Three brief statements on research
strategy to understand the mammalian brain
Included here are:
(1) “Adaptive and Intelligent Systems” – description of
a topic for funding within the field of engineering, which is crucial to
building up, enhancing and disseminating the kind of mathematics we need in
order to really understand how the brain works;
(2) “Cognitive Optimization and Prediction” –
description of a new crossdisciplinary topic for funding now in place at the
National Science Foundation; and
(3) Reverse-engineering
the brain – a response to this grand challenge, recently listed as one of
the 14 grand challenges in engineering by the National Academy of Engineering.
Item
(2) was copied directly from the official web site
of a major NSF funding opportunity. Items 1 and 3 are my personal views, and
not anything official. Item 3 was recently published in the newsletter
Autonomous Mental Development (AMD), the newsletter of the AMD task force of
the IEEE Computational Intelligence Society.
Adaptive and Intelligent Systems (AIS)
Examples of Subtopics: computational intelligence, neural networks, approximate
dynamic programming, machine learning, adaptive signal processing, pattern
recognition, data mining, on-chip learning, biologically inspired computing,
quantum learning, applications to testbeds in vehicles, robots, infrastructure,
energy, manufacturing and other.
Scope: Many research disciplines work to develop or use
general-purpose nonlinear algorithms for discovering patterns, making
predictions or making inferences from a stream of sensor data (or databases),
and for using such information to make decisions or to control engineering
systems or other plants. The Adaptive and Intelligent Systems (AIS) area
supports research in any of these disciplines, or combinations of disciplines,
which helps us make greater use of adaptation or
learning, or more distributed processing, in order to achieve greater
generality and less domain-dependence in such algorithms or architectures. The
ultimate goal is to develop the principles, subsystems and unifications which
will someday let us build an integrated all-purpose system which can “do it
all” – connect a huge set of diverse sensors to a huge set of actuators, using
a massively parallel set of simple underlying processing units, able to learn
how to manage complex systems in general in a near-optimal fashion without
major external help. More precisely, it should be able to replicate at least
that level of general-purpose higher intelligence that is found in the brains
of even the smallest mammals.
AIS does not expect any three-year research project to reach
this long-term goal, but it will give priority to proposals based on their
importance in bringing it closer to reality. Much AIS research involves general
adaptation or inference methods which can be applied to a wide range of
nonlinear function approximators, general enough to include neural networks as
an important special case. Some even develops new linear methods as part of a
progression well-planned to provide new general-purpose nonlinear capabilities
or to enhance the use of learning. About half addresses “value-free” tasks like
prediction, system identification, pattern recognition or state estimation.
About half addresses control or decision or optimization or reinforcement
learning; see www.eas.asu.edu/~nsfadp
for some examples. Proposals with titles beginning with “AIS:” will be
initially assigned to this area.
EMERGING FRONTIERS IN RESEARCH AND INNOVATION 2008 (EFRI-2008)
1. Cognitive Optimization and Prediction: From Neural Systems to Neurotechnology
(COPN)
2. Resilient and Sustainable Infrastructures (RESIN)
Preliminary Proposal Due Date(s) (required):
October 26, 2007
Full Proposal
Deadline(s) (due by 5 p.m.
proposer's local time):
April 30, 2008
Cognitive
Optimization and Prediction: From Neural Systems to Neurotechnology (COPN) -This EFRI topic provides partnership opportunities for
engineers and neuroscientists to address two goals.The first goal is to
understand how massively parallel circuits in brains address complex tasks in
adaptive optimal decision-making and prediction, and to understand the system
identification circuits in the brain which help make it possible.The second
goal is to use this understanding to develop new general-purpose designs or
algorithms for optimal decision-making over time, or prediction, or both,
powerful enough to work existing benchmark challenges in simulation or in
physical testbeds taken from engineering. This EFRI topic does not include all
aspects of cognitive science or cognitive engineering.The focus is on
subsymbolic intelligence, because it is a difficult and important prerequisite
to a deeper understanding of human intelligence. Engineers have unique
qualifications to address optimal decision-making and prediction over time
under uncertainty.A grand challenge is to develop new concepts of anticipatory
optimization that can cope with spatial complexity in time and nonconvexity as
required to improve probability of survival in nonlinear, stochastic
environments. Transformative benefits expected are: (1) to put science firmly
on the path to a truly functional, unified mathematical and systems
understanding of intelligence in the brain – an objective as important as the
search for unified models in physics; (2) new designs for optimal
decision-making which can handle complexity beyond the capacity of today’s
methods, as required for truly optimal rational management of complex
engineered systems; (3) improved performance in specified simulation testbeds;
and (4) development of new and more general ways to harness the potential power
of massively parallel “supercomputers on a chip."
