This review focuses on recent directions stemming from work by the authors and collaborators in the emerging field of neurotechnology. Neurotechnology has the potential to provide a greater understanding of the structure and function of the complex neural circuits in the brain, as well as impacting the field of brain-machine interfaces (BMI). We envision ultralow-power wireless neural interface systems that are life-lasting, fully integrated, and that supports bidirectional data flow with high bandwidth. Moreover, we believe in the importance of building neural interface technology that is truly tetherless, has a very small recording footprint, and little to no mechanical coupling between the sensor and the external world. We believe these developments will impact both neuroscience and neurology, revealing fundamental insight about how the nervous system functions in health and disease.

This work incorporates an open-ended design experience into an introductory circuits laboratory with the intended outcome of increasing self-efficacy for circuit prototyping and design. The authors have implemented a tinkering-based laboratory, in which students spend each lab period building a component of an inexpensive robot. The course culminates in a four week open-ended final hack that adds functionality to the finished robot. This design project prompts students to make connections across disciplines and exercise design thinking in a low-stakes environment. To determine the impact of this design experience on student learning, self-efficacy was measured through optional surveys administered both before and after the final hack. The design project resulted in a significant increase in design self-efficacy for students with some prior electronics experience, as well as an increase in prototyping self-efficacy in less experienced students, indicating that the design requirement had a positive impact on self-efficacy overall. It also showed that undergraduates in this course were ready to engage in a structured open-ended design experience even though they did not have a classical foundation in all the relevant theory, a common justification for the omission of design projects from intermediate-level engineering curricula.

In the original demonstration of insect flight control [2,4], flight initiation, cessation and elevation control were accomplished through neural stimulus of the brain which elicited, suppressed or modulated wing oscillation. Turns were triggered through the direct muscular stimulus of either of the basalar muscles. We characterized the response times, success rates, and free-flight trajectories elicited by our neural control systems in remotely controlled beetles.

In their first electronics course, many students find operational amplifiers, analog filters and sensor interface circuitry perplexing and daunting. The purpose of this paper is to present a circuit that addresses these pitfalls. A simplified electroencephelogram (EEG) circuit that is interfaced to a digital backend is proposed. The completed circuit involves using instrumentation amplifiers and filters for the EEG interface. The digital backend helps analyze EEG data on the computer.

Efforts to engineer synthetic gene networks that spontaneously produce patterning in multicellular ensembles have focused on Turing's original model and the "activator-inhibitor" models of Meinhardt and Gierer. Systems based on this model are notoriously difficult to engineer. We present the first demonstration that Turing pattern formation can arise in a new family of oscillator-driven gene network topologies, specifically when a second feedback loop is introduced which quenches oscillations and incorporates a diffusible molecule. We provide an analysis of the system that predicts the range of kinetic parameters over which patterning should emerge and demonstrate the system's viability using stochastic simulations of a field of cells using realistic parameters. The primary goal of this paper is to provide a circuit architecture which can be implemented with relative ease by practitioners and which could serve as a model system for pattern generation in synthetic multicellular systems. Given the wide range of oscillatory circuits in natural systems, our system supports the tantalizing possibility that Turing pattern formation in natural multicellular systems can arise from oscillator-driven mechanisms.