@shuqiw.bsky.social
Reposted
Beautiful and clear results showing that temporal difference error calculation is hardwired in the dopamine/striatum mircocircuits: www.biorxiv.org/content/10.1...
from @malcolmgcampbell.bsky.social and @naoshigeuchida.bsky.social
from @malcolmgcampbell.bsky.social and @naoshigeuchida.bsky.social
A hardwired neural circuit for temporal difference learning
The neurotransmitter dopamine plays a major role in learning by acting as a teaching signal to update the brain's predictions about rewards. A leading theory proposes that this process is analogous to...
www.biorxiv.org
September 20, 2025 at 11:36 AM
Beautiful and clear results showing that temporal difference error calculation is hardwired in the dopamine/striatum mircocircuits: www.biorxiv.org/content/10.1...
from @malcolmgcampbell.bsky.social and @naoshigeuchida.bsky.social
from @malcolmgcampbell.bsky.social and @naoshigeuchida.bsky.social
Reposted
Excited to share new computational work, led by @jzv.bsky.social, driven by Juan Carlos Fernandez del Castillo + contribution from Farhad Pashakanloo. We recover 3 core motifs in the olfactory system of evolutionarily distant animals using a biophysically-grounded model + efficient coding ideas!
Convergent motifs of early olfactory processing are recapitulated by layer-wise efficient coding
The architecture of early olfactory processing is a striking example of convergent evolution. Typically, a panel of broadly tuned receptors is selectively expressed in sensory neurons (each neuron exp...
www.biorxiv.org
September 4, 2025 at 4:51 PM
Excited to share new computational work, led by @jzv.bsky.social, driven by Juan Carlos Fernandez del Castillo + contribution from Farhad Pashakanloo. We recover 3 core motifs in the olfactory system of evolutionarily distant animals using a biophysically-grounded model + efficient coding ideas!
Reposted
Since I'm back on BlueSky - with @frostedblakess.bsky.social and @cpehlevan.bsky.social we wrote a brief perspective on how ideas about summary statistics from the statistical physics of learning could potentially help inform neural data analysis... (1/2)
Frontiers | Summary statistics of learning link changing neural representations to behavior
How can we make sense of large-scale recordings of neural activity across learning? Theories of neural network learning with their origins in statistical phy...
www.frontiersin.org
September 4, 2025 at 6:30 PM
Since I'm back on BlueSky - with @frostedblakess.bsky.social and @cpehlevan.bsky.social we wrote a brief perspective on how ideas about summary statistics from the statistical physics of learning could potentially help inform neural data analysis... (1/2)
Reposted
Just got back from a great summer school at Sapienza University sites.google.com/view/math-hi... where I gave a short course on Dynamics and Learning in RNNs. I compiled a (very biased) list of recommended readings on the subject, for anyone interested: aleingrosso.github.io/_pages/2025_...
Home
The school will open the thematic period on Data Science and will be dedicated to the mathematical foundations and methods for high-dimensional data analysis. It will provide an in-depth introduction ...
sites.google.com
September 15, 2025 at 11:57 AM
Just got back from a great summer school at Sapienza University sites.google.com/view/math-hi... where I gave a short course on Dynamics and Learning in RNNs. I compiled a (very biased) list of recommended readings on the subject, for anyone interested: aleingrosso.github.io/_pages/2025_...
Reposted
Is it possible to go from spikes to rates without averaging?
We show how to exactly map recurrent spiking networks into recurrent rate networks, with the same number of neurons. No temporal or spatial averaging needed!
Presented at Gatsby Neural Dynamics Workshop, London.
We show how to exactly map recurrent spiking networks into recurrent rate networks, with the same number of neurons. No temporal or spatial averaging needed!
Presented at Gatsby Neural Dynamics Workshop, London.
From Spikes To Rates
YouTube video by Gerstner Lab
youtu.be
August 8, 2025 at 3:25 PM
Is it possible to go from spikes to rates without averaging?
We show how to exactly map recurrent spiking networks into recurrent rate networks, with the same number of neurons. No temporal or spatial averaging needed!
Presented at Gatsby Neural Dynamics Workshop, London.
We show how to exactly map recurrent spiking networks into recurrent rate networks, with the same number of neurons. No temporal or spatial averaging needed!
Presented at Gatsby Neural Dynamics Workshop, London.
Reposted
Out today in @nature.com: we show that individual neurons have diverse tuning to a decision variable computed by the entire population, revealing a unifying geometric principle for the encoding of sensory and dynamic cognitive variables.
www.nature.com/articles/s41...
www.nature.com/articles/s41...
June 25, 2025 at 10:38 PM
Out today in @nature.com: we show that individual neurons have diverse tuning to a decision variable computed by the entire population, revealing a unifying geometric principle for the encoding of sensory and dynamic cognitive variables.
www.nature.com/articles/s41...
www.nature.com/articles/s41...
Reposted
Our new preprint 👀
The firing of neural populations is high-dim even if their subthreshold activity is low-dim! This work by @bio-emergent.bsky.social and @haydari.bsky.social shows how, with a solvable model, a data analysis technique, and data from mouse visual cortex: www.biorxiv.org/content/10.1...
High-dimensional neuronal activity from low-dimensional latent dynamics: a solvable model
Computation in recurrent networks of neurons has been hypothesized to occur at the level of low-dimensional latent dynamics, both in artificial systems and in the brain. This hypothesis seems at odds ...
www.biorxiv.org
June 9, 2025 at 7:32 PM
Our new preprint 👀