II. PROGRAM DESCRIPTION
1. Cognitive Optimization and Prediction: From Neural
Systems to Neurotechnology (COPN)
Vertebrate brains, from fish to the smallest
rodents, show an amazing ability to learn how to maximize probability of
survival under all kinds of diverse, complex and unexpected circumstances. The
first goal of this topic is to mobilize systems engineers (as defined below) to
lead partnerships which help us understand how massively parallel circuits in
vertebrate brains can learn to address such complex tasks through adaptive
optimal decision-making, and through the prediction or system identification
circuits in the brain which help make it possible. The goal is to achieve the
kind of understanding which can be represented as new general-purpose designs
or algorithms for optimal decision-making over time, or prediction, or both,
powerful enough to work on existing benchmark challenges or testbeds taken from
engineering. Prediction in the brain includes and unifies capabilities such as
pattern recognition, state estimation and memory.
This topic does not include all aspects of
cognitive science or cognitive engineering. For example, it does not address
how mirror neurons, empathy and symbolic reasoning give human brains additional
hard-wired capabilities beyond the part which we inherit from the mouse. It
does not address phenomena like social or collective intelligence. This topic
focuses on subsymbolic intelligence and learning, because it is a difficult and
important prerequisite to a deeper understanding of human intelligence, and
because engineers have unique qualifications to address optimal decision-making
and prediction over time under uncertainty. Many engineers have focused on a
narrow, deterministic or even linear concept of optimization, which cannot
address challenges like maximizing a probability of survival in a nonlinear
environment, where it is impossible to prove a theorem that survival can be
guaranteed. The second goal of this topic is to stimulate and enlarge the
important emerging communities of engineers who work on truly stochastic
optimization and prediction, who use quality of service concepts to translate
reliability issues into an optimization problem, and who are finding ways to
use new massively parallel chips with teraflops on a chip.
This topic will fund new crossdisciplinary
teams, led by systems engineers, to reverse-engineer the brain’s
general-purpose domain-independent ability to converge towards optimal
decisions when confronted with spatial complexity, temporal complexity, or
challenges which require a high degree of creativity (nonconvex
optimization). All teams must include at least three types of expertise,
led by a systems engineer: (1) systems engineering, such as control theory,
artificial neural networks, signal processing, nonlinear dynamical systems or
operations research, with capabilities in stochastic optimization tasks
requiring foresight; (2) relevant neuroscience, such as cognitive systems,
computational neuroscience, or behavioral neuroscience; (3) device or systems
technology for studying the brain or systems of neurons (e.g., novel neural
recording technology, technology for probing systems of neurons on a chip or
intracellular sensing) or massively parallel neuromorphic chips like Cellular
Neural Network (CNN) chips.
All proposals must have the
potential to make significant contribution in both of two areas:
A. Progress towards understanding of learning in the
vertebrate brain.
The long-term goal is to move closer to a
truly functional, unified mathematical and systems understanding of learning in
the brain – a transformation as important in its way as the Newtonian revolution
was to physics. Proposals will not be expected to accomplish this revolution in
four years, but will be judged by how much they really bring us closer to it.
Thus all proposals must address empirical data from
neuroscience areas, and include co-PIs who have participated in projects
collecting such data.
Proposals need not include the development of
new devices for recording from neural systems. If they do, however, they will
be judged on the basis of the new knowledge which may result from using these
devices as part of the project; device development for its own sake will not be
funded. No projects will be funded which include development or use of neural
implants to stimulate living whole brains.
B. Progress towards handling spatial or temporal complexity
in technology, or replicating brain-like creativity.
The long-term goal is to develop
general-purpose engineering designs capable of learning to perform optimization
or prediction, using massively parallel computing hardware, in the face of
complexity, nonconvexity and nonlinearity so severe that absolute guarantees of
stability are impossible. Projects will be judged on the basis of whether they
effectively address the challenge of getting us to that goal as soon as
possible. Thus all projects must include the use of a difficult benchmark
challenge for technology. This could be a challenge in simulation or in real
hardware, but the benchmarking and the level of difficulty are what really test
the relevance of technology. Even such testbeds as the game of Go would be
acceptable, so long as the models to be tested are both general and linked to
biological testing.
