Tim Sainburg
@timsainburg.bsky.social
1.8K followers 2.9K following 19 posts
Neuro, Cogsci, ML, and Ethology postdoc / Schmidt Science Fellow at Harvard.
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Reposted by Tim Sainburg
lenagies.bsky.social
Thank you to everyone at #ibac2025 for this amazing conference. I met so many great people and learned a lot about so many interesting projects. Excited to see you all again some time! And proud that my poster got the student poster price ☺️ stay tuned for the publication!
Me with my poster on the greylag goose vocal repertoire and how data representation types influence the predictions of unsupervised methods
Reposted by Tim Sainburg
Reposted by Tim Sainburg
markthornton.bsky.social
The psych job market may not be dead... but it is gravely injured 😬 So far it's looking like the Trump administration's attacks on higher ed/research are going to have more than 2x the impact on the job market as the covid-19 pandemic. #psychjobs #neurojobs #academicjobs
Bar plot showing the number of psychology jobs posted each year by area. There are major dips in 2020 due to covid, and in 2025 (now).
timsainburg.bsky.social
⭐ Long-term support: Actively maintained for 6+ years, 1700+ stars on GitHub, hundreds of citations (even without a paper!)
🐦 We also release Birdsong NOIZEUS, a new benchmark for bioacoustic denoising
timsainburg.bsky.social
✅ Domain-general: strong baseline when ML models/data aren’t available
🎛️ Stationary & non-stationary variants
⚡ GPU-accelerated for real-time and high-throughput use
🧪 Validated across many domains
timsainburg.bsky.social
New paper out today with Asaf Zorea : "Domain-general noise reduction for time-series signals with Noisereduce" (open access)!

We present Noisereduce, a lightweight Python library for denoising signals.

Read the paper: doi.org/10.1038/s415...
Code (library): github.com/timsainb/noi...
Domain general noise reduction for time series signals with Noisereduce - Scientific Reports
Scientific Reports - Domain general noise reduction for time series signals with Noisereduce
doi.org
Reposted by Tim Sainburg
felixbaier.bsky.social
🚨Very happy that my PhD work is now out in @nature.com!

We discovered that evolution, by acting in the midbrain, shifted the threshold to escape in Peromyscus mice, to fine-tune defensive strategies in different environments

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

This was a truly collaborative effort! 🧵⬇️
Reposted by Tim Sainburg
nickjourjine.bsky.social
Very happy to share the latest from my postdoc‬!

10 yrs of mouse social networks + 1.25 yrs of acoustic data ➡️ insight into vocalization & sociality in a wild population of your favorite lab model 🐁

paper: bit.ly/4n93yyD
data: bit.ly/4lfFBEk
code: bit.ly/4kNnMwx

#bioacoustics #neuroskyence

1/8
Graphical abstract for "Vocal communication is seasonal in social groups of wild, free-living house mice."

The abstract has, from top to bottom, a title, four middle image panels, and two bottom text panels.

Image title: "Vocal communication in social groups of wild-free living house mice"

Middle image panels from left to right: (1) An aerial snap shot of the region where the study site is located, an agricultural landscape in rural Switzerland. (2) An image of the study site, a small barn in the forest inhabited by mice. (3) An image of a radio frequency identification (RFID) box used to track mouse social interactions. A mouse is entering the box from the left while another sits outside. (4) A spectrogram showing example vocalizations - one low frequency squeak and one ultrasonic call - recorded from an RFID box.

Bottom panels:
Left: Data Collection 
- 10 years of RFID-based tracking data (from 6,946 mice)
- 15 months of acoustic monitoring (totaling 6,594 hours)
- Machine learning for vocal detection and labeling (CNN)

Right: Key Findings
- Vocalization is seasonal (most in spring and summer)
- Vocalization is associated with the presence of pups
- Vocalization is correlated with social group dynamics
Reposted by Tim Sainburg
biotay.bsky.social
1/8 Decoding Dolphin Communication

After studying 313 dolphins (in Sarasota, Florida) for over 40 years and across six generations, a catalog of their vocalizations has been produced.
These vocalizations are more complex than expected.

(preprint) www.biorxiv.org/content/10.1...
https://www.sarasotamagazine.com/travel-and-outdoors/2020/08/dolphin-watching-tour-sarasota
Reposted by Tim Sainburg
timsainburg.bsky.social
Sensory populations reflect the Bayesian likelihood. And expectation modulates sensory activity. But here’s the twist: sensory neurons don’t integrate the likelihood and prior expectation. (6/n)
timsainburg.bsky.social
Finally, this work is the product of an amazing team, in particular @trevorsupan.bsky.social and Tim Gentner! And a huge thank you to our reviewers and everyone who provided feedback throughout!
timsainburg.bsky.social
This was the longest project of my career! It started nine years ago, back in 2016, the second year of my PHD. Every paper I have ever published has been enveloped by this one.
timsainburg.bsky.social
Takeaway:

1) Song sequence perception follows Bayesian integration.
2) Sensory populations reflect the likelihood, and are modulated by expectation, but don't follow Bayesian integration.
3) Instead, expectation refines sensory precision, leaving an unbiased signal for downstream processing.
timsainburg.bsky.social
This challenges the idea that sensory neurons integrate prior expectations the way decision-making circuits do. Instead, expectation boosts acuity where needed, letting decision-making systems flexibly integrate an unbiased sensory signal. (8/n)
timsainburg.bsky.social
Instead of integrating expectations, neural populations enhance the likelihood of expected stimuli—sharpening perception rather than shifting it.
This means sensory systems maintain a veridical, faithful, representation of the world. (7/n)
timsainburg.bsky.social
Sensory populations reflect the Bayesian likelihood. And expectation modulates sensory activity. But here’s the twist: sensory neurons don’t integrate the likelihood and prior expectation. (6/n)
timsainburg.bsky.social
Behaviorally, birds integrate expectations and sensory input probabilistically, following a Bayesian strategy. This aligns with classic models of categorical perception. 5/n
timsainburg.bsky.social
To investigate this, we trained European starlings to classify ambiguous song syllables generated from a variational autoencoder. We manipulated expectation by changing the probabilities of syllables within song sequences. 4/n
timsainburg.bsky.social
We show that while decision-making systems integrate expectations probabilistically, sensory systems do something surprising: Rather than biasing perception, expectation sharpens it, enhancing sensory precision. 3/n
timsainburg.bsky.social
On one hand, we use expectations to pull experience into expected perceptual categories, stabilizing perception but *reducing acuity*. On the other hand our expectations allow us to focus our attention on relevant signals, *improving acuity*. 2/n