Soroush Mirjalili
@soroushmirjalili.bsky.social
230 followers 220 following 28 posts
Postdoc in the Kuhl Lab at the University of Oregon, PhD from UT Austin. Episodic Memory | Computational Neuroscience | Cognitive Neuroscience | Machine Learning. 🌿 -> 🐝 -> 🐂 -> 🦆, he/him soroushmirjalili.com
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soroushmirjalili.bsky.social
This work was a true team effort, and I’d like to thank Wanjia Guo, Dominik Grätz, Eric Wang, Dr. Ulrich Mayr, and Dr. Brice Kuhl for their invaluable help throughout the process. I’m especially grateful to Brice for his guidance and support that extended far beyond this paper.
soroushmirjalili.bsky.social
These findings provide insight into how the hippocampus resolves memory interference 'one dimension at a time', demonstrating highly dynamic and adaptive processes that dramatically increase the representational distance between memories that are most at risk for interference.
soroushmirjalili.bsky.social
Finally, dimension-specific input-output functions in CA3/DG strikingly mirrored the sequential pattern observed in behavior: CA3/DG inverted each similarity dimension when it contributed to memory interference but preserved the dimension when it didn't contribute to interference.
soroushmirjalili.bsky.social
Among the 10 dimensions, the first 2 dimensions of similarity strongly predicted memory interference errors. However, their influence on behavior sharply changed with experience. Whereas one dimension drove interference earlier in learning, the other drove interference later.
soroushmirjalili.bsky.social
First, we generated a set of natural scene images from two visual categories and rigorously characterized similarity using a wide array of methods. We then applied PCA to these similarity matrices to identify orthogonal components (dimensions) of similarity across the 10 metrics.
Reposted by Soroush Mirjalili
biorxiv-neursci.bsky.social
Hippocampal transformations occur along dimensions of memory interference https://www.biorxiv.org/content/10.1101/2025.10.13.682242v1
Reposted by Soroush Mirjalili
lexidecker.bsky.social
Excited to share that I'm joining WashU in January as an Assistant Prof in Psych & Brain Sciences! 🧠✨!

I'm also recruiting grad students to start next September - come hang out with us! Details about our lab here: www.deckerlab.com

Reposts are very welcome! 🙌 Please help spread the word!
DeckerLab
www.deckerlab.com
Reposted by Soroush Mirjalili
chrisbaldassano.bsky.social
Years ago my lab tried to brainstorm ways to separately manipulate low-level (texture/pattern) and high-level (scene/object) image properties, for studying visual representations in the brain. Thanks to imaginative work by PhD student Zall Hirschstein, we now have a stimulus set that does just that!
mariamaly.bsky.social
Excited to release the SPOT grid: a new image set that factorially crosses scene-object & texture-pattern pairings.

We hope these stimuli will be useful to researchers aiming to (partially) disentangle the contributions of lower- and higher-level visual features to behavior & brain activity.

1/
8x8 grid depicting the approach to stimulus creation. Feature pairs are on the axes and images are in the cells. The x-axis represents the high-level feature pairs: setting (green) and object (teal). For example, the first column of images all depict “truck” (object) in “field” (setting) rendered in various textures and patterns. The y-axis represents low-level feature pairs: texture (blue) and pattern (purple). For example, the first row of images all depict different objects and settings rendered as if drawn with crayon (texture) and containing large horizontal edges (pattern).
Reposted by Soroush Mirjalili
s-michelmann.bsky.social
🚀Excited to share our project: Canonical Representational Mapping for Cognitive Neuroscience. @schottdorflab.bsky.social and I propose a novel multivariate method to isolate neural representations aligned with specific cognitive hypotheses 🧵https://www.biorxiv.org/content/10.1101/2025.09.01.673485v1
Reposted by Soroush Mirjalili
jrclimer.bsky.social
I’m excited to share my final co-first author paper from my postdoc in Dan Dombeck's lab!

We explored how behavior, senses, and neurons influence how much information in the brain changes over time.

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

#science #research #neuroscience #hippocampus #placecells
Hippocampal representations drift in stable multisensory environments - Nature
Tracking of individual place cells in mouse CA1 shows that representational drift is not influenced by changes in environment or behaviour, and is lower for more excitable place cells.
www.nature.com
Reposted by Soroush Mirjalili
Reposted by Soroush Mirjalili
marcelomattar.bsky.social
Thrilled to see our TinyRNN paper in @nature! We show how tiny RNNs predict choices of individual subjects accurately while staying fully interpretable. This approach can transform how we model cognitive processes in both healthy and disordered decisions. doi.org/10.1038/s415...
Discovering cognitive strategies with tiny recurrent neural networks - Nature
Modelling biological decision-making with tiny recurrent neural networks enables more accurate predictions of animal choices than classical cognitive models and offers insights into the underlying cog...
doi.org
Reposted by Soroush Mirjalili
martamasilva.bsky.social
🧠 Paper out!

We investigated how hippocampal and cortical ripples support memory during movie watching. We found that:

🎬 Hippocampal ripples mark event boundaries
🧩 Cortical ripples predict later recall

Ripples may help transform real-life experiences into lasting memories!

rdcu.be/eui9l
Movie-watching evokes ripple-like activity within events and at event boundaries
Nature Communications - The neural processes involved in memory formation for realistic experiences remain poorly understood. Here, the authors found that ripple-like activity in the human...
rdcu.be
soroushmirjalili.bsky.social
This is really really cool!
soroushmirjalili.bsky.social
Thanks for sharing this, I already linked my submission to this group!
Reposted by Soroush Mirjalili