There are many new and emerging opportunities
to bring engineering and neuroscience together in the bold way that is called
for by this topic; here are just a few examples:
·
Reverse-engineering
the circuits which learn to predict in the thalamo-cortical systems. This would
build on work on the barrel system of rats, for which it has been shown that
certain thalamic cells predict other cells and relearn to predict when the
initial circuit is disrupted. Millisecond-level multielectrode data might show
waves of information during calculation and adaptation similar to those in
artificial nonlinear estimation systems, drawing on systems concepts such as
time-lagged recurrent networks, extended Kalman filtering, real-time least
squares and seasonal adjustment, and gating based on encoder/decoder designs.
(Seasonal adjustment addresses the presence of multiple lags, such as 100
millisecond delays induced by the alpha rhythm pacemaker cells, and local codes
based on the strengths of bursts at discrete time intervals.)
·
Reverse-engineering
cultures/circuits of multiple types of neurons on a chip, where two-way
chemical and electrical stimulation and real-time imaging or sending is
possible. A key question is how to “persuade” such cultures to learn to address
a menu of optimization or prediction tasks provided by an engineer. Controlled
experiments in this venue may allow rapid progress in understanding the capabilities
of various types of neurons. Progress in replicating capabilities similar to
those of an entire brain may help us understand what kinds of cells, conditions
and connections are necessary to achieve (and enhance) such capabilities.
·
Development of a
new system which uses new biologically-grounded models of learning to train a
CNN chip to learn the non-Euclidean image transformations essential to improved
performance on face-recognition tasks. Research on biological face recognition
by humans, other mammals or birds could play a crucial role here, and help
elucidate the role of symmetry properties and attention to objects in coping
with spatial complexity.
·
Understanding how
phenomena like chemically-encoded memory can interact with
larger-scale prediction circuits, in order to make the multilevel system
perform so well in difficult prediction tasks. Just as the equations of
the Kalman filter are a general-purpose system for estimation and
prediction of linear, stochastic systems, the cerebro-thalamic system appears
to include a general-purpose ability to learn to predict the entire “input
vector” of sensory information registered in the thalamus, “the movie screen of
the upper brain."
Please pay special attention to the
additional review criteria in Section VI.A.
Reverse
Engineering the Brain: A Proposed New Thrust
On February 19, 2008, the
National Academy of Engineering listed fourteen grand challenges for the coming
century, including: “Reverse-engineering the brain, to determine how it
performs it magic, should offer the dual benefits of helping treat diseases
while providing clues for new approaches to computerized artificial
intelligence.”
(1) Specific Goals for Engineering
1a. New
methods for more accurate adaptive, anticipatory optimization in controlling
systems of ever
greater
complexity in space and in time, with additional “creativity” mechanisms to
anticipate
future
possibilities and escape from local minima. Such adaptive systems may start from management systems designed
by humans for the specific systems being controlled; however, in this topic,
the challenge is to build adaptive systems which can improve upon such initial
starting points, and adapt to new circumstances which the human designer did
not specifically anticipate.
1b. New
methods for designing general-purpose domain-independent systems which learn to predict more
and more
accurately, in the face of nonlinearity,
random disturbance, unobserved variables, and ever greater complexity in space
and in time.
1c. A new paradigm of resilient control, where one minimizes the probability of undesired events, under
conditions
so complex and challenging that guaranteed survival is impossible under
realistic assumptions.
1d. Methods, algorithms and
architectures which fit with the most important constraint which computations
in the
brain must live with: massively parallel computation across a large number of
(mostly) sparsely connected processing units. This can vastly increase our
ability to harness the potential
benefits
of massively parallel “supercomputers on a chip,” such as Cellular Neural
Network chips. The use of general-purpose learning to better harness such new
capabilities is part of this topic.
1e.
Contribution to NSF’s mission in basic science. Arguably the most important single goal here is to
demonstrate
what engineers can contribute to enhancing our understanding of the brain and
of the mind, above and beyond what other disciplines are now able to do. New
engineering technology for studying the brain is part of this, although
neuroscientists already look for new technology. New systems level models and
methods, and true reverse engineering of the mammalian brain, is the biggest
unmet opportunity of importance to humanity.
1f. Better performance in
some selected testbeds used for this effort, such as control of electric power
infrastructures, face recognition, video processing, and others.
