This lesson explores the similarities between how a human being moves/walks and how a robot moves. This allows students to see the human body as a system, i.e., from the perspective of an engineer. It shows how movement results from (i) decision making, i.e., deciding to walk and move, and (ii) implementing the decision by conveying the decision to the muscle (human) or motor (robot).

Survey of principles underlying the structure and function of the nervous system, integrating molecular, cellular, and systems approaches. Topics: development of the nervous system and its connections, cell biology or neurons, neurotransmitters and synaptic transmission, sensory systems of the brain, the neuroendocrine system, the motor system, higher cortical functions, behavioral and cellular analyses of learning and memory. First half of an intensive two-term survey of brain and behavioral studies for first-year graduate students. Open to graduate students in other departments, with permission of instructor.

This class is the second half of an intensive survey of cognitive science for first-year graduate students. Topics include visual perception, language, memory, cognitive architecture, learning, reasoning, decision-making, and cognitive development. Topics covered are from behavioral, computational, and neural perspectives.

Students learn about the similarities between the human brain and its engineering counterpart, the computer. Since students work with computers routinely, this comparison strengthens their understanding of both how the brain works and how it parallels that of a computer. Students are also introduced to the "stimulus-sensor-coordinator-effector-response" framework for understanding human and robot actions.

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.

This course is designed to provide an understanding of how the human brain works in health and disease, and is intended for both the Brain and Cognitive Science major and the non-Brain and Cognitive Science major. Knowledge of how the human brain works is important for all citizens, and the lessons to be learned have enormous implications for public policy makers and educators. The course will cover the regional anatomy of the brain and provide an introduction to the cellular function of neurons, synapses and neurotransmitters. Commonly used drugs that alter brain function can be understood through a knowledge of neurotransmitters. Along similar lines, common diseases that illustrate normal brain function will be discussed. Experimental animal studies that reveal how the brain works will be reviewed. Throughout the seminar we will discuss clinical cases from Dr. Byrne's experience that illustrate brain function; in addition, articles from the scientific literature will be discussed at each class.

For undergraduates taking Course 9 IAP subjects for credit. See IAP Guide for details.

Students continue their exploration of the human senses and their engineering counterparts, focusing on the auditory sense. Working in small groups, students design, create and run programs to control the motion of LEGO® TaskBots. By doing this, they increase their understanding of the use and function of sound sensors, gain experience writing robot programs, and reinforce their understanding of the sensory process.

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 gain a deeper understanding of how sound sensors work through a hands-on design challenge involving LEGO MINDSTORMS(TM) NXT taskbots and sound sensors. Student groups each program a robot computer to use to the sound of hand claps to control the robot's movement. They learn programming skills and logic design in parallel. They experience how robots can take sensor input and use it to make decisions to move and turn, similar to the human sense of hearing. A PowerPoint® presentation and pre/post quizzes are provided.

Considers molecular control of neural specification, formation of neuronal connections, construction of neural systems, and the contributions of experience to shaping brain structure and function. Topics include: neural induction and pattern formation, cell lineage and fate determination, neuronal migration, axon guidance, synapse formation and stabilization, activity-dependent development and critical periods, development of behavior.

Students' understanding of how robotic ultrasonic sensors work is reinforced in a design challenge involving LEGO MINDSTORMS(TM) NXT robots and ultrasonic sensors. Student groups program their robots to move freely without bumping into obstacles (toy LEGO people). They practice and learn programming skills and logic design in parallel. They see how robots take input from ultrasonic sensors and use it to make decisions to move, resulting in behavior similar to the human sense of sight but through the use of sound sensors, more like echolocation. Students design-test-redesign-retest to achieve successful programs. A PowerPoint® presentation and pre/post quizzes are provided.

Students' understanding of how robotic light sensors work is reinforced in a design challenge involving LEGO MINDSTORMS(TM) NXT robots and light sensors. Working in pairs, students program LEGO robots to follow a flashlight as its light beam moves around. Students practice and learn programming skills and logic design in parallel. They see how robots take input from light sensors and use it to make decisions to move, similar to the human sense of sight. Students also see how they perform the steps of the engineering design process in the course of designing and testing to achieve a successful program. A PowerPoint® presentation and pre/post quizzes are provided.

