Valeria Fascianelli
@valeriafascianelli.bsky.social
📤 163
📥 185
📝 4
Computational neuroscientist @ Center for Theoretical Neuroscience, Columbia University, New York
reposted by
Valeria Fascianelli
Italian Academy, Columbia University
10 days ago
Despite the rain, a full house for
@valeriafascianelli.bsky.social
(Alexander Bodini Fellow in Developmental & Adolescent Psychiatry) &
@stefanofusi.bsky.social
(
@zuckermanbrain.bsky.social
) in our Open Seminars series: "How does the Geometry of Brain Activity Shape Behavior?"
0
4
1
reposted by
Valeria Fascianelli
Italian Academy, Columbia University
11 days ago
Tomorrow! Oct 30, 4:30pm "How does the Geometry of Brain Activity Shape Behavior?" Valeria Fascianelli; moderator Stefano Fusi, Zuckerman Institute, Columbia. Open seminars series; register:
tinyurl.com/379uda2z
@valeriafascianelli.bsky.social
@columbiauniversity.bsky.social
@stefanofusi.bsky.social
0
6
1
Happy to talk about the “Geometry of Emotions” at the Italian Academy on Oct 30th!
add a skeleton here at some point
17 days ago
0
8
0
Honored to be one of the new fellows of the
@italianacademy.bsky.social
in this fall!
add a skeleton here at some point
about 2 months ago
0
4
0
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
6 months ago
0
7
0
reposted by
Valeria Fascianelli
Aldo Battista
8 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...
loading . . .
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
0
34
11
reposted by
Valeria Fascianelli
Camillo Padoa-Schioppa
8 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...
loading . . .
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
0
14
7
reposted by
Valeria Fascianelli
Joao Barbosa
10 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
add a skeleton here at some point
3
103
38
reposted by
Valeria Fascianelli
Carsen Stringer
10 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...
9
303
82
reposted by
Valeria Fascianelli
Mario Dipoppa
11 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
loading . . .
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
1
50
24
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...
add a skeleton here at some point
11 months ago
1
30
12
reposted by
Valeria Fascianelli
David G. Clark
11 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
loading . . .
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
2
55
17
you reached the end!!
feeds!
log in