(2) Role of Engineering in the Broader Context of NSF
Adaptive Systems Theory
People have talked about understanding how intelligence
emerges in the brain, and building artificial intelligence, for decades and
decades. For example, in the 1960’s, one of the two leading centers on
artificial intelligence promised NASA that they would deliver a full-fledged artificial
human brain, based on linguistics and symbolic reasoning, in a robot ready to
be sent to Mars in the 1980’s. Likewise, in the past, many neuroscientists
suggested that we could reach an understanding of intelligence and learning in
the brain by first doing exhaustive probes of simple organisms like snails
(aplysia), and extracting the mathematical principles; however, current
research on such organisms suggests that much of their behavior is indeed
genetically programmed, and is not so helpful in understanding how we can learn
new things above and beyond what is already coded into our genes. Yet another
stream of research has stressed that the human mind has an almost magical
ability to understand things beyond the scope of conventional computing – and
then suggested that we follow up by building “computers to compute the
noncomputable.”
Engineering today has made enough progress to allow us to
construct a fourth pathway of research, which can allow us to bypass the
obstacles which have limited the accomplishments of the old paths. The old
paths still have much to contribute, but something new is needed, to achieve a
real functional understanding of intelligence in the brain. In order to meet
this new opportunity,
we will need to focus on the unique new directions in engineering
which make it possible, and to focus on ways to exploit these new directions.
It is necessary to build strong partnerships with empirical neuroscience
(especially systems neuroscience)
and psychology, without falling into the limitations of traditional research in
those fields.
The first key goal on this new path is to focus on trying
to understand learning-based general
intelligence, as it exists in brains ranging from the crudest fish to the
smallest mammal. While others focus on snails or try to build an
“artificial Einstein,” the new opportunity lies at this middle level. Symbolic
reasoning and empathy (“mirror neurons”) have given great power to the human
brain, not shared by these lower organisms. Yet before we can really understand
the “deep structure” which underlies language and semantics, we must first
understand the functional wiring and learning which human brains and mouse
brains share. The six-layer neocortex of the human brain is almost
indistinguishable from that of the mouse. Different topographic regions in the
human brain usually contain different knowledge from what mouse brains contain,
but human brains differ from each other, and can change with time; the famous
“edge detectors” hard-wired into certain regions are easily relearned in other
parts of the brain, when the system is damaged, if the wiring from thalamus to
neocortex is not cut.
The next key element on this new path is to focus on cognitive optimization and prediction.
General intelligence is not the ability to do well in a narrow specific task,
like playing a good game of chess. Computer scientists have shown how that kind
of narrow focus is a dead-end, when it comes to learning about intelligence in
general. On other hand, the task of adaptive,
anticipatory optimization in the face of a large, unknown, nonlinear
stochastic environment is of general importance. It is a task where engineers
have led the development of new types of mathematical design, which can handle
far more complexity than traditional forms of reinforcement learning which have
been popularized in computer science. The most
important research questions here are: (1) how can we deepen our
understanding of the mathematical principles of adaptive, anticipatory
optimization and of prediction, so as to cope with the kind of complexity in
time and in space which the brain can handle?; (2) how can we insert more
brain-like approaches into engineering technology?; (3) how can we unravel the
circuits and methods which the brain uses to learn to predict or to optimize
better, in a way which transcends specific testbeds for prediction or
optimization? How can the smallest mouse learn to maximize its probability of survival?
As an example, Nicolelis and Chapin showed in the 1990’s
that certain cells in the thalamus predict the sensor inputs that are received
by other cells, and relearn to
predict well after damage to the system. How does this finding apply across all
sensory inputs, and how does the brain learn this?
In general, the goal here is not to predict
brains in a “behaviorist”
or “positivist” way, as if they were like a passive crystal of ice; rather it
is to understand the general functional
capabilities of brains, so that we can better understand, work with and
enhance the intelligence possessed even by the smallest mouse. How do brains
themselves learn to predict better and better with time?
(3) Some Specific Themes.
Many, many disciplines have approached these questions from
different perspectives. A one or two page statement of goals cannot do justice
to the important details of ideas and strategies and mathematical principles
which will be essential to success in this venture. The EFRI announcement on
Cognitive Optimization and Prediction fills in some of these details, as do a
number of other sources of the web (such as workshop reports or papers for
workshops). Certainly the improvement of dialogue between disciplines will be a
crucial factor in determining how much we actually accomplish.