Jahan
@jahanesfand.bsky.social
PhD Student @ESI_Frankfurt | Computational Neuroscience
Reposted by Jahan
Firing rates in visual cortex show representational drift, while temporal spike sequences remain stable
www.sciencedirect.com/science/arti...
Great work by Boris Sotomayor and with @battaglialab.bsky.social
www.sciencedirect.com/science/arti...
Great work by Boris Sotomayor and with @battaglialab.bsky.social
Firing rates in visual cortex show representational drift, while temporal spike sequences remain stable
Neural firing-rate responses to sensory stimuli show progressive changes both within and across sessions, raising the question of how the brain mainta…
www.sciencedirect.com
April 10, 2025 at 10:44 AM
Firing rates in visual cortex show representational drift, while temporal spike sequences remain stable
www.sciencedirect.com/science/arti...
Great work by Boris Sotomayor and with @battaglialab.bsky.social
www.sciencedirect.com/science/arti...
Great work by Boris Sotomayor and with @battaglialab.bsky.social
Reposted by Jahan
We show here that neural travelling waves may not indicate the direction of information flow - counter to the common interpretation.
To recover information flow,
the conventional phase-based methods should be supplemented by causal inference techniques like Granger causality
1/2
To recover information flow,
the conventional phase-based methods should be supplemented by causal inference techniques like Granger causality
1/2
www.biorxiv.org/content/10.1...
"What do neural travelling waves tell us about information flow?"
A new preprint is out, with Antoine Grimaldi, Fredo Chavane and @martinavinck.bsky.social
"What do neural travelling waves tell us about information flow?"
A new preprint is out, with Antoine Grimaldi, Fredo Chavane and @martinavinck.bsky.social
What do neural travelling waves tell us about information flow?
In many behavioral conditions, neural activity propagates within and across brain regions as traveling waves, revealing the importance of analyzing spatiotemporal dynamics in electrophysiological data...
www.biorxiv.org
February 27, 2025 at 6:51 AM
We show here that neural travelling waves may not indicate the direction of information flow - counter to the common interpretation.
To recover information flow,
the conventional phase-based methods should be supplemented by causal inference techniques like Granger causality
1/2
To recover information flow,
the conventional phase-based methods should be supplemented by causal inference techniques like Granger causality
1/2
Reposted by Jahan
Happy to see this study led by Irene Onorato finally out - we show distinct phase locking and spike timing of optotagged PV cells and Sst interneuron subtypes during gamma oscillations in mouse visual cortex, suggesting an update to the classic PING model www.sciencedirect.com/science/arti...
Distinct roles of PV and Sst interneurons in visually induced gamma oscillations
Gamma-frequency oscillations are a hallmark of active information processing and are generated by interactions between excitatory and inhibitory neuro…
www.sciencedirect.com
March 6, 2025 at 10:22 PM
Happy to see this study led by Irene Onorato finally out - we show distinct phase locking and spike timing of optotagged PV cells and Sst interneuron subtypes during gamma oscillations in mouse visual cortex, suggesting an update to the classic PING model www.sciencedirect.com/science/arti...
Reposted by Jahan
🚨Preprint Alert
New work with @martinavinck.bsky.social
We elucidate the architectural bias that enables CNNs to predict early visual cortex responses in macaques and humans even without optimization of convolutional kernels.
🧠🤖
🧠📈
Neural responses in early, but not late, visual cortex are well predicted by random-weight CNNs with sufficient model complexity https://www.biorxiv.org/content/10.1101/2025.02.05.636721v1
February 8, 2025 at 3:06 PM
🚨Preprint Alert
New work with @martinavinck.bsky.social
We elucidate the architectural bias that enables CNNs to predict early visual cortex responses in macaques and humans even without optimization of convolutional kernels.
🧠🤖
🧠📈