A PDMS-based integrated stretchable microelectrode array (isMEA) for neural and muscular surface interfacing.

Numerous applications in neuroscience research and neural prosthetics, such as electrocorticogram (ECoG) recording and retinal prosthesis, involve electrical interactions with soft excitable tissues using a surface recording and/or stimulation approach. These applications require an interface that is capable of setting up high-throughput communications between the electrical circuit and the excitable tissue and that can dynamically conform to the shape of the soft tissue. Being a compliant material with mechanical impedance close to that of soft tissues, polydimethylsiloxane (PDMS) offers excellent potential as a substrate material for such neural interfaces. This paper describes an integrated technology for fabrication of PDMS-based stretchable microelectrode arrays (MEAs). Specifically, as an integral part of the fabrication process, a stretchable MEA is directly fabricated with a rigid substrate, such as a thin printed circuit board (PCB), through an innovative bonding technology-via-bonding-for integrated packaging. This integrated strategy overcomes the conventional challenge of high-density packaging for this type of stretchable electronics. Combined with a high-density interconnect technology developed previously, this stretchable MEA technology facilitates a high-resolution, high-density integrated system solution for neural and muscular surface interfacing. In this paper, this PDMS-based integrated stretchable MEA (isMEA) technology is demonstrated by an example design that packages a stretchable MEA with a small PCB. The resulting isMEA is assessed for its biocompatibility, surface conformability, electrode impedance spectrum, and capability to record muscle fiber activity when applied epimysially.

A robotic device for understanding neuromechanical interactions during standing balance control.

Postural stability in standing balance results from the mechanics of body dynamics as well as active neural feedback control processes. Even when an animal or human has multiple legs on the ground, active neural regulation of balance is required. When the postural configuration, or stance, changes, such as when the feet are placed further apart, the mechanical stability of the organism changes, but the degree to which this alters the demands on neural feedback control for postural stability is unknown. We developed a robotic system that mimics the neuromechanical postural control system of a cat in response to lateral perturbations. This simple robotic system allows us to study the interactions between various parameters that contribute to postural stability and cannot be independently varied in biological systems. The robot is a 'planar', two-legged device that maintains compliant balance control in a variety of stance widths when subject to perturbations of the support surface, and in this sense reveals principles of lateral balance control that are also applicable to bipeds. Here we demonstrate that independent variations in either stance width or delayed neural feedback gains can have profound and often surprisingly detrimental effects on the postural stability of the system. Moreover, we show through experimentation and analysis that changing stance width alters fundamental mechanical relationships important in standing balance control and requires a coordinated adjustment of delayed feedback control to maintain postural stability.

Stimulus-artifact elimination in a multi-electrode system.

To fully exploit the recording capabilities provided by current and future generations of multi-electrode arrays, some means to eliminate the residual charge and subsequent artifacts generated by stimulation protocols is required. Custom electronics can be used to achieve such goals, and by making them scalable, a large number of electrodes can be accessed in an experiment. In this work, we present a system built around a custom 16-channel IC that can stimulate and record, within 3 ms of the stimulus, on the stimulating channel, and within 500 mus on adjacent channels. This effectiveness is achieved by directly discharging the electrode through a novel feedback scheme, and by shaping such feedback to optimize electrode behavior. We characterize the different features of the system that makes such performance possible and present biological data that show the system in operation. To enable this characterization, we present a framework for measuring, classifying, and understanding the multiple sources of stimulus artifacts. This framework facilitates comparisons between artifact elimination methodologies and enables future artifact studies.

Functional consequences of model complexity in rhythmic systems: II. Systems performance of model and hybrid oscillators.

Neural models are increasingly being used as design components of physical systems. In order to best use models in these novel contexts, we must develop design rules that describe how decisions in model construction relate to the functional performance of the resulting system. In the accompanying paper, we described a series of related neuron models of varying complexity. Here, we use these models to build several half-center oscillators, and investigate how model complexity influences the robustness and flexibility of these oscillators. Our results indicate that model complexity has a significant effect on the robustness and flexibility of systems that incorporate neural models.

Functional consequences of model complexity in rhythmic systems: I. Systematic reduction of a bursting neuron model.

