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.

It has been widely recognized that closed-loop neuroprosthetic systems achieve more favorable outcomes for users then equivalent open-loop devices. Improved performance of tasks, better usability and greater embodiment have all been reported in systems utilizing some form of feedback. However the interdisciplinary work on neuroprosthetic systems can lead to miscommunication due to similarities in well established nomenclature in different fields. Here we present a review of control strategies in existing experimental, investigational and clinical neuroprosthetic systems in order to establish a baseline and promote a common understanding of different feedback modes and closed-loop controllers. The first section provides a brief discussion of feedback control and control theory. The second section reviews the control strategies of recent Brain Machine Interfaces, neuromodulatory implants, neuroprosthetic systems and assistive neurorobotic devices. The final section examines the different approaches to feedback in current neuroprosthetic and neurorobotic systems.

Much attention has been focused on the detection of volitionary motor commands from the efferent Peripheral Nervous System as a control signal for an advanced prosthetic limb, or the delivery of artificial sensory data to the Peripheral Nervous System as feedback. Less explored has been the potential for natural sensory signals to act as sensor input to neuroprosthetic systems. Many conditions with paralysis as a symptom leave the afferent peripheral nervous system functional, and potentially available as a feedback signal to a control system. In order to demonstrate the feasibility of using such a signal we decode a multiunit afferent nerve signal and use an extreme learning machine to perform a regression to decode force data. From this we were able to show that afferent signals from the fingertip can be decoded into force profiles.

We will demonstrate a 100-CAR-section cochlear model running in real time on an FPGA. Although our result suggests that an electronic cochlea with 1224 cochlear sections can be implemented on an average FPGA [1], the data rate limit of USB 2.0 does not permit us to implement more than 100 filter sections and display the output on a PC. Future work will explore alternatives to increase the bandwidth such as a PCI interface or USB 3.0 that will enable us to implement more filter sections. Nonetheless, our work demonstrates the capability of the CAR model to process sound in real-time.