Samuel Eckmann

Dendrites help neuroscientists see the forest for the trees Dendritic arbors provide just the right scale to study how individual neurons reciprocally interact with their broader circuitry—and are our best bet to bridge cellular and systems neuroscience.

Dendritic arbors provide neuroscience with an ideal opportunity to study how individual neurons interact with their broader circuitry and are the field’s best bet to bridge cellular and systems neuroscience, writes @justinkohare.bsky.social.

#neuroskyence

www.thetransmitter.org/dendrites/de...

Compact deep neural network models of the visual cortex Nature - Parsimonious deep neural network models can be used for prediction of visual neuron responses.

DNN models of the brain are getting bigger. Are we replacing one complicated system in vivo with another in silico?

In new work, we seek the *smallest* DNN models of visual cortex, balancing prediction with parsimony.

It turns out these compact models are surprisingly small!

rdcu.be/e5H8G

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Do neighboring #PlaceCells in #Hippocampus CA1 map neighboring locations in space? 🧠🗺️
Is there micro-scale spatial topography in the hippocampus?
See our latest paper from @kavlintnu now out in @pnas.org www.pnas.org/doi/10.1073/...

Temporal Coding rather than Circuit Wiring allows Hippocampal CA3 Neurons to Dynamically Distinguish Different Cortical Inputs https://www.biorxiv.org/content/10.64898/2026.02.20.707083v1

Place cells in CA1 lack topographical organization of firing locations | PNAS Topography is a well-described and well-known concept for cortical organization in primary sensory and motor cortices of mammalian brains. Similar ...

A half-century old question may have its final answer. Using high-resolution #Mini2P microscopes, we find no evidence of local topography in #PlaceCells. Place fields of neighbouring cells are no more similar than those of randomly selected cells. 🧠🗺️ Out now in @pnas.org www.pnas.org/doi/10.1073/...

New preprint from the lab! Stefan Sun and @q-perrenoud.bsky.social collaborated to look at how sensory experience affects the adult cortex. Using ephys and 2-photon imaging, they found that varied visual experience reorganizes dendrite-targeting inhibitory circuits in V1.

Pavlov would have been shocked and stoked by this study!

It meticulously shows that what matters most for cue-reward learning is the duration between rewards: the longer this duration, the fewer the number of trials needed for behavioral and DA learning

www.nature.com/articles/s41...

Motor sequence generation _without_ cortico-thalamic loops!

Visual language models show widespread visual deficits on neuropsychological tests - Nature Machine Intelligence Tangtartharakul and Storrs use standardized neuropsychological tests to compare human visual abilities with those of visual language models (VLMs). They report that while VLMs excel in high-level obje...

Our latest paper, “Visual language models show widespread visual deficits on neuropsychological tests”, is now out in Nature Machine Intelligence: www.nature.com/articles/s42...

Non-paywalled version:
arxiv.org/abs/2504.10786

Tweet thread below from first author @genetang.bsky.social...

Position-independent emergence of neocortical neuron molecular identity, connectivity and function - Nature Neuroscience Even when neocortical neurons form in abnormal locations, they retain their identity and function, revealing that brain circuit formation can be guided by intrinsic developmental programs rather than ...

I'm very excited to finally see this one out in @natneuro.nature.com ! It has been an incredible journey alongside extremely talented people! Thanks to everyone involved and enjoy the reading! 1/n
www.nature.com/articles/s41...

We had a lot of fun doing these experiments! Theta sweeps in MEC and internal direction signals in parasubiculum track moving objects during pursuit and reverse during backward movement. Simultaneously recorded HD cells in other areas remain locked to head direction across behaviors:

I agree so long as the computational models make predictions that other models do not, and those predictions are then tested.

"How much of the brain's learned algorithms depend on the fact it is a brain?" arxiv.org/abs/2601.02063 The brain is a neural network, but also a biological organ (unlike artificial neural networks). How much does this matter to cognition?

