TY - GEN
T1 - Optimizing the dynamics of spiking networks for decoding and control
AU - Huang, Fuqiang
AU - Riehl, James
AU - Ching, Shinung
N1 - Publisher Copyright:
© 2017 American Automatic Control Council (AACC).
PY - 2017/6/29
Y1 - 2017/6/29
N2 - In this paper, an optimization-based approach to construct spiking networks for the purposes of decoding and control is presented. Specifically, we postulate a simple objective function wherein a network of interacting, primitive spiking units is decoded in order to drive a linear system along a prescribed trajectory. The units are assumed to spike only if doing so will decrease a specified objective function. The optimization gives rise to an emergent network of neurons with diffusive dynamics and a threshold-based spiking rule that bears resemblance to the Integrate and Fire neural model.
AB - In this paper, an optimization-based approach to construct spiking networks for the purposes of decoding and control is presented. Specifically, we postulate a simple objective function wherein a network of interacting, primitive spiking units is decoded in order to drive a linear system along a prescribed trajectory. The units are assumed to spike only if doing so will decrease a specified objective function. The optimization gives rise to an emergent network of neurons with diffusive dynamics and a threshold-based spiking rule that bears resemblance to the Integrate and Fire neural model.
UR - http://www.scopus.com/inward/record.url?scp=85027016022&partnerID=8YFLogxK
U2 - 10.23919/ACC.2017.7963374
DO - 10.23919/ACC.2017.7963374
M3 - Conference contribution
AN - SCOPUS:85027016022
T3 - Proceedings of the American Control Conference
SP - 2792
EP - 2798
BT - 2017 American Control Conference, ACC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 American Control Conference, ACC 2017
Y2 - 24 May 2017 through 26 May 2017
ER -