Download Reverse-engineer the brain - University of Missouri

COMPUTATIONAL NEUROSCIENCE - ECE Undergraduate Elective Track
THE NATIONAL ACADEMY OF ENGINEERING (NAE) HAS IDENTIFIED 14 GRAND CHALLENGES FOR
ENGINEERS, ONE OF WHICH IS REVERSE-ENGINEER THE BRAIN.
FOR MORE DETAILS VISIT THE SITE http://www.engineeringchallenges.org/cms/8996.aspx
…… SEVERAL ELECTRICAL & COMPUTER ENGINEERING DEPTS AROUND THE NATION ARE
BEGINNING PROGRAMS TO ADDRESS THIS GRAND CHALLENGE, AND MU ECE IS TOO!
The following text is from the NAE website above listing the Engineering Grand Challenges:
Reverse-engineer the brain
The intersection of engineering and neuroscience promises great advances in health care,
manufacturing, and communication.
For decades, some of engineering’s best minds have focused their thinking skills on how
to create thinking machines — computers capable of emulating human intelligence.
Why should you reverse-engineer the brain?
While some of thinking machines have mastered specific narrow skills — playing chess,
for instance — general-purpose artificial intelligence (AI) has remained elusive.
Part of the problem, some experts now believe, is that artificial brains have been designed
without much attention to real ones. Pioneers of artificial intelligence approached thinking
the way that aeronautical engineers approached flying without much learning from birds. It
has turned out, though, that the secrets about how living brains work may offer the best
guide to engineering the artificial variety. Discovering those secrets by reverseengineering the brain promises enormous opportunities for reproducing intelligence the way assembly lines spit
out cars or computers.
Figuring out how the brain works will offer rewards beyond building smarter computers. Advances gained from
studying the brain may in return pay dividends for the brain itself. Understanding its methods will enable engineers
to simulate its activities, leading to deeper insights about how and why the brain works and fails. Such simulations
will offer more precise methods for testing potential biotechnology solutions to brain disorders, such as drugs or
neural implants. Neurological disorders may someday be circumvented by technological innovations that allow
wiring of new materials into our bodies to do the jobs of lost or damaged nerve cells. Implanted electronic devices
could help victims of dementia to remember, blind people to see, and crippled people to walk.
Sophisticated computer simulations could also be used in many other applications. Simulating the interactions of
proteins in cells would be a novel way of designing and testing drugs, for instance. And simulation capacity will be
helpful beyond biology, perhaps in forecasting the impact of earthquakes in ways that would help guide evacuation
and recovery plans.
Much of this power to simulate reality effectively will come from increased computing capability rooted in the
reverse-engineering of the brain. Learning from how the brain itself learns, researchers will likely improve
knowledge of how to design computing devices that process multiple streams of information in parallel, rather than
the one-step-at-a-time approach of the basic PC. Another feature of real brains is the vast connectivity of nerve
cells, the biological equivalent of computer signaling switches. While nerve cells typically form tens of thousands of
connections with their neighbors, traditional computer switches typically possess only two or three. AI systems
attempting to replicate human abilities, such as vision, are now being developed with more, and more complex,
connections.
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What are the applications for this information?
Already, some applications using artificial intelligence have benefited from simulations based on brain reverseengineering. Examples include AI algorithms used in speech recognition and in machine vision systems in
automated factories. More advanced AI software should in the future be able to guide devices that can enter the
body to perform medical diagnoses and treatments.
Of potentially even greater impact on human health and well-being is the use of new AI insights for repairing
broken brains. Damage from injury or disease to the hippocampus, a brain structure important for learning and
memory, can disrupt the proper electrical signaling between nerve cells that is needed for forming and recalling
memories. With knowledge of the proper signaling patterns in healthy brains, engineers have begun to design
computer chips that mimic the brain’s own communication skills. Such chips could be useful in cases where
healthy brain tissue is starved for information because of the barrier imposed by damaged tissue. In principle,
signals from the healthy tissue could be recorded by an implantable chip, which would then generate new signals
to bypass the damage. Such an electronic alternate signaling route could help restore normal memory skills to an
impaired brain that otherwise could not form them.
―Neural prostheses‖ have already been put to use in the form of cochlear implants to treat hearing loss and
stimulating electrodes to treat Parkinson’s disease. Progress has also been made in developing ―artificial retinas,‖
light-sensitive chips that could help restore vision.
Even more ambitious programs are underway for systems to control artificial limbs. Engineers envision
computerized implants capable of receiving the signals from thousands of the brain’s nerve cells and then
wirelessly transmitting that information to an interface device that would decode the brain’s intentions. The
interface could then send signals to an artificial limb, or even directly to nerves and muscles, giving directions for
implementing the desired movements.
