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.
Highlights
It has been demonstrated that tactile afferents—associated with cutaneous mechanoreceptors —signal information to the brain that is relevant for dexterous object manipulation in humans [1, 2]
Fast adapting type I (FA-I) afferents respond to dynamic skin deformations of relatively high frequencies (5–60 ms), slowly adapting type I (SA-I) afferents are tuned towards low frequency skin deformations of the glabrous skin [13, 14], fast adapting type II (FA-II) afferents are optimized for detecting transient mechanical events [13,14,15,16], and slowly adapting type II (SA-II) afferents respond to remotely applied
We can predict force profiles given results based on parameters learned during the encoding model and slowly adapting type I tactile afferents
Summary
It has been demonstrated that tactile afferents—associated with cutaneous mechanoreceptors —signal information to the brain that is relevant for dexterous object manipulation in humans [1, 2]. In order to gain insight into the representation and consequent reconstruction of properties of the object and motor control, a systematic approach within a quantitative framework that is simple to interpret is of interest. This will facilitate studying factors that concurrently contribute to the afferent spiking behavior. Neural decoding techniques that avoid explicit encoding models (reverse correlation) have been applied [20,21,22], and recently extended to tactile afferent data [23]
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