A Caltech Library Service

Learning-based Approaches for Controlling Neural Spiking

Liu, Sensen and Sock, Noah M. and Ching, ShiNung (2018) Learning-based Approaches for Controlling Neural Spiking. In: 2018 Annual American Control Conference (ACC). IEEE , Piscataway, NJ, pp. 2827-2832. ISBN 9781538654286. PMCID PMC8046338.

[img] PDF - Published Version
See Usage Policy.

[img] PDF - Accepted Version
See Usage Policy.


Use this Persistent URL to link to this item:


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.

Item Type:Book Section
Related URLs:
URLURL TypeDescription CentralArticle
Additional Information:© 2018 AACC. 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.
Funding AgencyGrant Number
Burroughs-Wellcome FundUNSPECIFIED
Air Force Office of Scientific Research (AFOSR)15RT0189
PubMed Central ID:PMC8046338
Record Number:CaltechAUTHORS:20210422-085956347
Persistent URL:
Official Citation:S. Liu, N. M. Sock and S. Ching, "Learning-based Approaches for Controlling Neural Spiking," 2018 Annual American Control Conference (ACC), 2018, pp. 2827-2832, doi: 10.23919/ACC.2018.8431158
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:108801
Deposited By: Tony Diaz
Deposited On:22 Apr 2021 17:24
Last Modified:22 Apr 2021 17:24

Repository Staff Only: item control page