Bayesian Decoding

Title:

The Bayesian Decoding of Force Stimuli from Slowly Adapting Type I Fibers in Humans

Authors:

Patrick Kasi, James Wright, Heba Khamis, Ingvars Birznieks, André van Schaik

Journal:

PLOS One

Year:

2016

Abstract:

It is well known that signals encoded by mechanoreceptors facilitate precise object manipulation in humans. It is therefore of interest to study signals encoded by the mechanoreceptors because this will contribute further towards the understanding of fundamental sensory mechanisms that are responsible for coordinating force components during object manipulation. From a practical point of view, this may suggest strategies for designing sensory-controlled biomedical devices and robotic manipulators. We use a two-stage nonlinear decoding paradigm to reconstruct the force stimulus given signals from slowly adapting type one (SA-I) tactile afferents. First, we describe a nonhomogeneous Poisson encoding model which is a function of the force stimulus and the force's rate of change. In the decoding phase, we use a recursive nonlinear Bayesian filter to reconstruct the force profile, given the SA-I spike patterns and parameters described by the encoding model. Under the current encoding model, the mode ratio of force to its derivative is: 1: 26 to 1: 02. This indicates that the force derivative contributes significantly to the rate of change to the SA-I afferent spike modulation. Furthermore, using recursive Bayesian decoding algorithms is advantageous because it can incorporate past and current information in order to make predictions consistent with neural systems with little computational resources. This makes it suitable for interfacing with prostheses.

Files:

• README.txt. Describes the data in the .xls file
• Data.xls. The data used in the paper for analysis