Implications of the Dependence of Neuronal Activity on Neural Network States for the Design of Brain-Machine Interfaces.

Stefano Panzeri,Houman Safaai,Alessandro Vato,Vito De Feo

doi:10.3389/fnins.2016.00165

Abstract

Brain-machine interfaces (BMIs) can improve the quality of life of patients with sensory and motor disabilities by both decoding motor intentions expressed by neural activity, and by encoding artificially sensed information into patterns of neural activity elicited by causal interventions on the neural tissue. Yet, current BMIs can exchange relatively small amounts of information with the brain. This problem has proved difficult to overcome by simply increasing the number of recording or stimulating electrodes, because trial-to-trial variability of neural activity partly arises from intrinsic factors (collectively known as the network state) that include ongoing spontaneous activity and neuromodulation, and so is shared among neurons. Here we review recent progress in characterizing the state dependence of neural responses, and in particular of how neural responses depend on endogenous slow fluctuations of network excitability. We then elaborate on how this knowledge may be used to increase the amount of information that BMIs exchange with brain. Knowledge of network state can be used to fine-tune the stimulation pattern that should reliably elicit a target neural response used to encode information in the brain, and to discount part of the trial-by-trial variability of neural responses, so that they can be decoded more accurately.

Highlights

Brain-machine interfaces (BMIs) are devices mediating the dialogue between a brain and the external world
Afferent or sensory BMIs sense physical quantities from the environment and use an encoding interface to translate these sensory signals into patterns of neural activity elicited using causal interventions on the brain with the goal of provoking the desired sensation (Fitzsimmons et al, 2007)
Variability of neural activity cannot be reduced by improving technology to record and stimulate from ever increasing numbers of electrodes (Baranauskas, 2014; Lebedev, 2014), because some of the main sources of variability are generated at the network level and are shared across neurons (Goris et al, 2014; Lin et al, 2015; Schölvinck et al, 2015). This can be conceptualized by thinking of neural activity as state-dependent: neural activity does not depend only on external task-related variables and on internal network variables. In this Perspective article, we will discuss recent findings about state dependence of neural activity, and we will reason on how taking state dependence of neural activity into account can help us to build better sensory, motor, and bidirectional BMIs

Summary

INTRODUCTION

Brain-machine interfaces (BMIs) are devices mediating the dialogue between a brain and the external world. Efferent or motor BMIs use sensors to record neural activity—such as single-unit (SUA) or Multi-unit (MUA) activity, Local Field Potentials (LFPs), electrocorticograms (ECoG), or electroencephalograms (EEGs)—and decode this activity to infer the motor intent of the subject and command an artificial actuator (a robotic arm, a motorized wheelchair, or a computer cursor). These systems can have a considerable clinical impact for the treatment of patients with neurological diseases such as stroke, spinal cord injury, or Parkinson’s disease

BMIs and Neural State Dependence

STATE DEPENDENCE OF NEURAL RESPONSES

PRACTICAL CHALLENGES FOR EXPLOITING STATE DEPENDENCE FOR BMIs