Work

Learning-based Approaches for Controlling Neural Spiking

Year: 2018

Type: conference-paper

Abstract: 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.

Source: Proceedings of the ... American Control Conference/Proceedings of the American Control Conference

DOI: 10.23919/acc.2018.8431158

Authors: Sensen Liu, Noah M. Sock, ShiNung Ching

Institutions: Washington University in St. Louis, California Institute of Technology

Language: English

Topic: Neural dynamics and brain function

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

Open Access status: green

Funders: Wellcome Trust, National Eye Institute

Award: Spatiotemporal control of large neuronal networks using high dimensional optimization