Cosyne schedule, Saturday, March 14, 1.15pm to 4.15pm, Poster number 3-161, Dynamics-based alignment across sessions reveals latent neural computation

Recurrent circuit with spiking measurements on two days. On the left, a 2D flowfield shows bistable dynamics and sample latent trajectories underlying the population activity.

Want to match neural representations from different days and get more trials for analyses? Interested in multi-scale neural dynamics in decision variability?
Visit our #cosyne2026 poster today afternoon (Sat)!
3-161: Dynamics-based alignment across sessions reveals latent neural computation

6/ What we found in the Cell paper:

Figure 2:

When subjects pick between two options, each associated with reward magnitude and probability, vmPFC embeds these options as “locations” in a 2D choice attribute space and represents the trajectory between these options with a hexagonal periodic code

📍🇵🇹 Excited for #cosyne2026! Come see my poster on Saturday if you want to talk drift.

My colleagues at @sprekeler.bsky.social's lab also have some great posters coming up:
#compneurosky

Deconstruction of a memory engram reveals distinct ensembles recruited at learning - Nature Neuroscience Pouget et al. identified distinct CA1 neuron ensembles active during specific moments of fear learning and uncovered the core engram essential for memory formation.

How does the brain build a memory?
A common assumption is that the neurons activated during an experience collectively form the memory engram.
In our new Nature Neuroscience paper (finally out!), we show that this is not the case.
www.nature.com/articles/s41...

1-031, Poster session 1 (Thu, Mar. 12, 20:30): @maxschwabe.bsky.social (a Cosyne presenter awardee 🎉, a MASTER student) will present how decomposing neural dynamics to intrinsic and input-driven modes can tell us how naturalistic decision computations are realized in the RNNs.

If you are interested in decomposing neural dynamics to understand how RNNs solve naturalistic decision-making tasks, visit 1-031 today at 20:30 and sai hi to @maxschwabe.bsky.social!

will be at #Cosyne2026 presenting this new work on Saturday's posters session starting 1:15p! Come learn about how many seemingly disparate sensorimotor functions in the brain can be explained by a single anatomical motif and computational framework.

Here's my bottom line:

Much of what they are doing is bad.
Much of what they are doing is unpopular.
Pushback is working.
We are winning.
Keep fighting.
Be creative.
Push for what you'd want.
Allow for insiders and outsiders.
Realize that change takes time.
Reduce harm in the meantime.

How does circuit structure shape neural activity? The lab of @epfl-brainmind.bsky.social Prof. Wulfram Gerstner shows that distinct neural circuits can produce similar low-dimensional dynamics, while retaining clear signatures in single neuron and population activity data: doi.org/10.1016/j.ne...

Interested in dopaminergic learning, interaction of appetitive and aversive learning systems, & history-dependent modulation of learning? Come by my #cosyne2026 poster Thursday [1-096]: Reinforcement learning with distributed representations of prediction error and value.
#compneurosky #neuroskyence

Very happy to share our review on Reinforcement Learning vs Statistical Learning, with @ambrafer.bsky.social and @predictivebrain.bsky.social:

www.sciencedirect.com/science/arti...

A nice summary:
www.sainsburywellcome.org/blog/two-eng...

Is spatial navigation innate 🧠? Using #NeuroPixels we show that the #torus 🍩 underlying the #GridCell map exists already on day 10 in rats — before pups open eyes and ears and before they start upright walking. 🧵1:4
👇
www.biorxiv.org/content/10.6...

2/7 Using large-scale #Neuropixels recordings in rat pups (P8-P19), we recorded up to ~1000 simultaneous MEC-PaS units. We found toroidal manifolds in the medial entorhinal cortex (#MEC) as early as postnatal day 10 (P10), preceding eye/ear opening and active exploration🍩

1/7 🧠 My journey into development begins with this work and question: how does the brain's spatial navigation system develop? We found that the neural networks for spatial navigation (tori and rings) are preconfigured and only later anchor gradually to the world with experience! 🧵

Come check out our poster at Cosyne 2026, on Thursday! Plasticity vs dynamics, RNN with online learning rule, inference based on structure and many other ideas!

BioRiv paper:
www.biorxiv.org/content/10.6...

Hippocampus a ‘general-purpose statistical learning machine’ New cross-species findings may help settle a long-standing debate about whether the hippocampus is required for passively learning information.

Whether the hippocampus is involved in unrewarded learning has been a controversial question. A new preprint finds that it may be critical for passively learning information.

By @natmesanash.bsky.social

#neuroskyence

www.thetransmitter.org/memory/hippo...

Feedback control of random networks as a model of flexible motor cortical dynamics across tasks Kalidindi and Crevecoeur develop a computational framework linking feedback-controlled networks to limb dynamics. They demonstrate that optimal control of fixed network reproduces key motor cortical d...

Nice work, congratulations! In a recent model we found that similar neural dynamics may be due to similar task structure independent of network connectivity (supp. Figure). Could a simpler explanation be that all animals experienced essentially the same task structure?
www.cell.com/cell-reports...