What's the relation between voltage and calcium in dendrites? Xiang Wu studied this in CA2 hippocampal pyramidal cells in behaving mice. www.biorxiv.org/content/10.6...

Beyond Trafficking: Auxiliary Proteins as Multi-Domain Regulators of Neuronal Computation Auxiliary proteins aren’t just molecular bystanders and supporters, they’re computationally active and play an integral role in intracellular communication.

New Journal Club explores TARPs as multi-domain regulators that couple synaptic strength to intrinsic excitability—providing a molecular substrate for coordinated adaptation across timescales.
open.substack.com/pub/grounded...

"motor cortical engagement is sustained under conditions of sensorimotor uncertainty and dissipates rapidly when sensorimotor contingencies are resolved"

when doing neuroscience projects I often advocate for computational modelling, followed by data analysis to test model's predictions.

however a few times now I have had pushback from collaborators/reivewers suggesting it would be better to do the data analysis first, then the modelling.

thoughts?

I guess both is true. For me, 'add-on' models usually make the gap between a conceptual idea and the actual understanding very obvious – and, depending on the extent of the gap, are oftentimes unsatisfactory.

"In contrast to reactivation of rodent place cells, reactivation of human concept cells does not reflect sequences of events in human experience."

"It is essential for the field to systematically explore objectives and data curricula that might cause DNNs to learn more human-like visual representations"

hammer -> nail

New preprint from the lab! 🚀
We find that hippocampal OLM interneurons provide a circuit-level inhibitory feedback signal that dynamically controls when and where behavioral timescale synaptic plasticity can occur.
Feedback welcome!

Better artificial intelligence does not mean better models of biology Deep neural networks (DNNs) once showed increasing alignment with primate perception as they improved on vision benchmarks, raising hopes that advances in artificial intelligence (AI) would naturally ...

Published @cp-trendscognsci.bsky.social with @drewlinsley.bsky.social & @tonyfeng.bsky.social: As vision models scale to human/superhuman accuracy, they’re becoming worse models of primate vision—benchmark engineering isn’t neuroscience. @carneyinstitute.bsky.social @browncopsy.bsky.social

What is the computational role of dendritic excitations? Byung Hun Lee and team mapped voltage dynamics throughout the dendritic trees of CA1 pyramidal neurons in mice navigating in virtual reality. www.biorxiv.org/content/10.6...

2025: A Review of the Year in Neuroscience Enlightening the brain

Just published my review of neuroscience in 2025, on The Spike.

The 10th of these, would you believe?

This year we have foundation models, breakthroughs in using light to understand the brain, a gene therapy, and more

Enjoy!

medium.com/the-spike/20...

Dendrite-targeting OLM interneurons regulate the formation of learning-related CA1 place cell representations Spatial learning depends on the rapid formation of hippocampal CA1 place cell representations through behavioral timescale synaptic plasticity (BTSP)[1][1]–[7][2]. BTSP is driven by dendritic Ca²⁺ pla...

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

You can do this right now:

Think of a person who wrote a paper you love, whose work influenced or helped you, or has made your professional life better.

Search up their email address. Shoot them a quick email of thanks. It means so, so much. This is a rough time of year, share some joy.

Cortical glutamatergic and GABAergic inputs support learning-driven hippocampal stability Flexibility and stability of neuronal ensembles are crucial features of brain function. Little is known about how these properties of local circuits are influenced by long-range inputs. We show, in mi...

Cortical glutamatergic and GABAergic inputs support learning-driven hippocampal stability | Science www.science.org/doi/10.1126/...

screenshot of my post

Big new blogpost!

My guide to data visualization, which includes a very long table of contents, tons of charts, and more.

--> Why data visualization matters and how to make charts more effective, clear, transparent, and sometimes, beautiful.
www.scientificdiscovery.dev/p/salonis-gu...

BTSP in visual cortex