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valeriafascianelli.bsky.social
Valeria Fascianelli
@valeriafascianelli.bsky.social
160 followers 180 following 3 posts
Computational neuroscientist @ Center for Theoretical Neuroscience, Columbia University, New York
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valeriafascianelli.bsky.social
Valeria Fascianelli @valeriafascianelli.bsky.social · 28d
Honored to be one of the new fellows of the @italianacademy.bsky.social in this fall!
italianacademy.bsky.social
Italian Academy, Columbia University @italianacademy.bsky.social · 28d
New Fellow joining the Academy:
Valeria Fascianelli; Project: “The neural geometry of emotions: cognitive implications and individual variability”
tinyurl.com/2y5mzaw9
See all Fellows: tinyurl.com/5afsx4kv
@valeriafascianelli.bsky.social
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valeriafascianelli.bsky.social
Valeria Fascianelli @valeriafascianelli.bsky.social · Apr 29
Excited to speak at the Davide Giri Talks at the Consulate General of Italy in New York!
We’ll be discussing complex systems: from atoms, to people, to machines. @sueyeonchung.bsky.social
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Reposted by Valeria Fascianelli
aldobattista.bsky.social
Aldo Battista @aldobattista.bsky.social · Mar 15
Excited to share our latest preprint with @camillopadoasch.bsky.social and Xiao-Jing Wang! We present a biologically plausible framework showing how neural circuits compute & compare value to drive flexible economic decision making.

www.biorxiv.org/content/10.1...
A Neural Circuit Framework for Economic Choice: From Building Blocks of Valuation to Compositionality in Multitasking
Value-guided decisions are at the core of reinforcement learning and neuroeconomics, yet the basic computations they require remain poorly understood at the mechanistic level. For instance, how does t...
www.biorxiv.org
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Reposted by Valeria Fascianelli
camillopadoasch.bsky.social
Camillo Padoa-Schioppa @camillopadoasch.bsky.social · Mar 14
New collaborative ms! We built & trained a neural network that is biophysically realistic, performs multiple economic choice tasks, and provides insights into orbitofrontal cortex.
(We = Aldo Battista 😉)
www.biorxiv.org/content/10.1...
A Neural Circuit Framework for Economic Choice: From Building Blocks of Valuation to Compositionality in Multitasking
Value-guided decisions are at the core of reinforcement learning and neuroeconomics, yet the basic computations they require remain poorly understood at the mechanistic level. For instance, how does t...
www.biorxiv.org
7 14
Reposted by Valeria Fascianelli
jbarbosa.org
Joao Barbosa @jbarbosa.org · Jan 12
Check our latest in which we leverage shape metrics to compare neural geometry across regions, sessions or subjects and how their differences predict behavior.

w/ Nejatbakhsh, Duong, @sarah-harvey.bsky.social, Brincat, @siegellab.bsky.social, @earlkmiller.bsky.social & @itsneuronal.bsky.social
biorxiv-neursci.bsky.social
bioRxiv Neuroscience @biorxiv-neursci.bsky.social · Jan 11
Quantifying Differences in Neural Population Activity With Shape Metrics https://www.biorxiv.org/content/10.1101/2025.01.10.632411v1
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Reposted by Valeria Fascianelli
computingnature.bsky.social
Carsen Stringer @computingnature.bsky.social · Jan 12
What if… spontaneous neural activity 🧠 reflects the baseline rumblings of a brainwide dynamical system initialized for learning? We find that the rumblings have macroscopic properties like those emerging from linear symmetric, critical systems 🧵 #neuroscience #neuroAI www.biorxiv.org/content/10.1...
schematic of neural recordings from mouse V1, whole-brain, and hippocampus; neural activity traces from the population, showing more correlated activity in V1 and whole-brain recordings versus more decorrelated activity in hippocampus
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Reposted by Valeria Fascianelli
mariodipoppa.bsky.social
Mario Dipoppa @mariodipoppa.bsky.social · Dec 16
New results! Visual adaptation changes the geometry of V1 population activity: frequent stimuli elicit smaller responses but become more discriminable. Similar results are seen in ANNs trained with metabolic constraints, suggesting these changes emerge from efficient coding. bit.ly/3VJHXRn
Adaptation shapes the representational geometry in mouse V1 to efficiently encode the environment
Sensory adaptation dynamically changes neural responses as a function of previous stimuli, profoundly impacting perception. The response changes induced by adaptation have been characterized in detail...
bit.ly
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valeriafascianelli.bsky.social
Valeria Fascianelli @valeriafascianelli.bsky.social · Dec 4
What is the neural code and statistical structure of neural states characterizing stress?
Our new work in Nature answers these questions and more. Thanks to my amazing co-first @fxia.bsky.social @stefanofusi.bsky.social @mazenkheirbek.bsky.social for precious guidance
www.nature.com/articles/s41...
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Reposted by Valeria Fascianelli
david-g-clark.bsky.social
David G. Clark @david-g-clark.bsky.social · Dec 3
(1/5) Fun fact: Several classic results in the stat. mech. of learning can be derived in a couple lines of simple algebra!

In this paper with Haim Sompolinsky, we simplify and unify derivations for high-dimensional convex learning problems using a bipartite cavity method.
arxiv.org/abs/2412.01110
Simplified derivations for high-dimensional convex learning problems
Statistical physics provides tools for analyzing high-dimensional problems in machine learning and theoretical neuroscience. These calculations, particularly those using the replica method, often invo...
arxiv.org
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