Jonathan A. Michaels
@JonAMichaels
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How do we move? I study brains and machines at York University (Assistant Professor). Full-time human.
Toronto
Joined October 2011
Can the motor system use sensory expectations to prepare for unexpected events?. Excited to share my latest work with @andpru – where we establish that sensory expectations shape neural population dynamics in motor circuits!. 🧵 and paper below.1/.
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Our work provides a mechanistic model of motor memory formation that: Demonstrates savings from intrinsic dynamics, supports a causal role for persistent neural traces, and bridges recent neurophysiological findings.
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We perturb the RNN’s preparatory activity along and against the neural shift direction. Perturbing with the shift enhances savings, while perturbing against it reduces or eliminates savings. This suggests a causal link between the trace and savings.
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Our model predicts in silico the same neural trace of prior learning seen in vivo in monkey motor cortex (Sun, O’Shea et al., Nature 2022): A shift in preparatory activity that persists after washout, without affecting movement.
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Savings refers to faster relearning after prior exposure to a motor task. We show that an RNN trained to control a realistic model of the arm predicts savings in motor learning without any explicit cues. This suggests a neural population-level account of how savings can emerge.
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In this work, we use a computational model to test the idea that savings in motor learning can emerge from neural population dynamics—even without explicit cues or cognitive strategies.
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Excited to share our new paper, led by @_mshahbazi, with @OlivierCodol and supervised by me and Paul Gribble, where we introduce a context-free model of savings in motor learning:
biorxiv.org
Learning to adapt voluntary movements to an external perturbation, whether mechanical or visual, is faster during a second encounter than during the first. The mechanisms underlying this phenomenon,...
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RT @aran_nayebi: In this lecture, we discuss task-optimized models of the motor system, surveying the classic work of Evarts, Georgopolous,….
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Very exciting initiative!.
We’re excited to announce the 18 teams we’re funding in Precision Neurotechnologies 🧠 They’ll work to unlock new ways to interface with the brain at the neural circuit level, with unprecedented precision. (1/3).
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Great decision. Congrats Frank!.
We celebrate the arrival of @WillettNeuro, who joins the department as an Assistant Professor of Neurosurgery. Dr. Willett previously worked as a research scientist in the Neural Prosthetics Translational Lab, where he will continue as co-director:
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RT @bsauerbrei1: Something I love about teaching: the opportunity to revisit foundational studies. Here, the measurement of potentials in s….
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RT @takaki_komiyama: 663 days since the senseless tragedy that took An, we present a manuscript that reports some of the discoveries that s….
biorxiv.org
During the execution of learned motor skills, the neural population in the layer 2/3 (L2/3) of the primary motor cortex (M1) expresses a reproducible spatiotemporal activity pattern. It is debated...
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RT @mehrdadkashefi: Excited to share my latest work with @DiedrichsenJorn and @andpru. In this work, we ask whether motor sequence learning….
biorxiv.org
Learning new sequential movements is a fundamental skill for many animals. Although the behavioral manifestations of sequence learning are clear, the underlying mechanisms remain poorly understood....
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More holiday reading! We argue that BCIs should be classified based on their intended application, and that this language is best for the public, regulators, policy makers, etc.
What is or isn't a "BCI"? (Brain Computer Interface). We argue in @natBME today that if the tech stimulates or records brain activity AND does computation, it's a BCI. This definition would align with the popular concept of BCI, but we still need a way to discuss different.
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RT @andpru: 📢📢📢 Big paper out from the lab today! We show how motor circuits across cortex and thalamus do sensory planning and how this im….
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@andpru . and much much more in the paper! Thanks to our fantastic collaborators, @mehrdadkashefi @OlivierCodol @Jeff_Weiler @DiedrichsenJorn and others. Science takes a long time. I drew up the initial idea for this experiment in 2017!.11/.
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@andpru Sensory expectations organize neural population activity so that – when triggered by an unexpected event – initial muscle activity reflects the best guess about how to respond, revealing a fundamental component of movement preparation in motor circuits. 10/
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@andpru However – using sensory expectations is only possible when incoming sensory information indicating perturbation direction coincides with – or is preceeded by – a condition-independent signal indicating that a perturbation has occurred. 9/
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@andpru Using neural networks coupled to a biomechanical model of the arm, we show that this neural geometry emerges naturally through training and causally controls the probabilistic modulation of feedback responses. 8/
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@andpru The structure of these preparatory signals in the neural population state was simple, scaling directly with the probability of each perturbation direction – and was accompanied by a very large condition-independent signals across motor areas. 7/
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@andpru Using high-density neural recordings (over 9500 single neurons!), we establish that sensory expectations are widespread across the brain, including the motor cortical areas involved in preparing self-initiated actions. 6/
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