Other research has explored, with some success, implants that could literally read the thoughts of immobilized
patients and signal an external computer, giving people unable to speak or even move a way to communicate with
the outside world.
What is needed to reverse-engineer the brain?
The progress so far is impressive. But to fully realize the brain’s potential to teach us how to make machines learn
and think, further advances are needed in the technology for understanding the brain in the first place. Modern
noninvasive methods for simultaneously measuring the activity of many brain cells have provided a major boost in
that direction, but details of the brain’s secret communication code remain to be deciphered. Nerve cells
communicate by firing electrical pulses that release small molecules called neurotransmitters, chemical
messengers that hop from one nerve cell to a neighbor, inducing the neighbor to fire a signal of its own (or, in
some cases, inhibiting the neighbor from sending signals). Because each nerve cell receives messages from tens
of thousands of others, and circuits of nerve cells link up in complex networks, it is extremely difficult to completely
trace the signaling pathways.
Furthermore, the code itself is complex — nerve cells fire at different rates, depending on the sum of incoming
messages. Sometimes the signaling is generated in rapid-fire bursts; sometimes it is more leisurely. And much of
mental function seems based on the firing of multiple nerve cells around the brain in synchrony. Teasing out and
analyzing all the complexities of nerve cell signals, their dynamics, pathways, and feedback loops, presents a
major challenge.
Today’s computers have electronic logic gates that are either on or off, but if engineers could replicate neurons’
ability to assume various levels of excitation, they could create much more powerful computing machines. Success
toward fully understanding brain activity will, in any case, open new avenues for deeper understanding of the basis
for intelligence and even consciousness, no doubt providing engineers with insight into even grander
accomplishments for enhancing the joy of living.
References
Berger, T.W., et al. ―Restoring Lost Cognitive Function,‖ IEEE Engineering in Medicine and Biology Magazine
(September/October 2005), pp. 30-44.
Griffith, A. 2007. ―Chipping In,‖ Scientific American (February 2007), pp. 18-20.
Handelman, S. ―The Memory Hacker,‖ Popular Science (2005).
Hapgood, F. ―Reverse-Engineering the Brain,‖ Technology Review (July 11, 2006).
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Lebedev, M.A. and Miguel A.L. Nicolelis. ―Brain-machine interfaces: Past, present, and future,‖ Trends in Neurosciences 29
(September 2006), pp. 536-546.
Info. gleaned from several internet sources:
MIT – EECS Dept
Computational neuroscience focuses on computational models of brain activity and development, using a
wide variety of measurement techniques, from cognitive experiments, to EEG, MEG and MRI. Research in
this area builds heavily on control theory and signal processing. Probability and machine learning often prove
useful in modeling noisy biomedical measurements. The courses in this area introduce computational
models for various brain functions, from motor control to knowledge representation and cognition.
A Computational Approach to Neurobiology
The Center for Theoretical Neuroscience was established at Columbia University in New York City in 2004 as part of
the Swartz Program in Theoretical Neuroscience. In addition to the faculty listed within, we have additional long-term
visitors, research staff and faculty. We are excited to bring our varied theoretical approaches together (please click on
the "people" link to learn more about the work we each do). We interact closely with the many experimental
neuroscientists here at Columbia and elsewhere in New York City and also with other theoretical biologists here. We
believe we have one of the most exciting and interactive environments anywhere for bringing theoretical approaches
to Neuroscience.
Cognitive, Computational and Systems Neuroscience Curriculum Pathway
Recent years have seen a blurring of the traditional lines between brain-related research in psychology,
biology and engineering. To train the next generation of top-flight brain scientists, Washington University has
developed an integrated curriculum that provides graduate students with the training and resources to
become leaders in this new interdisciplinary science.
Princeton EECS offers a certificate program in Neuroscience
MU ECE has both an undergraduate elective track in Computational Neuroscience,
and also offers an undergraduate Minor in Computational Neuroscience – see
following pages for details
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OPEN TO MU ENGINEERING UNDERGRADS FROM ALL DEPTS - SOPHOMORES to SENIORS
ECE 4580 COMPUTATIONAL NEUROSCIENCE
(cross-listed with BioSci 4580)
Timings:
MWF 1:00-1:50 p.m. (lecture)
W
1:00-3:50 p.m. (lab)
Engrg Bldg West 145
Tucker Hall 403
Instructors: Satish Nair (Engineering) and David Schulz (Biol. Sciences)
Course credit: 4 hours [Three 1-hr lectures + one 2-hr hands-on lab]
Class-size: 12 students (limited by number of experimental setups)
Pre-requisites: Engineering students contact S. Nair, [email protected]; 882-2964; 229 EBW
Text: From Molecules to Networks – An Introduction to Cellular and Molecular Neuroscience, by Byrnes and
Roberts, Elsevier, 2004 (Required) + Notes hosted at website, incl. GENESIS Software Manual
Assessment, Grading: Grades will be based on (i) two exams (midterm and final), (ii) student participation
during hands-on and recitation sessions, and (iii) written lab-reports.
