Sepehr Sima

PhD student in Neurobiology and Behavior | Columbia University; Psychology | University of Tehran; Lab Technician | (IPM)

[Ariel Zylberberg @azylb]

New preprint alert! Do words or numbers better capture our confidence?
We found that words (eg "very likely") predict choice accuracy more reliably than numbers.
I’m highly confident you’ll like it. Or is it 85% confident? :-) https://t.co/aGkPES5O7b

[Erfan Zabeh @ZabehErfan]

Finally a causal formulation of spike-wave duality.

Over the past couple of years I have been constantly asked about causality of brain waves.
After two years of spontaneously thinking about this, I dared to put this "baby" out.
Shout out to my talented collaborator @causalkasra whom perfected the causal machinery behind this work.

If you are at @NeurIPSConf come to our poster and let’s chat. https://t.co/T7akP33kWK

[Jorge Bravo Abad @bravo_abad]

Building compositional tasks with shared neural subspaces

One of the big open questions in cognitive neuroscience is how the brain pulls off the kind of flexible, compositional behaviour that modern AI systems are still struggling to match. We know animals can recombine simple skills—“categorize this,” “move eyes there”—to solve new tasks, and that artificial networks trained on many tasks tend to reuse internal components. But what does that reuse look like in real neural populations?

Sina Tafazoli and coauthors tackle this by training monkeys on three cleverly related tasks that recombine the same subtasks: categorizing by colour or shape, and responding along one of two saccade axes. While the animals switched between tasks without explicit cues, the authors recorded from prefrontal, parietal, temporal cortex and striatum. Using population decoding, they show that task-relevant information—colour category, shape category, motor response—lives in low-dimensional subspaces of neural activity. Crucially, these subspaces are shared across tasks: the same colour subspace is reused whenever colour matters, and the same motor subspace is reused whenever a given response axis is required.

The really interesting part is how these shared subspaces are engaged. The authors decode an internal “task belief” signal from prefrontal activity during fixation, and show that as the monkeys infer which task is currently active, the brain selectively scales the relevant subspaces (amplifying useful colour or shape information, suppressing irrelevant features) and funnels activity from the appropriate sensory subspace into the appropriate motor subspace. Motor axes are updated quickly; sensory representations adjust more slowly, matching behaviour. The picture that emerges is strikingly aligned with ideas in multitask and continual learning in AI: flexible behaviour arises not from isolated, task-specific circuits, but from a set of shared neural primitives that can be recombined and gain-modulated on the fly.

Paper: https://t.co/oXjrDC6jNm

[Manuel Beiran @mBeiran]

So happy to see this work out!

We studied when brain connectomes can be used to accurately predict neural activity, and when they can't.

Happy to chat more about it, please do reach out if interested.

https://t.co/ZRTzBRpUV7

[Tahereh Toosi @taherehtoosi]

How does our brain excel at complex object recognition, yet get fooled by simple illusory contours? What unifying principle governs all Gestalt laws of perceptual organization?

We may have an answer: integration of learned priors through feedback. New paper with Ken Miller! 🧵

[Amirhossein Farzmahdi @farzmahdi]

Excited that our work is out together with the amazing @CoenCagli_Lab and Adam Kohn! We demonstrate how neural co-variability in the visual cortex encodes uncertainty about natural scenes and is adaptively modulated by spatial context! (1/7)

Paper link: https://t.co/f9JhaULAa4

[Mohammad Atari @MohammadAtari90]

New paper: The chronospatial revolution in psychology.

With @JF_Schulz @JoHenrich

[Friedemann Zenke @hisspikeness]

1/6 Why does the brain maintain such precise excitatory-inhibitory balance?
Our new preprint explores a provocative idea: Small, targeted deviations from this balance may serve a purpose: to encode local error signals for learning.
https://t.co/q7aXO03tdb
led by @j_rossbroich

[Erfan Zabeh @ZabehErfan]

Thrilled to share this collaboration with lead author @Gedankient at @neurojosh and @bradlega labs. Check it out the preprint:

[Sepehr Sima @sepehr_sssa]

@ma_fakharian @ScienceMagazine @reziliusReza @just_alden @pauliehage @HElseweifi Well-deserved. Congrats Amin!

[Mohammad Amin Fakharian @ma_fakharian]

First first-author paper now in @ScienceMagazine! Great thanks to incredible mentor @reziliusReza and coauthors @just_alden @pauliehage @HElseweifi.

We asked how neurons in the cerebellar cortex control rapid eye movements (saccades). 1/3

https://t.co/5Cf5cGHlGa

[Assaf Ramot @AssafRamot]

Thrilled to share my postdoc work from @takaki_komiyama lab, showing how motor learning reshapes thalamic influence on motor cortex to enable learned movements. Hats off to our collaborators and co-authors for their incredible contributions!
https://t.co/CvU6DkPtz7

[Alireza Valizadeh @valizadeh7]

The brain is modular—but what if inhibition shapes that modularity? We show that wide-range inhibitory connections boost info processing by letting one module "speak" while others stay silent.
#neuroscience #Brain #hopfield #AI #neuron
#synapse #memory
https://t.co/8dUfxGkgKT

[Jérôme Lecoq @LecoqJerome]

How does our brain predict the future? Our review of predictive processing + research program is now on arXiv https://t.co/4GEXauuCeK
50+ neuroscientists distributed across the world worked together to create this unique community project.

[Decision Neuroscience and Neuroeconomics Papers @decisionneurop]

A neural geometry approach comprehensively explains apparently conflicting models of visual perceptual learning https://t.co/5ETX5reVwv

[Earl K. Miller @MillerLabMIT]

Beyond Mechanism—Extending Our Concepts of Causation in Neuroscience
https://t.co/NCRSZVguqX
#neuroscience

[Vikram Gadagkar @VikramGadagkar]

Excited to share our latest work on songbirds showing dopamine guides learning of natural behavior https://t.co/ufuX4F2rRV Congrats to @JonathanKasdin Alison Duffy Nathan Nadler @pantamallion @alfairhall @neuro_kim ! @ZuckermanBrain @Columbia @GoogleDeepMind @UW

[Weizhe Hong @TheHongLab]

We are excited to share our latest work showing that mice display rescue-like prosocial behavior toward unresponsive partners. @ScienceMagazine
https://t.co/Jzwna46rFy

[Chuyi Su, PhD @ChuyiSu]

Happy to share that my work on Fast-Spike Interneurons in Visual Cortical Layer 5: Heterogeneous Response Properties Are Related to Thalamocortical Connectivity is now published in @SfNJournals https://t.co/1CFJg9WFJX

[CLaE @leafs_s]

Nature Reviews Neuroscience

The curious case of dopaminergic prediction errors and learning associative information beyond value

https://t.co/91IsidcVEA