@tspisak.bsky.social
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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!
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!
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
🚨 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
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
AbstractBackground. Multivariate predictive models play a crucial role in enhancing our understanding of complex biological systems and in developing innov
doi.org