Katrina Rose Quinn

Can't wait to see this fantastic line-up 🤩

Can humans use artificial limbs for body augmentation as flexibly as their own hands?
🚨 Our new interdisciplinary study put this question to the test with the Third Thumb (@daniclode.bsky.social), a robotic extra digit you control with your toes!
www.biorxiv.org/content/10.1...
🧵1/10

Overview of the simulation strategy and analysis. a) Pial and white matter boundaries surfaces are extracted from anatomical MRI volumes. b) Intermediate equidistant surfaces are generated between the pial and white matter surfaces (labeled as superficial (S) and deep (D) respectively). c) Surfaces are downsampled together, maintaining vertex correspondence across layers. Dipole orientations are constrained using vectors linking corresponding vertices (link vectors). d) The thickness of cortical laminae varies across the cortical depth (70–72), which is evenly sampled by the equidistant source surface layers. e) Each colored line represents the model evidence (relative to the worst model, ΔF) over source layer models, for a signal simulated at a particular layer (the simulated layer is indicated by the line color). The source layer model with the maximal ΔF is indicated by “˄”. f) Result matrix summarizing ΔF across simulated source locations, with peak relative model evidence marked with “˄”. g) Error is calculated from the result matrix as the absolute distance in mm or layers from the simulated source (*) to the peak ΔF (˄). h) Bias is calculated as the relative position of a peak ΔF(˄) to a simulated source (*) in layers or mm.

🚨🚨🚨PREPRINT ALERT🚨🚨🚨
Neural dynamics across cortical layers are key to brain computations - but non-invasively, we’ve been limited to rough "deep vs. superficial" distinctions. What if we told you that it is possible to achieve full (TRUE!) laminar (I, II, III, IV, V, VI) precision with MEG!

It's gotta be a Zelda playlist for me - those games trained me to problem-solve to that music 😆

Two examples of how contextual information can bias visual perception. Top: Luminance illusion created by shadows (source: https://persci.mit.edu/gallery/checkershadow). Square B looks brighter than square A but has the same luminance, i.e., they have identical grayscale values in the picture. Bottom: Perception of object motion is biased by self-motion. The combination of leftward self-motion and up-left object motion in the world produces retinal motion that is up-right. If the animal partially subtracts the optic flow vector (orange dashed arrow) generated by self-motion (yellow arrow) from the image motion on the retina (black arrow), they may have a biased perception of object motion (red arrow) that lies between retinal and world coordinates (green arrow).

Rewarding animals to accurately report their subjective #percept is challenging. This study formalizes this problem and overcomes it with a #Bayesian method for estimating an animal’s subjective percept in real time during the experiment @plosbiology.org 🧪 plos.io/3HaxiuB

🚨 New WP! 📄 "Publish or Procreate: The Effect of Motherhood on Research Performance" (w/ @valentinatartari.bsky.social
👩‍🔬👨‍🔬 We investigate how parenthood affects scientific productivity and impact — and find that the impact is far from equal for mothers and fathers.

Press release on our new paper from @hih-tuebingen.bsky.social 🧠🥳
Link: www.nature.com/articles/s42...
Thread: bsky.app/profile/migh...
#neuroskyence #compneurosky #magnetoencephalography

The members of the Cluster of Excellence "Machine Learning: New Perspectives for Science" raise their glasses and celebrate securing another funding period.

We're super happy: Our Cluster of Excellence will continue to receive funding from the German Research Foundation @dfg.de ! Here’s to 7 more years of exciting research at the intersection of #machinelearning and science! Find out more: uni-tuebingen.de/en/research/... #ExcellenceStrategy

Communication of perceptual predictions from the hippocampus to the deep layers of the parahippocampal cortex High-resolution neuroimaging reveals stimulus-specific predictions sent from hippocampus to the neocortex during perception.

Our study using layer fMRI to study the direction of communication between the hippocampus and cortex during perceptual predictions is finally out in Science Advances! Predicted-but-omitted shapes are represented in CA2/3 and correlate specifically with deep layers of PHC, suggesting feedback. 🧠🟦

Abstract choice representations during stable choice-response associations - Communications Biology Human magnetoencephalography reveals neural representations of perceptual choices that are abstracted from motor-responses even during stable choice-response associations. This suggests a general role...

Human MEG reveals neural representations of perceptual choices abstracted from motor-responses even during stable choice-response associations. @mightyrosequinn.bsky.social @siegellab.bsky.social @fsandhaeger.bsky.social @nimanoury.bsky.social @ezezelic.bsky.social. www.nature.com/articles/s42...

Suggesting that far from abstract choices being the exception, they could rather be the rule. 7/7

Figure showing distinct cortical distributions of neural information for choice and motor-response as a function of time.

Furthermroe, choice and motor-response showed distinct cortical distributions with choice over fronto-parietal regions. 6/7

Figure showing the time-course of abstract choice information.

We found neural representations of the perceptual choice, independent of those for motor-response and stimulus. 5/7

To test this we used a motion discrimination task where choice-motor mappings were held stable over longer time periods. We did this in conjunction with MEG measurements and an encoding framework that would allow us to disentangle representations of choice and motor-response. 4/7

But one limitation of these tasks has been that the mapping between perceptual choice and motor-response has varied rapidly. This could lead to a more flexible representation, in the form of an abstract decision variable, that might not be present in more stable contexts. 3/7

Perceptual decisions are often entangled with the motor-response used to make them, but we also make decisions that are not immediately linked to an action plan. Evidence from the lab has shown neural representations for these "abstract choices", even when the motor-response is known in advance. 2/7

Abstract choice representations during stable choice-response associations - Communications Biology Human magnetoencephalography reveals neural representations of perceptual choices that are abstracted from motor-responses even during stable choice-response associations. This suggests a general role...

Fresh off the press - our latest publication on abstract choices with @siegellab.bsky.social @fsandhaeger.bsky.social @nimanoury.bsky.social @ezezelic.bsky.social at @commsbio.nature.com
Link: www.nature.com/articles/s42...
🧵below (1/7)

I'm so sorry you've had to go through this. I wish you all the best for your recovery and look forward to a conference catch-up down the line. Best wishes to you and your family 🙏🏻

Rewriting the rules of locust swarms Classical models of collective behavior fail to explain the mechanisms driving desert locust swarms

#Swarm science just got a shake-up! 🦗🦗🦗For decades, #locusts were thought to move like particles but new research indicates they actually use sensory & cognitive mechanisms, not simple alignment @icouzin.bsky.social @mpi-animalbehav.bsky.social @sercansayin.bsky.social

Robust encoding of stimulus–response mapping by neurons in visual cortex | PNAS Neural activity in sensory cortex is modulated by behavioral and cognitive factors, and this modulation is thought to contribute to the selection o...

Robust encoding of stimulus–response mapping by neurons in visual cortex
doi.org/10.1073/pnas...
#neuroscience

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Intro time!

We are the Siegel Lab, located at the @unituebingen.bsky.social and @hih-tuebingen.bsky.social

Our central goal is to investigate how cognition emerges from dynamic interactions across widely distributed neuronal ensemble, combining mainly human MEG and animal electrophysiology.