unclear. We employed proximity-assisted photoactivation (PAPA), ...

Unstructured transcription factor interactions enable emergent specificity www.science.org/doi/10.1126/... - such an interesting paper!

Researchers retract multisensory learning paper Even though one set of experiments did not hold up, the authors stand by the original conclusions of the work and plan to resubmit it as a new paper.

The authors of a Nature paper outlining a mechanism for multisensory memories in Drosophila melongaster have retracted the work after they were unable to replicate a set of imaging experiments.

By @callimcflurry.bsky.social

#neuroskyence

www.thetransmitter.org/retraction/r...

Symmetry breaking, germ layer specification and axial organisation in aggregates of mouse embryonic stem cells Mouse embryonic stem cells (mESCs) are clonal populations derived from preimplantation mouse embryos that can be propagated in vitro and, when placed into blastocysts, contribute to all tissues of the...

Symmetry breaking, germ layer specification and axial
organisation in aggregates of mouse embryonic stem cells journals.biologists.com/dev/article/... - what a gorgeous paper this is! (2014 from @amartinezarias.bsky.social and colleagues - introducing 'gastruloids'!)

A Spatiotemporal Perspective on Dynamical Computation in Neural Information Processing Systems Spatiotemporal flows of neural activity, such as traveling waves, have been observed throughout the brain since the earliest recordings; yet there is still little consensus on their functional role. Recent experiments and models have linked ...

Traveling waves may be how the brain computes, with neural dynamics mirroring changing signals through recurrent wave-like flows.
A Spatiotemporal Perspective on Dynamical Computation in Neural Information Processing Systems
pmc.ncbi.nlm.nih.gov/articles/PMC...
#neuroscience

Stoked this is finally out! We ask: how can we simulate the brain from the bottom up? It's not sufficient to grab the connectome and wire it up in silico! We need 1) ultrastructure 2) (causal) calibration data 3) functional data. Then we can build a simulation compiler. 1/

Preprint out arguing that we should build the techology to translate (compile) molecularly annotated connectomes into dynamics. I think this is incredibly important. arxiv.org/abs/2603.25713

alk Title: Subjective Feelings to Brain Mechanisms: Advancing the Science of Mood through Epistemic Iteration Abstract: What happens in our brains to generate our moods? We don't yet know. Unlike functions such as memory, which can be measured objectively, mood is typically assessed with subjective ratings, such as “On a scale of 1 to 5, how excited (or upset) are you?” At the same time, our most precise neural measurements come from nonhuman animals, who cannot report how they feel. Mood is an extreme example of a central problem in neuroscience: we need measurements to create understanding, but we need understanding to design good measurements. Once we fully understand mood, we'll know exactly how to measure it (for instance, in an animal). But how do we get there? In this talk, I will draw on the notion of “epistemic iteration,” proposed by the philosopher Hasok Chang, to describe how scientists tackled the conceptually analogous problem of understanding temperature in the 17th century. Building on this idea, we have developed a new approach to bridge the gap between behavioral and neural measures of mood. I will describe how we have used this strategy to identify a strong neural correlate of mood, reflected in the heterogeneous, persistent responses of individual neurons in monkey anterior insular cortex — a brain area implicated in human mood via lesions, fMRI, and microstimulation. Finally, I will describe how these insights are leading us to detailed accounts of how the brain converts experiences into mood and the mechanisms that keep mood within a healthy range, rather than spiraling out of control.

Photo of me, Details of talk time/location

Friends in Boston! I'm excited to visit you on Thursday at MIT, where I'll be presenting new work on mood via an intermingling of science and the philosophy of, arguing that the neuroscience of mood requires a different approach.

bcs.mit.edu/events/collo...

The System That Decides What Science Gets Published Is Breaking Down The peer review system that validates scientific research is trapped in a self-defeating cycle. A new mathematical model shows why—and what comes next.

Some first-rate science writing: For this story, @jdrakephd.bsky.social carefully read our recent paper and then we spent a very fun 90 minutes or so talking on zoom. His article that gets right to the heart of our model, explains it clearly, and then explores why it will matter in the future.

𝗥𝗲𝗽𝗿𝗲𝘀𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻𝘀 𝗶𝗻 𝗺𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗯𝗿𝗮𝗶𝗻𝘀
Great paper discussing the challenges of understanding brain representations and the models-brain correspondence
#neuroskyence
t.co/9l1VU6Pox1

In Expanding de Sitter Space, Quantum Mechanics Gets Even More Elusive | Quanta Magazine The basic shape that best describes our expanding universe is also the hardest shape for physicists to understand.

Physicists’ understanding of the quantum world breaks down in an expanding universe. Now they are looking for lessons from an unexpected source: black holes. @shalmawegs.bsky.social reports: www.quantamagazine.org/in-expanding...

Exercising fathers and metabolic health of offspring, summarized
nature.com/articles/s41...
www.cell.com/cell-metabol...

Human Gaze Behaviors Track Abstract Stimulus Categories Abstract. Categorization, or the ability to group stimuli according to behavioral relevance, is a cornerstone of abstract cognition. Neurophysiological studies in nonhuman primates have revealed that ...

Human Gaze Behaviors Track Abstract Stimulus Categories
doi.org/10.1162/JOCN...
#neuroscience

These ideas are picking up new momentum lately. Dendritic neuroscience FTW! 💪🏼

Our latest publication grapples with how the brain could implement gradient descent by sending learning targets top-down, gating plasticity with dendritic inhibition, and updating synaptic weights with biologically observed learning rules like BTSP.

www.cell.com/cell-reports...

A great entry into the proposals available for physiologically plausible gradient descent!

I think the way they use dendrite targeting inhibition in this model is particularly elegant.

Time to start testing these ideas folks!!!

#neuroscience 🧪 #NeuroAI

“Facilitatory surrounds resembled naturalistic continuations of the optimal center stimulus”

Excellent: this physiology data supports our recent finding using patterned stimulation—together suggesting that visual cortex neurons go beyond the classical RF and reflect the structure of natural vision.