What does the brain look like? As engineers, how can we look at neural networks without invasive surgery? In this activity, students design and build neuron models based on observations made while viewing neurons through a microscope. The models are used to explain how each structure of the neuron contributes to the overall function. Students share their models with younger students and explain what a neuron is, its function, and how engineers use their understanding of the neuron to make devices to activate neurons.

Students learn about complex networks and how to represent them using graphs. They also learn that graph theory is a useful mathematical tool for studying complex networks in diverse applications of science and engineering, such as neural networks in the brain, biochemical reaction networks in cells, communication networks, such as the internet, and social networks. Topics covered include set theory, defining a graph, as well as defining the degree of a node and the degree distribution of a graph.

Students build small-sized prototypes of mountain rescue litters rescue baskets for use in hard-to-get-to places, such as mountainous terrain to evacuate an injured person (modeled by a potato) from the backcountry. Groups design their litters within constraints: they must be stable, lightweight, low-cost, portable and quick to assemble. Students demonstrate their designs in a timed test during which they assemble the litter and transport the rescued person (potato) over a set distance.

In this lesson on the brain's neural networks, students investigate the structure and function of the neuron. They discover ways in which engineers apply this knowledge to the development of devices that can activate neurons. After a review of the nervous system specifically its organs, tissue, and specialized cells, called neurons students learn about the parts of the neuron. They explore the cell body, dendrites, axon and axon terminal, and learn how these structures enable neurons to send messages. They learn about the connections between engineering and other fields of study, and the importance of research, as they complete the lesson tasks.

Students learn about complex networks and how to use graphs to represent them. They also learn that graph theory is a useful part of mathematics for studying complex networks in diverse applications of science and engineering, including neural networks in the brain, biochemical reaction networks in cells, communication networks, such as the internet, and social networks. Students are also introduced to random processes on networks. An illustrative example shows how a random process can be used to represent the spread of an infectious disease, such as the flu, on a social network of students, and demonstrates how scientists and engineers use mathematics and computers to model and simulate random processes on complex networks for the purposes of learning more about our world and creating solutions to improve our health, happiness and safety.

This lesson describes the function and components of the human nervous system. It helps students understand the purpose of our brain, spinal cord, nerves and the five senses. How the nervous system is affected during spaceflight is also discussed in this lesson.

A simulation of how neuron's in the body work along with how a signal is sent along a neuron.

Vision is the primary sense of many animals and much is known about how vision is processed in the mammalian nervous system. One distinct property of the primary visual cortex is a highly organized pattern of sensitivity to location and orientation of objects in the visual field. But how did we learn this? An important tool is the ability to design experiments to map out the structure and response of a system such as vision. In this activity, students learn about the visual system and then conduct a model experiment to map the visual field response of a Panoptes robot. (In Greek mythology, Argus Panoptes was the "all-seeing" watchman giant with 100 eyes.) A simple activity modification enables a true black box experiment, in which students do not directly observe how the visual system is configured, and must match the input to the output in order to reconstruct the unseen system inside the box.

Building on their understanding of graphs, students are introduced to random processes on networks. They walk through an illustrative example to see how a random process can be used to represent the spread of an infectious disease, such as the flu, on a social network of students. This demonstrates how scientists and engineers use mathematics to model and simulate random processes on complex networks. Topics covered include random processes and modeling disease spread, specifically the SIR (susceptible, infectious, resistant) model.