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Mor Nitzan

@mornitzan.bsky.social
241 followers 137 following 4 posts

Faculty member at the Hebrew University, studying inference of spatial organization and dynamics from single-cell data, information processing, computation & self-organization in biology. www.nitzanlab.com

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Reposted by Mor Nitzan
APS journals @apsjournals.aps.org · 17/06/2026
By introducing concepts analogous to overfitting and underfitting, this study in PRX Life shows how overfitness and underfitness impact selected population complexity — offering an explanation for how organism complexity evolves to optimally match their environment: go.aps.org/44ekCel
On the left is a Muller plot of class frequencies with the environment, the ground truth function ϕ∗, changed to a different randomly sampled one at t = 50. Generations are on the x axis and frequency on the y axis. A legend on the right shows q = 1 as dark purple, q = 2 as violet, q = 3 as dark blue, q = 4 as blue, q = 5 as aquamarine, q = 6 as dark green, q = 7 as green, q = 8 as lime green, and q = 9 as yellow. When the ground truth function changes, shown as a vertical dashed line, the transient success of simple classes repeats for a second time before subsiding. On the right is another Muller plot with the same x and y axis. This plot depicts how, when the environment changes at the same timescale as that of the transient, the transient becomes fixed and simple classes come to dominate selection.
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Mor Nitzan @mornitzan.bsky.social · 19/06/2026
Fitness, overfitness and optimal complexity in changing environments, through the lens of implicit regularization in evolutionary dynamics. Great work by Hagai Rappeport journals.aps.org/prxlife/abst...
journals.aps.org
Fitness and Overfitness: Implicit Regularization in Evolutionary Dynamics
Mapping evolutionary dynamics to Bayesian learning shows that organismal complexity tends to match environmental complexity, but rapid environmental changes may favor simpler forms.
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Mor Nitzan @mornitzan.bsky.social · 08/12/2025
Great work by @jonathankarin.bsky.social and @zoepiran on modeling and generative design of swarm dynamics journals.aps.org/pre/abstract...
journals.aps.org
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Reposted by Mor Nitzan
Shane Liddelow @liddelowsa.bsky.social · 11/12/2024
3 reviews on planning, execution, & validation of transcriptomic experiments in neuroscience published in @natureportfolio.bsky.social #NatureNeuroscience. A MUST READ if you're doing transcriptomics. 3 reviews, an amazing editorial, a beautiful cover – this is the issue of the year! (mini thread)
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Mor Nitzan @mornitzan.bsky.social · 08/12/2024
Characterizing single-cell and spatial data structure and identifying gaps in our knowledge of their annotations by analyzing deep learning training dynamics! Led by @jonathankarin.bsky.social and Reshef Mintz, with @ravehlab.bsky.social
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Reposted by Mor Nitzan
Jonathan Karin @jonathankarin.bsky.social · 05/12/2024
1/3 Annotatability is out in Nature Computational Science! With Reshef Mintz, @ravehlab.bsky.social and @mor_nitzan www.nature.com/articles/s43... .
nature.com
Interpreting single-cell and spatial omics data using deep neural network training dynamics - Nature Computational Science
The Annotatability framework analyzes neural network training dynamics to interpret single-cell and spatial omics data. It identifies erroneous annotations and ambiguous cell states, infers trajectori...
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