Redwood Neuroscience Institute
pkanerva . . . hopefully
@csli.stanford . . . this will
.edu . . . discourage spam
The organization of the brain in large circuits of neurons is
compelling. To a computer engineer it means but one thing: the
circuits are there to accomplish computation. My research is aimed
at understanding the nature of this computation and eventually
finding out how our brains make us what we are.
Present-day computers are a pale model of the brain's computing.
Sensory input to the brain and the brain's circuits are far more
complex than those of computers, the architecture is not specified
in minute detail as it is in computers, and the components are
unreliable. The most remarkable differences, however, are in the
behaviors that brains and computers produce. These conspicuous
differences reflect fundamental differences in the internal form of
information and operations on it, which is what we need to understand.
Brains are a product of evolution and should be studied in that context.
They help individuals and species to survive and prosper in the world,
meaning that they produce beneficial action. They do it by predicting
events in the world, including consequences of their own actions.
The brain's computing is designed for interaction.
In their own way brains learn to model their interaction with the
world. They convert and integrate sensory signals and motor
commands into common internal form, a kind of ``universal code,''
and employ learning mechanisms that are shared by different
sensory modalities. My research is concerned with this encoding
and integration of information into a predictive model of the world:
What are the neural algorithms of memory and learning and how do
they capture statistical and logical regularities in the signal available
to the brain? How do brains find meaning it what they sense?
I use random distributed representation in spaces with thousands of
dimensions---i.e., population coding and computing with large, seemingly
random patterns---to model the brain's code and computing. Reasons
for this are several. The brain's circuits are large with no individual
neuron being critical to their operation (neurons can die), even the
simplest mental events involve the activity of thousands of neurons,
distributed representation is lifelike and tolerant of component failure,
and randomizing leads to general algorithms that do not depend on
precise architecture. The simplest models of neural circuits are based
on high-dimensional vectors---that is, on points of a high-dimensional
space, or large patterns, with the dimensionality in the thousands.
Although not immediately obvious, spaces with thousands of
dimensions have rich and subtle mathematical properties on which
to base computation. For example, high-dimensional representation
makes a system tolerant of ``errors''---it makes it possible for us to
recognize people and objects even when conditions vary. For another
example, the human mind works by analogy. The mapping of points in
a high-dimensional space could be its underlying mechanism, where the
mapping functions themselves are represented by points of the same
space. Although dimensionality in the range of 10--100 may well be
a curse (referred to as ``the curse of dimensionality''), when it's in the
thousands it is truly a blessing.
European Academy of Sciences
Cognitive Science Society
International Neural Network Society
Tampere University of Technology, 1990.
CSLI, Stanford University, 1994--
Co-chair, AAAI Symposium on Acquiring and Using Linguistic
and World Knowledge for Information Access, March 2002.
Publications: One book, two book chapters, and 25 papers on
distributed representation and memory.
Ph.D. Philosophy, Stanford University, 1984.
M.S. Forestry, minor in mathematics and statistics,
University of Helsinki, 1964.
A.A. Warren Wilson College, North Carolina, 1956.
2002--03 Senior Researcher, Redwood Neuroscience Institute.
1993--2002 Senior Researcher, Swedish Institute of Computer
1985--92 Senior Scientist, Research Institute for Advanced
Computer Science (RIACS), NASA Ames Research Center.
1984--85 Postdoctoral Fellow, Center for the Study of Language
and Information (CSLI), Stanford University.
1979--83 Computer Systems Specialist, Center for Information
Technology, Stanford University.
1967--78 Systems Programmer (1967--68, 1977--78) and Research
Assistant (1968--77), Institute for Mathematical Studies
in the Social Sciences (IMSSS), Stanford University.
1965--67 Chief of Computer Center, University of Tampere, Finland.
1963--65 Programmer--Analyst and Section Leader, Finnish State
1961--63 Statistician, part-time, Forest Research Institute of