Neural models are increasingly being used as design components of physical systems. In order to most effectively utilize neuronal models in these novel contexts, we need to develop design rules for neuronal systems that relate how model design affects overall system performance. In this paper and a companion article, we investigate how the complexity of a neural model affects the performance of a two-cell oscillator built from the model. In this paper, we create a series of related neuron models with different mathematical complexity. Starting with a complex mechanistic model of a bursting neuron, we use a variety of techniques to create a series of simplified neuron models. These three reduced models produce bursting activity that is qualitatively very similar to the original model. In the following companion article, we investigate the functional performance of oscillators built from these models.

Effects of stance width on control gain in standing balance.

Standing in a wide stance during a lateral perturbation is considered to be easier than standing in a narrow stance, but the basis for this ease of stance is not understood. To study the effects of increased stance width in balance control, we created a standing model of a cat with variable stance width and subjected it to lateral displacement perturbations. We studied balance control while varying postural orientation and control parameters that are not accessible in a biological cat. We determined that delayed feedback in the postural controller necessitates the reduction of active feedback gain as stance width increases from narrow to wide stance. By establishing the change in control requirements in a system that resembles a biological configuration, we can predict that similar control changes may occur in biological systems.

Multielectrode impedance tuning: reducing noise and improving stimulation efficacy.

Multielectrode arrays (MEAs) have emerged as a leading technology for extracellular, electrophysiological investigations of neuronal networks. The study of biological neural networks is a difficult task that is further confounded by mismatches in electrode impedance. Electrode impedance plays an important role in shaping incoming signals, determining thermal noise, and influencing the efficacy of stimulation. Our approach to optimally reduce thermal noise and improving the reliability of stimulation is twofold minimize the impedance and match it across all electrodes. To this aim, we have fabricated a device that allows for the automated, impedance-controlled electroplating of micro-electrodes. This device is capable of rapidly (minutes) producing uniformly low impedances across all electrodes in an MEA. The need for uniformly low impedances is important for controlled studies of neuronal networks; this need will increase in the future as MEA technology scales from tens of electrodes to thousands.

An integrated circuit implementation of the Huxley sarcomere model.

We have developed an integrated circuit to simulate the mechanical behavior demonstrated by sarcomeres found in skeletal muscle. The circuit is based upon the mathematical description of the attachment and detachment dynamics of crossbridge populations and the force generated by the crossbridges, originally formulated by A. F. Huxley. We describe the process of designing the circuit model from the mathematical model, present the sarcomere circuit implementation, and demonstrate the transient and steady-state behaviors that the fabricated circuit produces. Comparison of our results to published mechanical behavior of skeletal muscle shows qualitative similarities. We conclude that the circuit muscle model exhibits the potential for real-time simulation of muscle contractions and could be used to give engineered systems muscle-like properties.

Modeling alternation to synchrony with inhibitory coupling: a neuromorphic VLSI approach.

We developed an analog very large-scale integrated system of two mutually inhibitory silicon neurons that display several different stable oscillations. For example, oscillations can be synchronous with weak inhibitory coupling and alternating with relatively strong inhibitory coupling. All oscillations observed experimentally were predicted by bifurcation analysis of a corresponding mathematical model. The synchronous oscillations do not require special synaptic properties and are apparently robust enough to survive the variability and constraints inherent in this physical system. In biological experiments with oscillatory neuronal networks, blockade of inhibitory synaptic coupling can sometimes lead to synchronous oscillations. An example of this phenomenon is the transition from alternating to synchronous bursting in the swimming central pattern generator of lamprey when synaptic inhibition is blocked by strychnine. Our results suggest a simple explanation for the observed oscillatory transitions in the lamprey central pattern generator network: that inhibitory connectivity alone is sufficient to produce the observed transition.

A simple neuron servo.

A simple servo controller built from components having neuronlike features is described. This VLSI servo controller uses pulses for control and is orders of magnitude smaller than a conventional system. The basic circuit elements are described. A key element is a component and neuronlike capability that takes voltages as inputs and generates a pulse train as the output. It is shown how the circuits are combined to a proportional and derivative controller. The advantages of using a pulsed output representation to improve slow-speed operation of a friction-limited system is demonstrated. The utility of exploiting parallelism, aggregation, and redundancy to improve system-level performance given imprecise low-level components is discussed. Experimental results illustrate the properties of the system compared with conventional controllers.