Tatiana Engel

Thank you for having me on BrainInspired, Paul @braininspired.bsky.social! It was such an honor to be on my favorite show—a rare place where we can leisurely talk about manifolds, latent circuits, power laws, and other esoteric ideas, and still be taken seriously in knowing they are all real.

Our work with @pawa-pawa.bsky.social is out in Nature Machine Intelligence! The choice of activation function affects the representations, dynamics, and circuit solutions that emerge in RNNs trained on cognitive tasks. Activation matters!
www.nature.com/articles/s42...

We apply our model to survey the spiking irregularity across cortical areas and find that Poisson irregularity is a rare exception, not a rule. Our results show the need to include non-Poisson spiking in inferring neural dynamics from single trials.

In our model, a single parameter phi controls spiking irregularity, and the inhomogeneous Poisson process is just a special case of phi=1. Using intracellular voltage recordings and a spiking network model, we link this abstract statistical model to underlying biophysical processes.

We introduce a doubly stochastic renewal model that accounts for variable firing rates on single trials and can generate any spiking irregularity from periodic to super-Poisson.

However, neuronal spiking in various brain regions often diverges from Poisson irregularity, posing challenges for accurate identification of single-trial firing rate dynamics. Yet, robust methods to partition the firing rate and spiking irregularity are missing.

The firing rate is a prevalent concept used to describe neural computations, but estimating dynamically changing firing rates from irregular spikes is challenging. Most methods rely on an inhomogeneous Poisson process as the standard model for partitioning firing rate and spiking irregularity.

A study led by Cina Aghamohammadi is now out in ‪@natcomms.nature.com‬! We developed a mathematical framework for partitioning spiking variability, which revealed that spiking irregularity is nearly invariant for each neuron and decreases along the cortical hierarchy.
www.nature.com/articles/s41...

Two flagship papers from the International Brain Laboratory, now out in ‪@Nature.com‬:
🧠 Brain-wide map of neural activity during complex behaviour: doi.org/10.1038/s41586-025-09235-0
🧠 Brain-wide representations of prior information in mouse decision-making: doi.org/10.1038/s41586-025-09226-1 +

Princeton’s @ilanawitten.bsky.social, @engeltatiana.bsky.social, and @jpillowtime.bsky.social have created the first-ever brain-wide activity map during decision making in mice as part of an international group effort known as the @intlbrainlab.bsky.social.

🔗 pni.princeton.edu/news/2025/fi...

New work with @shiyanliang.bsky.social‬, @roxana-zeraati.bsky.social, @intlbrainlab.bsky.social, Anna Levina. We uncover the principles that organize single-neuron timescales across the entire brain, unifying regional specialization with universal brain-wide dynamics: www.biorxiv.org/content/10.1...

Out today in @nature.com: we show that individual neurons have diverse tuning to a decision variable computed by the entire population, revealing a unifying geometric principle for the encoding of sensory and dynamic cognitive variables.
www.nature.com/articles/s41...

“Like a group of skiers descending a mountain, each [neuron] prefers a slightly different path, but all are shaped by the same slope,” says PNI's @engeltatiana.bsky.social‬ on her lab’s new ‪@nature.com‬ study revealing how the brain makes decisions.

📰: pni.princeton.edu/news/2025/al...

Into population dynamics? Coming to #CNS2025 but not quite ready to head home?

Come join us! at the Symposium on "Neural Population Dynamics and Latent Representations"! 🧠
📆 July 10th
📍 Scuola Superiore Sant’Anna, Pisa (and online)
👉 Free registration: neurobridge-tne.github.io
#compneuro

Our new paper with @chrismlangdon is just out in @natureneuro.bsky.social! We show that high-dimensional RNNs use low-dimensional circuit mechanisms for cognitive tasks and identify a latent inhibitory mechanism for context-dependent decisions in PFC data.
www.nature.com/articles/s41...

Latent circuit inference from heterogeneous neural responses during cognitive tasks - Nature Neuroscience The latent circuit model identifies low-dimensional mechanisms of task execution from heterogenous neural responses. This approach reveals a latent inhibitory mechanism for context-dependent decisions...

The latent circuit model identifies low-dimensional mechanisms of task execution from heterogeneous neural responses. This approach revealed a latent inhibitory mechanism for context-dependent decisions in neural network models and prefrontal cortex 🧪🧠

www.nature.com/articles/s41...