Abstract
The lack of multi-scale empirical measurements (e.g., recording simultaneously from neurons, muscles, whole body, etc.) complicates understanding of sensorimotor function in humans. This is particularly true for the understanding of development during childhood, which requires evaluation of measurements over many years. We have developed a synthetic platform for emulating multi-scale activity of the vertebrate sensorimotor system. Our design benefits from Very Large Scale Integrated-circuit (VLSI) technology to provide considerable scalability and high-speed, as much as 365× faster than real-time. An essential component of our design is the proprioceptive sensor, or muscle spindle. Here we demonstrate an accurate and extremely fast emulation of a muscle spindle and its spiking afferents, which are computationally expensive but fundamental for reflex functions. We implemented a well-known rate-based model of the spindle (Mileusnic et al., 2006) and a simplified spiking sensory neuron model using the Izhikevich approximation to the Hodgkin–Huxley model. The resulting behavior of our afferent sensory system is qualitatively compatible with classic cat soleus recording (Crowe and Matthews, 1964b; Matthews, 1964, 1972). Our results suggest that this simplified structure of the spindle and afferent neuron is sufficient to produce physiologically-realistic behavior. The VLSI technology allows us to accelerate this behavior beyond 365× real-time. Our goal is to use this testbed for predicting years of disease progression with only a few days of emulation. This is the first hardware emulation of the spindle afferent system, and it may have application not only for emulation of human health and disease, but also for the construction of compliant neuromorphic robotic systems.
Highlights
The multi-scale nature of human nervous system makes it difficult to measure all relevant information about sensorimotor function
For the purpose of emulating sensorimotor function and disease, we argue that the model of spindle afferents does not have to include all anatomical details, but its outcome must satisfy at least two constraints: (1) distinguishable firing pattern between Group Ia and Group II afferents, and the difference should be compatible with physiological data; (2) distinguishable change in behavior that reflects changes in dynamic and static gamma fusimotor drive
Using our recently developed technique of neuromorphic emulation on programmable digital Very Large Scale Integrated-circuit (VLSI) hardware, we showed that a collection of spiking afferent fibers driven by a detailed model of muscle spindles suffice to produce biologically-realistic spindle afferents
Summary
The multi-scale nature of human nervous system makes it difficult to measure all relevant information about sensorimotor function. Since the 1980s, specialpurpose hardware with Very Large Scale Integrated-circuit (VLSI) technology started to benefit from some of the key insights in neural computation, including asynchrony among neurons, spike representation of information, and self-improving mechanisms such as plasticity (Mead, 1989; Serrano-Gotarredona et al, 2009; Indiveri et al, 2011; Neftci et al, 2014). This category of designs, termed “neuromorphic” hardware, has been successful in understanding mechanisms of memory (Chicca et al, 2003), visual representation (Lichtsteiner et al, 2008), and recently cognitive function (Eliasmith et al, 2012).
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