We have designed, fabricated, and tested an analog integrated-circuit architecture to implement the conductance-based dynamics that model the electrical activity of neurons. The dynamics of this architecture are in accordance with the Hodgkin-Huxley formalism, a widely exploited, biophysically plausible model of the dynamics of living neurons. Furthermore the architecture is modular and compact in size so that we can implement networks of silicon neurons, each of desired complexity, on a single integrated circuit. We present in this paper a six-conductance silicon-neuron implementation, and characterize it in relation to the Hodgkin-Huxley formalism. This silicon neuron incorporates both fast and slow ionic conductances, which are required to model complex oscillatory behaviors (spiking, bursting, subthreshold oscillations).

We hypothesize that one role of sensorimotor feedback for rhythmic movements in biological organisms is to synchronize the frequency of movements to the mechanical resonance of the body. Our hypothesis is based on recent studies that have shown the advantage of moving at mechanical resonance and how such synchronization may be possible in biology. We test our hypothesis by developing a physical system that consists of a silicon-neuron central pattern generator (CPG), which controls the motion of a beam, and position sensors that provide feedback information to the CPG. The silicon neurons that we use are integrated circuits that generate neural signals based on the Hodgkin-Huxley dynamics. We use this physical system to develop a model of the interaction between the sensory feedback and the complex dynamics of the neurons to create the closed-loop system behavior. This model is then used to describe the conditions under which our hypothesis is valid and the general effects of sensorimotor feedback on the rhythmic movements of this system.

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The complexity of problems that engineers are being asked to solve is increasing rapidly. Effective solutions often require the integration of mechanical, electrical, computer software, chemical, and/or biological components. In order to manage this complexity, it is becoming important for all engineering students to learn how to approach the solutions to these problems using a systems perspective (Baldwin 2014). In order to better motivate this approach to students the authors are introducing it within courses of their own engineering discipline. The authors are adapting traditional systems engineering concepts to create a framework of system models that can be introduced into courses of any engineering discipline at any level. Through the process of learning how to create these models, students gain an understanding of what is meant by a systems perspective and how this perspective can help them to solve problems. This paper discusses which systems models were incorporated into undergraduate curriculum and how each model is broken‐down into pieces that are easier for undergraduates to understand and faculty to teach.