While searching for objects, our brain uses internal (self-generated) and external cues to pinpoint its target. Where in the brain does the confluence of self-motion and stimulus space arise?

RT @LabPluta: I'm happy to share the latest manuscript from my lab, where we reveal how behavioral context transforms the somatotopic map i….

RT @VatsalaT: Preprint alert! Thesis work of @VandanaAgarwal_ jointly with @SriramRN1, Mohini Sengupta, @varmaalok22 @sudeepta_sarkar, and….

RT @KrishnaJayant: The lab's first paper, "Probing multiplexed basal dendritic computations using two-photon 3D holographic uncaging," is n….

RT @DiegoAldarondo: Happy to see my thesis work out today in @Nature! Josh Merel @jessedmarshall @lhq20 @yuvaltassa….

RT @PurdueBiolSci: Celebrating our wonderful students, faculty, and staff at the Department of Biological Science Spring Awards! A May to r….

RT @PurdueBiolSci: Scott Pluta, Assistant Prof of Biological Sciences, immerses himself in the complexities of neural integration. In a wor….

Keep an eye out for an exciting lineup of groundbreaking papers from @LabPluta scheduled for release this year!.

Check out our preprint for more interesting results.

Lastly, we discovered that the ongoing internal representation of whisker position in SC neurons directly modulates the strength of their tactile response, thereby potentially computing the egocentric distance of the object upon touch.

By trimming the length of the whisker, we discovered that sensory reafference plays a significant role in the representation of self-motion in some but not all neurons.

We experimentally tested this theory by revealing that a simple decoder model accurately predicts whisker angle and locomotion speed from SC activity spanning past, present, and future time bins.

Next, we hypothesized that neurons which encode multiple features could be essential for building an accurate model of whisker position (by combining midpoint, amplitude, and phase, as shown below), or for coordinating whisking and locomotion.

Interestingly, we found that SC neurons not only encode the current position of the body, but also past and future movements, thus mapping out a population-level trajectory of self-motion.

To disentangle which feature(s) provided a significant contribution to spike prediction, we implemented a Poisson GLM. We discovered that most neurons encoded a single kinematic feature, while in other neurons, prediction accuracy benefited from multiple features.

First, we notice that the firing rates of SC neurons were linearly related to the different kinematic features.

We targeted the mouse superior colliculus (SC), due to its close relationship with the sensory periphery and higher-order motor regions. We tracked locomotion speed and whisker motion while simultaneously recording populations of neurons.

Thank you @PurdueBiolSci for featuring my research. Grateful to my mentor, Scott Pluta @LabPluta , for his invaluable support in exploring the brain's complexities. Excited to continue unraveling these mysteries. Read more about our research -

Thank you so much, Scott (@LabPluta) for making me the scientist I am today. I am incredibly grateful and proud of this work. Can't wait to do more science! 👩‍🔬.@PurdueBiolSci @PurdueScience.