Vikash Gilja is an assistant professor at UCSD, where he researchers brain-machine interfaces. Dr. Gilja is an advisor to Paradromics. He holds a Ph.D. in computer science from Stanford University, an M.Eng and B.S. in EECS from MIT, and a B.S. in brain and cognitive sciences from MIT.

Rob Edgington is the head of AI at Paradromics. He holds a Ph.D. in brain-machine interfaces from UCL, and an M.Phys in physics from the University of Oxford.

Konrad Kording is a full professor at the University of Pennsylvania, where he works on data problems in neuroscience. He holds a Ph.D. in physics from ETH Zurich.

Top 3 Takeaways

Basic neuroscience and neural engineering can and should co-evolve, much the same as physics and electrical engineering. More granular understandings from neuroscience help inform machine learning models applied in neurotechnology. Speech prostheses are a promising area for modern BMIs.

Show Notes

[1:10] Rob’s introduction.

[1:24] Konrad’s introduction.

[1:35] Vikash’s introduction.

[1:47] Avery’s introduction.

[2:05] Neuroscience vs. neurotechnology.

[2:55] Basic science and causality.

[3:35] Definition of causality.

[6:10] Closed-loops require causal models.

[8:15] Visual system as closing the loop.

[9:55] Electrical engineering is an analogy to neural engineering.

[12:20] Modern BMI devices.

[13:00] More data means more degrees of freedom.

[15:15] Distributed recordings.

[19:40] Data processing constraints in BMI.

[20:00] Ontology refinement.

[22:35] Timescale of tool development.

[23:45] Future-proofing a BMI.

[25:00] On-chip processing.

[26:00] Evolution of BMIs.

[27:15] Industry is good for integrating engineering constraints.

[29:30] Estimating intended speech.

[30:20] Neurotech for locked-in patients.

[32:30] Visual communication.

[34:00] ML vs. DL in neurotech.

[37:00] Better models are inspired by basic science.

[38:35] Hiring in neurotechnology.

Selected Links

Paradromics

The Neurotechnology Age – Matt Angle, CEO Paradromics

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