To assess conserved neural computations, we tested the similarity of neural dynamics with Dynamical Similarity Analysis, an awesome technique for comparing the “rules” of two dynamical systems (arxiv.org/abs/2306.10168). Low dynamical distance, here, implies similar computations. 6/18

Characteristics and dynamical signatures of recurrent cortical circuits during context-dependent processing Context profoundly shapes neural responses and behavior. During context-dependent sensory processing, recurrent connections shape the integration of feedforward sensory input and feed-back input from downstream brain regions. How do different cell types, interacting through spatially structured recurrent lateral connections, give rise to context-dependent processing and circuit stability, and what dynamical signatures reveal their individual roles? To answer these questions, we employ data-driven approaches to construct spatially extended stabilized supralinear network models that capture the responses of diverse cell types in the mouse primary visual cortex during context-dependent processing. Analysis of well-fitting models reveals that the dominant inhibitory cell type affecting excitatory neurons is not fixed but dynamically varies with stimulus and space. While PV-mediated stabilization is indispensable across all models and stimulus conditions, SST-mediated stabilization is also required, and likely in a stimulus-dependent manner. Interestingly, even when a specific inhibitory cell type is required for circuit stabilization, a uniform perturbation of it does not necessarily produce a paradoxical change in its mean activity. Instead, assessing cell-type-specific circuit stabilization requires patterned perturbations, where paradoxical effects manifest along specific activity modes. Finally, we show that recurrent connections and input-output nonlinearities are essential for integrating feedforward and feedback inputs to reproduce the observed spatial response profiles. Recurrent excitatory connections, in particular, are required to account for responses to small stimuli, where external inputs are relatively weak. Taken together, our work reveals the crucial role of ubiquitous biological components in context-dependent processing and delineates the characteristics and dynamical signatures of these circuits. ### Competing Interest Statement The authors have declared no competing interest. Gatsby Charitable Foundation, GAT3708 National Institutes of Health, https://ror.org/01cwqze88, 1RF1DA056397, U19NS107613, T32 EY013933 U.S. National Science Foundation, DGE-2036197 Simons Foundation, SCGB 543017 Agencia Estatal de Investigación, PID2023-149174NB-I00

Interested in cell type diversity, the cortical operating regime, and how recurrent connections shape the integration of feedforward and feedback inputs during context-dependent processing? Come check out my poster [1-038] at #Cosyne2026.

Link: doi.org/10.64898/2026.02.06.704473

Check out our latest research drop! We show BLA dopamine signaling encodes the emotional weight of sensory transitions, but not the associative strength or value of stimuli. These signals dynamically rescale when the learning context changes: "this matters most!" www.biorxiv.org/content/10.1...

InputDSA: Demixing then comparing recurrent and externally driven dynamics in complex systems - Kempner Institute We explored how to measure the similarity between two complex systems when they are driven by external inputs, like biological neural circuits or reinforcement learning agents. Our novel method, calle...

NEW from the #KempnerInstitute: InputDSA, a tool to separate intrinsic dynamics from input-driven effects—enabling accurate, efficient comparisons of complex systems with external inputs.

Read the #DeeperLearning blog post by @annhuang42.bsky.social & @kanakarajanphd.bsky.social: bit.ly/4bkrvy9

Cerebellar climbing fibers impact experience-dependent plasticity in the mouse primary somatosensory cortex Optogenetic climbing fiber activation regulates experience-dependent plasticity in the primary somatosensory cortex of mice, suggesting a role of the olivo-cerebellum in instructive signaling across b...

Quite impressed with the results reported here, a cerebellar learning signal affects neocortex plasticity. In the context of credit assignment, this suggest a cross-system coordination of plasticity: elifesciences.org/articles/109...

Conflicting adaptations in an inhibitory feedback circuit Abstract figure legend We studied activity-dependent adaptation in the fruit fly Drosophila’s memory centre, the mushroom body. Here, excitatory Kenyon cells (KCs) receive feedback inhibition from th...

Check out our latest paper - In neural networks with inhibitory feedback, local homeostatic adaptation can conflict with adaptation at the network level doi.org/10.1113/JP29...

Nepotism has made Iran weak.

by Jared Kushner and RFK Jr.

Science thrives when diverse voices are heard.
This #InternationalWomensDay, we celebrate women everywhere - including women #neuroscientists who mentor, inspire & push the boundaries of discovery 🌟

Happy International Women’s Day from IBRO!

I’ll be presenting my work at #COSYNE2026. Stop by our poster to come learn about how the geometry of neural population activity shapes communication between brain areas during learned behavior 🐦‍⬛🧠🎶

Excited to give a talk at #Cosyne2026 about my PhD work!

We show that RNNs trained on visual search converge on brain-like solutions, producing primate-like behavior and neural representations. Happy to chat if you're at Cosyne!

📅 March 15, 2026
📍 Lisbon, Portugal
www.biorxiv.org/content/10.1...

Publisher demands $500 from impersonated author to retract paper Last year, we wrote about a Walsh Medical Media journal that refused to withdraw an author’s paper unless he paid a fee — even though he didn’t write or submit the article. For one reader, some details of that story were familiar. Laertis Ikonomou, an associate professor at the University of Buffalo in New York, … Continue reading Publisher demands $500 from impersonated author to retract paper

Lol. This is academic publishing taken to its logical end.

retractionwatch.com/2026/03/05/publisher-dem...

Task learning increases information redundancy of neural responses in macaque visual cortex How does the brain optimize sensory information for decision-making in new tasks? One hypothesis suggests that learning reduces redundancy in neural representations to improve efficiency, whereas anot...

RIP redundancy reduction?

Beautiful work by Liu & colleagues showing that neural redundancy increases with learning, as predicted by a Bayesian model:
www.science.org/doi/10.1126/...

The most sensitive inputs to cutaneous representing regions of primary somatosensory cortex do not change with behavioral training - PubMed Learning a sensory detection task leads to an increased primary sensory cortex response to the detected stimulus, while learning a sensory discrimination task additionally leads to a decreased sensory cortex response to the distractor stimulus. Neural responses are scaled up, and down, in strength, …

Some people have written about how these two ideas may not be mutually exclusive.

pubmed.ncbi.nlm.nih.gov/26634900/