DARPA N11AP20002 (Gerling, G.J. with Cederna, P.S. and Urbanchek, M.G., U. of Michigan)

Computational Models and Real-time Prototypes to Mimic the Signal Modulation Capability in Connecting Artificial Tactile Sensors with the Peripheral Neural Afferents

Major goals: We seek to understand signal modulation parameters of the biological generation of action potentials in response to the controlled input of current pulse waveforms, a necessary step in the design of a neural prosthetic sense of touch.  There are two aims proposed: 1) determining the amplitude and duration of the minimum current waveform to consistently elicit single APs in the rat peripheral nerve, 2) eliciting trains of APs in the living rat which mimic natural AP response to ramp-and-hold stimuli and vibration by inputting a) controlled modulation current waveforms to the sensory PNI and b) progressing to a direct connection between force sensor and SPNI.  Dr. Gerling's group in specific will perform research design and coordination, modeling of empirical relationships in the data, mathematical optimization and experimental actuator design. 

Example project: Designing artificial sensor systems to mimic touch receptors.  As artificial touch in neural prostheses requires replacing the biological touch receptors lost through amputation, we transition our computational model to create a physical sensor system that mimics the output of natural touch receptors. A force sensor is embedded in a skin-like silicon substrate (A). The analog output of the sensor is converted via algorithms (B) to biologically relevant trains of action potentials (C) that represent how mechanoreceptors code the magnitude of stimulus indentation and velocity of stimulus movement.

Research Staff and Graduate Student: Elmer Kim and Aaron Williams

NIH NINDS R01 NS073119-01 (Gerling, G.J. and Lumpkin E.A., Columbia U.) 

Part of NSF-NIH Collaborative Research in Computational Neuroscience (CRCNS) Program

Collaborative Research: Predictively Modeling the Impact of Receptor Arrangement on Action Potential Initiation in Mammalian Touch Receptors (short title: CRCNS: Modeling impact of receptor arrangement spike initiation in touch)

Major goals: To develop a computational model that describes action potential timing in mammalian touch receptors.  My group in specific will perform computational modeling and experimental actuator design.  In our modeling effort, my group will use computational methods including solid mechanics, differential equations, statistics, and information theory to explain the biphasic sensitivity and variability of SAI afferents.  In our work with experimental actuator platforms, we will use a custom-built mechanical indenter to perform mechanical characterization of mouse skin and a second indenter to deliver vibration stimuli to characterize the noise and variability of afferents.

Example project: Modeling the spike timing variability from the SAI (slowly adapting type I) mechanoreceptor in the mouse.  In general, to better understand the neural basis of touch, we design controlled experiments where our custom-built, computer-controlled indenter (A) stimulates mouse skin samples with an intact touch receptor and nerve (B) while neural responses are recorded (C). We then construct computational models to explain the sub-transformations underlying the touch receptor’s neural response (D).  

Graduate Students: Daine Lesniak and Yuxiang "Shawn" Wang

National Library of Medicine T15LM009462 (Guerlain, S.A.; Harrison, J.H; Gerling, G.J.; Bass, E.J. and  other Co-Is)

Systems Engineering Focus on Clinical Informatics

Major goals: To train students in health and bioinformatics. This training grant funds approximately 7 graduate students and 3 post docs per year who will attain a degree in Systems and Information Engineering and who are co-advised by faculty in the healthcare and biological domains.  The grant is a 5-year renewable grant ending in 06.30.2012.  Dr. Gerling's role is in advising Post-doctoral scholars, Ph.D. students, and STTP students (under-represented minority students on 3 month assignments) students. He also serves on the Executive Committee and is in charge of recruiting.

Example project: A mixed reality environment for treating phantom pain.  Phantom Limb Pain (PLP) is chronic, severe, and affects up to 90% of amputees.  Recently, non-invasive therapies like mirror visual feedback (MVF) have shown promise in treating PLP, and this approach has been extended to virtual reality (VR).  These treatments are thought to work, in part, by eliminating or reducing the incongruence between the sensory and motor systems caused by deafferentation.  We hypothesize that a dynamic interaction between the phantom limb, intact limb, and the environment will afford more life-like simulations and enhanced pain relief.  To test this hypothesis we are developing a mixed reality (MR) simulator for the treatment of phantom limb pain.  The goals are to augment virtual reality strategies with additional sense modalities, to enable the phantom to interact and impart forces on the intact limb in real-time.

Figure 1. (left) Prototype mirror box with proprioceptive and haptic feedback via slider bar.  Our preliminary results are that with a small group (N=3) of amputee volunteers, the haptic mirror box provided pain relief within 10 minutes, motion of the phantom was increased, and tactile sensation of the intact limb was referred to the phantom.  Movement of the stump enhances the illusion for below-knee amputees, and reduces the illusion for above-knee amputees, (right) Concept for a 1 degree of freedom mixed reality environment utilizing the animation of phantom hands and fingers in the VR environment and the presentation of physical stimuli to the intact limb.

Graduate Students: Aaron Williams and Mark Farrington (past: Daine Lesniak and Anila Jahangiri)