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Douglas Tomé

@douglasftome.bsky.social

Postdoctoral Fellow at ISTA • Computational Neuroscience • Learning and Memory

Douglas Tomé @douglasftome.bsky.social · 26/09/2026

How do we make sense of distributed, brain-wide neural representations? What do they compute, and how? In our #BernsteinConference workshop, we’ll tackle this through a combination of data-driven modeling and theory-guided experiments. Join the discussion on Monday 28.09 at 2:00 pm in Room 2.104!

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Reposted by Douglas Tomé

Maciej Kania @kaniamaciej.bsky.social · 25/09/2026

Train your model on a task, or fit it to biological data? And once it's optimized, what does it tell us about the brain? 🧠 In our #BernsteinConference satellite workshop, we'll discuss these questions through the lenses of different optimization methods. Check it out in Room 3.104! 🤓😉

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Reposted by Douglas Tomé

Tim Vogels @tpvogels.bsky.social · 22/09/2026

Playing Pong with spikes, dancing rate networks, composite engrams & #ihngrams4engrams. The labs most recent work-in-progress, now on biorxiv.com. Stay tuned for more Pong doi.org/10.64898/202... Dale doi.org/10.64898/202... Composite engrams doi.org/10.64898/202... Ihngrams doi.org/10.64898/202...

Small sub panel of the "Dale" paper's Fig. 1 ,depicting a humanoid dancing with joy. The paper is about how motor cortex can orchestrate a rich and flexible repertoire of network dynamics for rhythmic and goal-directed movements. Computational studies have begun to illuminate the mechanistic origins of this repertoire, but a comprehensive model that can explain the emergence of both transient and self-sustained dynamics is still missing. Condruz et al. show that three simple ingredients: Dalean connectivity, stability, and nonlinear neural responses, suffice to reverse-engineer networks that produce transient, steady-state, and self-sustained periodic activity. A single dynamical principle underlies this repertoire: the interaction of non-normal amplification, inherent to Dalean networks, with neuronal nonlinearity, so to ignite and sustain multi-stable, controllable dynamics. The approach yields entire families of connectivity matrices that require no hand-tuning or learning of weights. Without fitting them to data, these networks reproduce the population-level signatures of motor cortex, implying that the richness of cortical dynamics need not be sculpted by learning, but may emerge from simple biological ingredients. But really, we just feel like dancing with JOY!!

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Reposted by Douglas Tomé

Ann Kennedy @antihebbiann.bsky.social · 22/09/2026

Preprint time! This is a cool one. Complex systems, be they neural nets, interacting genes, or power grids, can produce diverse dynamics-- things like point attractors, limit cycles, and chaos. The dynamic landscape you get depends on how elements interact. But those interactions can also change!

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