Valeria Fascianelli
@valeriafascianelli.bsky.social
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📥 183
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Computational neuroscientist @ Center for Theoretical Neuroscience, Columbia University, New York
Honored to be one of the new fellows of the
@italianacademy.bsky.social
in this fall!
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12 days ago
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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
5 months ago
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Aldo Battista
6 months ago
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...
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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...
https://www.biorxiv.org/content/10.1101/2025.03.13.643098v1
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Camillo Padoa-Schioppa
6 months ago
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...
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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...
https://www.biorxiv.org/content/10.1101/2025.03.13.643098v1
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Joao Barbosa
8 months ago
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
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Carsen Stringer
8 months ago
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...
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Mario Dipoppa
9 months ago
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
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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...
https://bit.ly/3VJHXRn
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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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10 months ago
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David G. Clark
10 months ago
(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
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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...
https://arxiv.org/abs/2412.01110
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