Date(s) - 03/14/2018
3:30 pm - 4:30 pm

Please join us for March’s UWIN seminar! This installment features a captivating duo of short talks by UWIN faculty members Steve Perlmutter and Steve Brunton:

  • Changes in Corticospinal Synaptic Strength Lead to Compensatory Changes in Cortical Neuron Firing. What’s the Feedback Signal?”
    Steve Perlmutter, Research Associate Professor, Department of Physiology & Biophysics, University of Washington
  • Learning physics and the physics of learning”
    Steve Brunton, Assistant Professor, Department of Mechanical Engineering, University of Washington

The seminar is on Wednesday, March 14th, 2018, at 3:30pm in Husky Union Building (HUB) 337.  Refreshments will be served prior to the talks.


“Changes in Corticospinal Synaptic Strength Lead to Compensatory Changes in Cortical Neuron Firing. What’s the Feedback Signal?” (Steve Perlmutter):

We are using activity-dependent electrical stimulation to induce synaptic plasticity in behaving non-human primates. Spinal stimulation triggered by corticomotoneuronal cell activity leads to increases in synaptic strength at the synapse to spinal motoneurons.  The activity pattern of the triggering cell changes after the conditioning in a compensatory manner.  The mechanism for this compensatory change is not clear, but suggests an unexpectedly tight feedback loop to precisely regulate cortical output to motoneurons.


Learning physics and the physics of learning” (Steve Brunton):

The ability to discover physical laws and governing equations from data is one of humankind’s greatest intellectual achievements.  A quantitative understanding of dynamic constraints and balances in nature has facilitated rapid development of knowledge and enabled advanced technology, including aircraft, combustion engines, satellites, and electrical power.  There are many more critical data-driven problems, such as understanding cognition from neural recordings, inferring patterns in climate, determining stability of financial markets, predicting and suppressing the spread of disease, and controlling turbulence for greener transportation and energy.  With abundant data and elusive laws, data-driven discovery of dynamics will continue to play an increasingly important role in these efforts.  This work develops a general framework to discover the governing equations underlying a dynamical system simply from data measurements, leveraging advances in sparsity-promoting techniques and machine learning.