TY - GEN
T1 - Learning-based Approaches for Controlling Neural Spiking
AU - Liu, Sensen
AU - Sock, Noah M.
AU - Ching, Shinung
N1 - Funding Information:
S. Ching holds a Career Award at the Scientific Interface from the Burroughs-Wellcome Fund. This work was partially supported by AFOSR 15RT0189, NSF ECCS 1509342, NSF CMMI 1537015 and NSF CMMI 1653589, from the US Air Force Office of Scientific Research and the US National Science Foundation, respectively.
Publisher Copyright:
© 2018 AACC.
PY - 2018/8/9
Y1 - 2018/8/9
N2 - We consider the problem of controlling populations of interconnected neurons using extrinsic stimulation. Such a problem, which is relevant to applications in both basic neuroscience as well as brain medicine, is challenging due to the nonlinearity of neuronal dynamics and the highly unpredictable structure of underlying neuronal networks. Compounding this difficulty is the fact that most neurostimulation technologies offer a single degree of freedom to actuate tens to hundreds of interconnected neurons. To meet these challenges, here we consider an adaptive, learning-based approach to controlling neural spike trains. Rather than explicitly modeling neural dynamics and designing optimal controls, we instead synthesize a so-called control network (CONET) that interacts with the spiking network by maximizing the Shannon mutual information between it and the realized spiking outputs. Thus, the CONET learns a representation of the spiking network that subsequently allows it to learn suitable control signals through a reinforcement-type mechanism. We demonstrate feasibility of the approach by controlling networks of stochastic spiking neurons, wherein desired patterns are induced for neuron-to-actuator ratios in excess of 10 to 1.
AB - We consider the problem of controlling populations of interconnected neurons using extrinsic stimulation. Such a problem, which is relevant to applications in both basic neuroscience as well as brain medicine, is challenging due to the nonlinearity of neuronal dynamics and the highly unpredictable structure of underlying neuronal networks. Compounding this difficulty is the fact that most neurostimulation technologies offer a single degree of freedom to actuate tens to hundreds of interconnected neurons. To meet these challenges, here we consider an adaptive, learning-based approach to controlling neural spike trains. Rather than explicitly modeling neural dynamics and designing optimal controls, we instead synthesize a so-called control network (CONET) that interacts with the spiking network by maximizing the Shannon mutual information between it and the realized spiking outputs. Thus, the CONET learns a representation of the spiking network that subsequently allows it to learn suitable control signals through a reinforcement-type mechanism. We demonstrate feasibility of the approach by controlling networks of stochastic spiking neurons, wherein desired patterns are induced for neuron-to-actuator ratios in excess of 10 to 1.
UR - http://www.scopus.com/inward/record.url?scp=85052557984&partnerID=8YFLogxK
U2 - 10.23919/ACC.2018.8431158
DO - 10.23919/ACC.2018.8431158
M3 - Conference contribution
AN - SCOPUS:85052557984
SN - 9781538654286
T3 - Proceedings of the American Control Conference
SP - 2827
EP - 2832
BT - 2018 Annual American Control Conference, ACC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 Annual American Control Conference, ACC 2018
Y2 - 27 June 2018 through 29 June 2018
ER -