@tspisak.bsky.social
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tspisak.bsky.social
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
tspisak.bsky.social
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
tspisak.bsky.social
🚨 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
gigascience.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