Short course description: How does the firing of neurons translate to behavior….from the generation of heart
and stomach muscle rythms to implementing mating call recognition in insects?
Computational Neuroscience is an emerging interdisciplinary field that links the diverse fields of neurobiology and
quantitative sciences. This interdisciplinary course, which is team-taught by faculty from biology and engineering, will
introduce this exciting and growing field. We will explore basic concepts of neurobiology at the cellular and network level
and study their representation in computational models. In the laboratory component, students will use state-of-the-art
equipment to record neuronal activity at various levels. In the modeling component, students will develop neuron models
to explain and expand upon the observed biological concepts using the modeling software GENESIS. Students will work
in interdisciplinary teams of biology and engineering students. The course will provide a basic understanding of how
behavior is generated by nervous systems, i.e., neurons to behavior.
NEURONS TO BEHAVIOR…….VIA MODELS
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Undergraduate Elective Track
Electrical and Computer Engineering, MU
Track Title:
Computational Neuroscience
Faculty Point of Contact:
Satish S. Nair
(include contact information)
Courses and Credit Hours
Course Title
Course No.
Dept
Cr. Hrs.
Feedback Control Systems
4310
ECE
4
Computational Neuroscience
4580
ECE
4
Bioelectricity
4070
BIOL EN
3
Or any one course from the list below:
Notes
You can select any one course from the following list to replace BIOLEN 4070 if needed (for a total of three
courses for the track): BIOL 2300 Cell biology (4); BIOL 4310 – Physics in Cell and Developmental Biology
(3); BIOL 4500 – Neurobiology (3); BIOL 4560 – Sensory Physiology and Behavior (3); BIOL 4986 –
Neurology of Motor Systems (3); BIOL 4988 – Nerve Cells and Behavior (3)
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NEW INTERDISCIPLINARY MINOR IN COMPUTATIONAL NEUROSCIENCE
POSSIBLE COURSE CLUSTERS
Students have to take courses from each of the sections A (two), B (two), and C (required) below.
A. 1. Biology ‘core’ for Engineering/Physics students
(Engrg/Physics/Math/Psych students take six credits from the following)
 Either BIOL 1010+1020 – Gen. Biology w/lab for non-majors or FW1100 – Intro. Zoology w/ lab
(both are 5 cr hr courses)
 BIOL 1500 Introduction to Biological Systems (5 cr hr)
 BIOL 2300 Cell biology (3 cr hr)
 One of either PSYCH 2210 – Mind, brain, behavior, or PSYCH 4210 – Physiological Psychology (both are 3 cr hr).
 Seminar for undergraduates (1 cr hr)
A.2.
Engineering/Physics/Math ‘core’ for Biology students
(Bio students take six credits from this group)
 PHYSICS 1220 – College Physics II (4 cr hr)
 CS 1040 Introduction to Problem Solving and Programming (for non-majors; 3 cr hr)
 BE 2080 – Programming for Engineers
 Higher level BioENG or CS courses can also be taken, with consent
______________________________
B.
OTHER COURSES (students take two from any of the section B courses)
Bio courses:
BIOL 3700 – Animal Physiology
BIOL 4310 – Physics in Cell and Developmental Biology
BIOL 4500 – Neurobiology
BIOL 4560 – Sensory Physiology and Behavior
BIOL 4986 – Neurology of Motor Systems
BIOL 4988 – Nerve Cells and Behavior
Engineering/Physics/Math courses:
BIOENG 4070 – Bioelectricity
BIOENG 4080 – Engineering Computations
PHYSICS 4310 – Physics in Cell and Developmental Biology
PHYSICS 4500 – Computational Biological Physics
______________________________
C. REQUIREMENT: BioSci/ECE 4580 Computational Neuroscience
Campus units that would be interested: Div of Biological Sciences, Dept. of Psychological Sciences, All depts.. in
Engineering, Dept. of Physics, Dept. of Mathematics, Dept. of Statistics
Contacts: Satish Nair (Engineering) and David Schulz (Biology)
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