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SUMMARY:Toward Scalable Neuromorphic Control: From Conductance Modelling t
 o Hierarchical Event-Based Architectures - Yongkang Huo\, University of Ca
 mbridge
DTSTART:20260312T140000Z
DTEND:20260312T150000Z
UID:TALK245608@talks.cam.ac.uk
CONTACT:Rodolphe Sepulchre
DESCRIPTION:This seminar summarises PhD research that develops a methodolo
 gy for scalable neuromorphic control\, linking the modelling of individual
  neuromorphic elements to the design of large event-based control networks
 . At the element level\, it introduces a kernel-based framework for learni
 ng fading-memory conductance dynamics from data while preserving causality
 \, memristive structure\, and time-scale separation. At the network level\
 , it develops rebound Winner-Take-All motifs that unify rhythmic generatio
 n and discrete decision-making in a hierarchical architecture. The framewo
 rk is demonstrated through a neuromorphic controller for a snake robot wit
 h layers for actuation\, coordination\, and supervisory switching.
LOCATION:LR3A\, Department of Engineering
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