Teaching Statement:
The value of a residential education must be based on experiences that cannot be delivered
online - training that focusses on process, and knowledge that cannot be delivered by a search
engine. In my teaching I am focussed on providing students with opportunities to do independent
creative work. I have developed and taught three courses at Boston University that succeed to
varying degrees in this aim.

BI502 - Topics in the theory of Biological Networks
This course examines mathematical principles underlying the activity of various biological
networks. The subject matter ranges from abstract boolean networks to bacterial chemotaxis,
systems neuroscience, and ecosystems. We examine how interesting dynamical properties arise
from simple rules and the geometry of network connections. We focus particularly on the
properties of adaptive networks networks whose geometry changes as a result of the activity of
the network. The final projects in this course are computer programs written in matlab, with an
accompanying presentation.

BI/NE 444/ 644 - Neuroethology
Neuroethology is the study of neural systems in organisms performing natural behaviors. Neural
systems are governed by general principles, but the best way to uncover these principles often
comes down to the art of choosing the right organism to study. Songbirds are champions of vocal
learning, rodents are champions of spatial navigation, owls are champions of binaural hearing,
and bats are champions at auditory sensory-motor integration. Each of these example organisms
provides an opportunity to examine general questions about neural systems in a uniquely
tractable manner. This course begins by examining the historical roots of ethology. Early
ethologists recognized that studies of animal behavior in artificial settings would often fail to
uncover essential principles of their behavior. In a series of now famous experiments, the
founders of ethology revealed simple principles underlying various natural behaviors in a wide
range of species. Following this introduction, we step back to examine the challenges faced by
neural systems in a complex world. These challenges include imperfect sensors, noisy
environments, and many degrees of freedom in motor control. We examine in depth a few animal
models that have provided unique insights into general principles of neural systems, from honey
bees to bats, mice and songbirds. Interspersed throughout the course, visiting lectures are given
by researchers from the Boston area. A three hour weekly laboratory examines neural recordings
in singing birds.
BI/NE 449/ 649: Neuroscience Design Lab.

Neuroscience design lab provides real-world experience in creative problem solving. In this
class, students design and build devices for neuroscience experiments. They interface sensors
with computers using Arduino microprocessors, and learn to program these devices. Guided
exercises focus on learning elementary skills, and taking simple sensory and motor
measurements in humans. These early exercises provide the foundation for the major work of
this class - independent design projects that quantify human sensory and motor performance, or
emulate animal sensory-motor circuits.
The class builds on a rich ecosystem of online material that has sprung up around hobbyists that
use Arduino microprocessors to build simple devices. The Arduino provides a fantastic
opportunity to practice problem solving skills. Students are encouraged to tinker, play, make
mistakes, struggle and learn to marshall the resources to solve problems. The key home page for
this community is a detailed tutorial (http://arduino.cc/en/Tutorial/HomePage). At the start of
class, students are given Arduino kits with circuit boards, wires, sensors, lights, buttons, etc.
Students are encouraged to take these home and practice. Students are also encouraged to
exchange information freely inside and outside of class and also access online support
communities for help in problem solving (http://arduino.cc/forum/)

Of these three classes, Neuroscience Design Lab is the most successful, and best resembles my
vision for teaching. Most of the students are quite uncomfortable with the class at first. There is
no step by step manual for how to get an A in the class. Students understand that expectations are
high, and are given examples of the great projects completed in previous years. The students
undergo a transformation over the course of the semester…often waiting to be spoon fed at first,
they soon take ownership of a project and delight in the learning process.
I work with each student develop an individualized project matched to their interests and
abilities, and also set up individualized learning plans that tailor a progression of exercises to
each student’s needs. The class is highly collaborative and the more advanced students engage in
helping others that are struggling. Creative steps are brought to the attention of the whole class,
and it is clear that students delight in showing of their inventions, and in helping each other move
forward. This collaborative environment is what makes the class possible. Most students have
little or no programming experience prior to starting the class, and the kind of information
exchange that occurs between students promotes rapid problem solving.
For some, the class is transformative, and a number of students have reported career changes in
the direction of science and engineering after taking the class. Students often put in many hours
of work beyond the six hours of scheduled lab time per week, and most leave the class with a
real sense of accomplishment. I think that learning to design scientific instrumentation through
Arduinos can be an engaging, scalable module for many science courses.
In the words of one student:
“Neuroscience Design Lab has taught me many things that I feel are not offered in any other class in
CAS. Instead of being a structured lab where every step is written down to follow, it allows students to
design their own experiments, more closely resembling real world lab work. This lab engenders creativity
and innovation that is otherwise not seen in any lab classes, and has spawned very creative ideas from
students who set out to solve a particular problem.
Being able to construct experimental devices with the Arduino empowers the student to adopt a more 'doit-
yourself' strategy when designing experiments, rather than relying on expensive equipment that might
not be the most fitting tool for their experimental design. This gives students more options for what they
want to learn about in this lab, and make it fit more to their own interests. If students are more interested
in what they are learning about, they will commit more time into what they are learning, and they will
learn it more thoroughly.
Winthrop Gillis - Fall 2013