Introduction
In their natural context, neural circuits are part of a sensory-motor loop. They are embodied along with sensory organs to perceive the environment in which the animal is situated. Neurons in turn control the animal's movements, which results in new sensory input. This tight, continuous sensory-motor loop (from brain, to body, to environment, back to brain) is important for learning to predict the consequences of actions and is essential for optimizing adaptive behaviors (Chiel and Beer, 1997; Clark, 1997; Pfeifer and Bongard, 2007). Neuroscience researchers and biomedical engineers are beginning to appreciate how closing the loop around a neural circuit (Potter et al., 2006; Arsiero et al., 2007) can provide more natural information about nervous system dynamics, and lead to more effective treatment of nervous system disorders. Consider a typical open-loop experiment, where sensory input is presented to an anesthetized, reduced, or even disembodied nervous system, and its response is measured. In contrast, with closed-loop experiments, some aspects of how the nervous system responds will determine what is presented next, in real time, without experimenter intervention (Brice and McLellan, 1980). In this way, input and output sides of the nervous system can be studied together, in a more natural context. On the clinical side, consider the deep-brain stimulation currently used to treat Parkinsonism: stimulation parameters remain constant at levels set by the clinician, operating open-loop, regardless of the present brain state of the patient. In contrast, future closed-loop therapies will continuously tailor brain stimulation to optimize therapeutic effect and respond to changing brain states. By closing the loop with technology, researchers can probe or alter nervous system function not only in intact animals, but also in reduced preparations such as hybrots (Reger et al., 2000; DeMarse et al., 2001; Potter, 2002; Karniel et al., 2005; Bakkum et al., 2007). Considering that nervous systems are dynamic, complex, and responsive, it is logical that the tools we use to probe and modulate them should also be dynamic, complex, and responsive.
Closed-loop electrophysiology systems have two main parts, viewed from the animal's perspective: an output (efferent) side, which acquires data from the neural system, and a sensory (afferent) side, which influences the neural system. Among the many ways to acquire neural signals (including electroencephalography, functional magnetic resonance imaging, magnetoencephalography, optical imaging, etc.), we focus on extracellular multi-microelectrode array (MEA) recordings. Likewise, there are many possible ways to alter neural activity: via the animal's own sensory system, with electrical stimulation, pharmacology, optical control of ion channels (Zhang et al., 2007), etc. We focus on delivering neural stimuli via MEAs. Although MEAs are a proven mode of acquiring neural data for a range of prostheses and preparations (Chapin and Moxon, 2000; Taketani and Baudry, 2006), they are less often used for delivering stimulation patterns, with the notable and very successful exception of cochlear prostheses. Motor prostheses, such as artificial limbs, would benefit from having artificial sensory feedback (tactile, kinesthetic). Sensory prostheses and brain stimulation therapies might adapt to the users’ needs more quickly or effectively if they recorded and rapidly responded to the effects of their neural stimuli. Therapeutic and research tools enabled with closed-loop technology can deliver complex stimulation via many microelectrodes, while neural responses are continuously monitored via the same set of microelectrodes to influence subsequent stimulation. Integrated, responsive hybrid neural systems, comprised of both living and artificial components, will someday be commonplace.
For technical reasons such as stimulation artifacts and the unavailability of adequate commercial systems, MEAs are very seldom used for both stimulation and recording in one preparation. We have overcome some of the technical difficulties, and have developed closed-loop electrophysiology systems, including hardware and software, as part of an ongoing effort to study learning and information processing in vitro (Wagenaar et al., 2004, 2005a,b; Wagenaar and Potter, 2004).