Matt Perich

They also are missing those pesky spinal cords 😬

Don't worry, we love the cerebellum here, just a more focused graphic design choice!

P.S., for those at Cosyne26, come find me or Margaux if you want to chat about these results! And stop by Margaux’s main meeting talk at 9:45am on Friday! 19/18

Neuroscience has a species problem If neuroscience is serious about building general principles of brain function, cross-species dialogue must become a core organizing principle.

I’ll end by echoing the sentiment put forward recently by @suthanalab.bsky.social. I think comparative analyses across species is ultimately going to greatly improve our understanding of neural computations and brain function. www.thetransmitter.org/animal-model... 18/18

Of course, the broader behavioral repertoires of these species can be quite different. There's much fascinating future work to understand how this shared base of computation adapts to enable, say, a human to play a piano sonata. But at some level, we argue there is conservation across species. 17/18

In summary, we argue that, at least for shared behaviors like reaching and grasping, evolution can maintain and repurpose computations as a base for future behavioral adaptations. 16/18

We found using DSA and CCA that RNNs find a wide range of control solutions, but generally geometry tended to track behavior and dynamics were independent. Interestingly, it was quite difficult to produce conserved geometries *and* dynamics without maintaining conserved circuit properties. 15/18

We then use RNN simulations with MotorNet (elifesciences.org/articles/88591) to explore how geometry relates to dynamics in neural circuits, by manipulating architectural properties (learning rule, effector, etc) and training the RNNs to perform the same reaching task. 14/18

We align the trajectory geometries with Canonical Correlation Analysis (CCA; previously shown to align across individuals in the same behavior: www.nature.com/articles/s41...) and found that geometries change as behavioral needs diverge. 13/18

You might be wondering: the bodies/effectors, brain anatomy, etc across these species seems so different; surely this must be accounted for *somewhere* in the brain? Indeed, we argue that individual- and species-specific variability can be accounted for in the geometry of neural trajectories. 12/18

I want to really stress how cool this is: the motor cortex of mice, monkeys, and humans really seem to operate by similar dynamics. It’s more similar across species than across regions or across behavioral phases in the same species! 11/18

We then show that neural dynamics also change significantly between planning and movement—two behavioral phases we believe should have different underlying computations, with planning ramping from quiescence and execution in a feedback control regime—even within monkeys. 10/18

We first contextualize the dynamical similarity by comparing across brain regions (motor and somatosensory cortex) in our human clinical trial participant. The input-driven nature of somatosensory cortex (just look at those trajectories!) led to huge differences in dynamical similarity. 9/18

We found that neural dynamics across species were surprisingly similar, on par with comparisons across different individuals within a species (mice/monkeys). 8/18

All three species were quite well-fit under these dynamical systems assumptions, with good quality fits to the neural trajectories. Fitting these models let us directly compare the learned state transition matrices as a window into dynamical similarity. 7/18

To assess conserved neural computations, we tested the similarity of neural dynamics with Dynamical Similarity Analysis, an awesome technique for comparing the “rules” of two dynamical systems (arxiv.org/abs/2306.10168). Low dynamical distance, here, implies similar computations. 6/18

We assembled a multi-species dataset of intracortical recordings of motor cortex during reaching and object interaction tasks in mice, monkeys, and humans and analyzed the motor cortical trajectories along neural manifolds. 5/18

We hypothesized that behavioral adaptations of modern species build on neural computations inherited from common ancestors. In our paper, we apply the computation through dynamics framework to investigate and directly compare computations across mice, monkeys, and humans. 4/18

Despite vast differences in brain/body anatomy, many species perform common behaviors such as reaching and grasping. Across mammals, the motor cortex (a homologous region that likely first evolved over 100 mya) is tightly linked to such behaviors. 3/18

This wouldn’t have happened without the massive efforts by co-first authors @oliviercodol.bsky.social & (the bluesky-less) Margaux Asclipe, and our amazing collaborators (especially @juangallego.bsky.social @dudman.bsky.social @ansobinov.bsky.social @glajoie.bsky.social). 2/18

New paper hot off the (pre-)press! We dig into the evolutionary origins of neural computations for behavioral control across mice, monkeys, and humans: www.biorxiv.org/content/10.6....

As our lab's first foray into comparative analysis of neural dynamics, I’m super excited about this work! 1/18

Humbled by Evolution Understanding the history and diversity of life inspires awe and wonder.

1. I’m quite happy with this popular piece that Lee Dugatkin and I wrote recently. For the next two weeks it’s free to read on the American Scientist website.

Primate dexterous hand movements are controlled by functionally distinct premotoneuronal systems Distinct spinal and cortical pathways coordinate muscle synergies and fine control to support primate dexterous hand movements.

Our paper is out in Science Advances!
What makes primate hands so dexterous?
We show that evolutionarily distinct spinal and cortical pathways work together to balance stability and flexibility, supporting remarkable primate hand control.
www.science.org/doi/10.1126/...

Preach.

Beautiful to see! Great work!

Yes, of course, a model which can't process "artificial" stimuli the way natural visual systems do is also not a good model of the natural visual system!

Indeed. A model that cannot process natural stimuli is simply not a good model of the natural visual system...

3. Backyard brains ephys stim kit hooked up to a cockroach leg. Play james brown from an iphone. Our body is electric!
4. Driving a lego mindstorms car w/ EMG signals from a surface electrode on the biceps. Pseudo-BCI demo!

Did a lot of outreach at Northwestern with other grad students there for a yearly "brain fair". Some fun things I remember:
1. Optical illusions are always fun
2. Put a sheep brain from the butcher in a blender to show brains are mostly fat

🚨📜+🧵🚨 Very excited about this work showing that people with no hand function following a spinal cord injury can control the activity of motor units from those muscles to perform 1D, 2D and 3D tasks, play video games, or navigate a virtual wheelchair

By a wonderful team co-mentored w Dario Farina

I mean, they're clearly on strike...