" We are now at an unprecedented point in the field of neuroscience: We can watch the human brain in action as it sees, thinks, decides, reads, and remembers. Functional magnetic resonance imaging (fMRI) is the only method that enables us to monitor local neural activity in the normal human brain in a noninvasive fashion and with good spatial resolution. A large number of far-reaching and fundamental questions about the human mind and brain can now be answered using straightforward applications of this technology. This is particularly true in the area of high-level vision, the study of how we interpret and use visual information including object recognition, mental imagery, visual attention, perceptual awareness, visually guided action, and visual memory. The goals of this course are to help students become savvy and critical readers of the current neuroimaging literature, to understand the strengths and weaknesses of the technique, and to design their own cutting-edge, theoretically motivated studies. Students will read, present to the class, and critique recently published neuroimaging articles, as well as write detailed proposals for experiments of their own. Lectures will cover the theoretical background on some of the major areas in high-level vision, as well as an overview of what fMRI has taught us and can in future teach us about each of these topics. Lectures and discussions will also cover fMRI methods and experimental design. A prior course in statistics and at least one course in perception or cognition are required."

Students learn about the anatomy of the ear and how the ears work as a sound sensor. Ear anatomy parts and structures are explained in detail, as well as how sound is transmitted mechanically and then electrically through them to the brain. Students use LEGO® robots with sound sensors to measure sound intensities, learning how the NXT brick (computer) converts the intensity of sound measured by the sensor input into a number that transmits to a screen. They build on their experiences from the previous activities and establish a rich understanding of the sound sensor and its relationship to the TaskBot's computer.

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.

This lesson highlights the similarities between human sensors and their engineering counterparts. Taking this approach enables students to view the human body as a system, that is, from the perspective of an engineer. Humans have recreated most human sensors in robots – eyes, ears and sensors for temperature, touch and smell. The lesson inculdes a PowerPoint file that is programmed to run a Jeopardy-style game as a fun assessment tool.

Through six lesson/activity sets, students learn about the functioning of sensors, both human and robotic. In the activities, student groups use LEGO MINDSTORMS(TM) NXT robots and components to study human senses (sight, hearing, smell, taste, touch) in more detail than in previous units in the series. They also learn about the human made rotation, touch, sound, light and ultrasonic sensors. "Stimulus-sensor-coordinator-effector-response" pathways are used to describe the processes as well as similarities between human/animal and robotic equivalent sensory systems. The important concept of sensors converting/transducing signals is emphasized. Through assorted engineering design challenges, students program the LEGO robots to respond to input from various LEGO sensors. The overall framework reinforces the theme of the human body as a system with sensors that is, from an engineering perspective. PowerPoint® presentations, quizzes and worksheets are provided throughout the unit.

Students learn more about how light sensors work, reinforcing their similarities to the human sense of sight. They look at the light sensing process incoming light converted to electrical signals sent to the brain through the human eye anatomy as well as human-made electrical light sensors. A mini-activity, which uses LEGO MINDSTORMS(TM) NXT intelligent bricks and light sensors gives students a chance to investigate how light sensors function in preparation for the associated activity involving the light sensors and taskbots. A PowerPoint® presentation explains stimulus-to-response pathways, sensor fundamentals, and details about the LEGO light sensor, including its two modes of gathering data and what its numerical value readings mean. Students take pre/post quizzes and watch a short online video. This lesson and its associated activity enable students to gain a deeper understanding of how robots can take sensor input and use it to make decisions via programming.

Students learn about how sound sensors work, reinforcing their similarities to the human sense of hearing. They look at the hearing process sound waves converted to electrical signals sent to the brain through human ear anatomy as well as sound sensors. A mini-activity, which uses LEGO MINDSTORMS(TM) NXT intelligent bricks and sound sensors gives students a chance to experiment with the sound sensors in preparation for the associated activity involving the sound sensors and taskbots. A PowerPoint® presentation explains stimulus-to-response pathways, sensor fundamentals, the unit of decibels, and details about the LEGO sound sensor, including how readings are displayed and its three modes of programming sound input. Students take pre/post quizzes and watch a short online video. This lesson and its associated activity enable students to appreciate how robots can take sensor input and use it to make decisions to via programming.