@tspisak.bsky.social
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tspisak.bsky.social
@tspisak.bsky.social
· May 30
Self-orthogonalizing attractor neural networks emerging from the free energy principle
Attractor dynamics are a hallmark of many complex systems, including the brain. Understanding how such self-organizing dynamics emerge from first principles is crucial for advancing our understanding ...
arxiv.org
As many of you know, I’ve been fascinated by brain attractor dynamics lately.
Thrilled to share a new preprint on their link to orthogonal neural representations, co-authored with Karl Friston:
arxiv.org/abs/2505.22749
- with implications for both neuroscience & AI!
First in a series - stay tuned!
Thrilled to share a new preprint on their link to orthogonal neural representations, co-authored with Karl Friston:
arxiv.org/abs/2505.22749
- with implications for both neuroscience & AI!
First in a series - stay tuned!
Replicability of BWAS with functional and structural MRI has been hotly debated.
But what about DWI?
Our new paper - first authored by @rkotikalapudi.bsky.social - shows that multivariate DWI models of trait-like phenotypes can be replicable, even with moderate sample sizes.
🔗 Link in comment!
But what about DWI?
Our new paper - first authored by @rkotikalapudi.bsky.social - shows that multivariate DWI models of trait-like phenotypes can be replicable, even with moderate sample sizes.
🔗 Link in comment!
November 7, 2025 at 9:00 AM
Replicability of BWAS with functional and structural MRI has been hotly debated.
But what about DWI?
Our new paper - first authored by @rkotikalapudi.bsky.social - shows that multivariate DWI models of trait-like phenotypes can be replicable, even with moderate sample sizes.
🔗 Link in comment!
But what about DWI?
Our new paper - first authored by @rkotikalapudi.bsky.social - shows that multivariate DWI models of trait-like phenotypes can be replicable, even with moderate sample sizes.
🔗 Link in comment!
Reposted
Serious concerns about a new cortical biomarker for pain sensitivity
jamanetwork.com/journals/jam...
We (with @tspisak.bsky.social, @christianbuchel.bsky.social) published a commentary on Chowdhury, Bi et al. (2025, JAMA Neurology) raising serious concerns about their reported results.
👇 1/13
jamanetwork.com/journals/jam...
We (with @tspisak.bsky.social, @christianbuchel.bsky.social) published a commentary on Chowdhury, Bi et al. (2025, JAMA Neurology) raising serious concerns about their reported results.
👇 1/13
Concern About Predictive Performance of a Pain Sensitivity Biomarker
To the Editor Chowdhury et al1 evaluated a biomarker for pain sensitivity, combining peak alpha frequency and corticomotor excitability. The authors report outstanding performance (validation set area...
jamanetwork.com
July 22, 2025 at 3:24 PM
Serious concerns about a new cortical biomarker for pain sensitivity
jamanetwork.com/journals/jam...
We (with @tspisak.bsky.social, @christianbuchel.bsky.social) published a commentary on Chowdhury, Bi et al. (2025, JAMA Neurology) raising serious concerns about their reported results.
👇 1/13
jamanetwork.com/journals/jam...
We (with @tspisak.bsky.social, @christianbuchel.bsky.social) published a commentary on Chowdhury, Bi et al. (2025, JAMA Neurology) raising serious concerns about their reported results.
👇 1/13
As many of you know, I’ve been fascinated by brain attractor dynamics lately.
Thrilled to share a new preprint on their link to orthogonal neural representations, co-authored with Karl Friston:
arxiv.org/abs/2505.22749
- with implications for both neuroscience & AI!
First in a series - stay tuned!
Thrilled to share a new preprint on their link to orthogonal neural representations, co-authored with Karl Friston:
arxiv.org/abs/2505.22749
- with implications for both neuroscience & AI!
First in a series - stay tuned!
Self-orthogonalizing attractor neural networks emerging from the free energy principle
Attractor dynamics are a hallmark of many complex systems, including the brain. Understanding how such self-organizing dynamics emerge from first principles is crucial for advancing our understanding ...
arxiv.org
May 30, 2025 at 7:14 AM
As many of you know, I’ve been fascinated by brain attractor dynamics lately.
Thrilled to share a new preprint on their link to orthogonal neural representations, co-authored with Karl Friston:
arxiv.org/abs/2505.22749
- with implications for both neuroscience & AI!
First in a series - stay tuned!
Thrilled to share a new preprint on their link to orthogonal neural representations, co-authored with Karl Friston:
arxiv.org/abs/2505.22749
- with implications for both neuroscience & AI!
First in a series - stay tuned!
🚨 New paper out in GigaScience!
To avoid common pitfalls in multivariate modeling: combine external validation with pre-registration — freeze your model before testing.
For the pros: decide on the fly when to stop training!
First-authored by the brilliant @ggallitto.bsky.social
To avoid common pitfalls in multivariate modeling: combine external validation with pre-registration — freeze your model before testing.
For the pros: decide on the fly when to stop training!
First-authored by the brilliant @ggallitto.bsky.social
A new approach for transparent reporting of prospective predictive modeling studies involving preregistration of machine learning models.
External validation of machine learning models—registered models and adaptive sample splitting doi.org/10.1093/giga...
External validation of machine learning models—registered models and adaptive sample splitting doi.org/10.1093/giga...
External validation of machine learning models—registered models and adaptive sample splitting
AbstractBackground. Multivariate predictive models play a crucial role in enhancing our understanding of complex biological systems and in developing innov
doi.org
May 28, 2025 at 8:44 PM
🚨 New paper out in GigaScience!
To avoid common pitfalls in multivariate modeling: combine external validation with pre-registration — freeze your model before testing.
For the pros: decide on the fly when to stop training!
First-authored by the brilliant @ggallitto.bsky.social
To avoid common pitfalls in multivariate modeling: combine external validation with pre-registration — freeze your model before testing.
For the pros: decide on the fly when to stop training!
First-authored by the brilliant @ggallitto.bsky.social