Welcome to Applied Neurodynamics
"The 20-20 MINDSITE"
(Neural Network Systems Engineering and Consciousness Studies)
Web page for Stephen Deiss, Consultant and Explorer
[last updated July 10, 2010]
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Main Menu
Essays on Mind (all about my big TOE2):
? Neurodynamics ?
? Applied Neurodynamics
?
? What For ?
Services:
Accomplishments:
Pubs, TRs, and References:
Related Web Sites:
On a Personal Note (with resume’ link):
Essays on
Mind - Steve's big TOE2 and GUT
…toward a Theory of Everything Else - a
different kind of Grand Unified Theory
In order to understand where consciousness fits into nature and how science can deal with it, it requires some philosophical sophistication. Consciousness, if anything, is “nothing but” hard to get a handle on. In the essays below I have been creeping up on a clarification of what consciousness is and where it fits into nature. This is a naturalist view. The “UNCC” draft below is the most succinct exposition so far. The “What It All Means” draft is the most complete (but rambling treatment). The ‘Zombie Blues’ poster is a succinct and humorous summary. The others represent milestones in my thinking along the way going back almost 40 years.
All these ideas will be in a book eventually.
(Copyright 1968-2007, Stephen R. Deiss, all rights reserved)
Beautiful
Minds: Time and Consciousness in NonDual Physics, SAND 2011 Talk
Can there be a scientific
explanation of consciousness without qualia, 2010 ASSC Poster
The Hard Problem
and a Possible Solution, 2010 TSSC Poster
UNCC: The Cure
for Chronic 'Zombie Blues,' 2007 ASSC Poster
UNCC: The
Universal Correlates of Consciousness, 2006, Work in Progress
What It All Means,
2004-6, Work in Progress
Laws of
Nature and Consciousness Poster, 2006
Awareness of
Meaning and Understanding in the Brain Poster, 2005
Feeling of
Understanding Poster, 2005
Good Ideas Feel Good (graduate essay)
Free Will through Determinism (undergraduate
work on FW summarized)
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Neurodynamics
The word
“Neurodynamics” is a term used here in my consulting firm name (Applied
Neurodynamics) to represent a conceptual and eclectic methodological approach
to understanding neural network activity, and to use this perspective to bridge
from neuroscience to cognitive science, conscious experience and
behavior. Conventional neural network architectures are often simplistic
feed forward or recurrent models where the timing of events is not important to
the processing being done. Dynamics studies causal systems where timing
is a key consideration [1, 19]. Dynamics underlies all “computation”
which is the preeminent (and overworked) paradigm in all areas of science today
[31].
Other inspiration
comes from Hebb's visionary notion in 1949 of reverberating cell assemblies
and the many modern reinterpretations now being used to understand perception [18]. Most important is the potential to extend this idea
to context dependent sequential activation of neural ensembles to account
for serial order in thought and action [7].
There is mounting evidence that brains do use population codes that are
sensitive to temporal relationships on many time scales [ 14,
16, 30, 35].
The conclusion to
use this approach arose from a personal inquiry into the mind-body problem and
excursions into related areas in Psychology, Neuroscience, Computer Science,
and the System Sciences including Nonlinear Dynamics (see the section called On a Personal Note) over the last 35 years.
Applied
Neurodynamics is a small business that from 1988 to about 2004 specialized in
the design of and infrastructural support for electronic embodiments of this
kind of information processing architecture and related neurocomputing
systems. As the Owner-Founder and Chief Engineer (janitor too) I take
great pride in just having lasted this long in a rather research oriented
industry [see Accomplishments]. I am in
for the long haul and mostly interested in the hard problems. While I try to keep a hand in for my
company, it has taken a back burner while I work in Neuromorphic Engineering at
UCSD in the Neurobiology Dept.
In my ongoing
personal research I have tackled the problem of the place of consciousness in
nature. Being against supernatural
explanations and frustrated with most of the confusing explanations coming out
of modern physics, neuroscience and philosophy, I have developed my own novel
view which you can find in draft form elsewhere on this web page. It takes a lot of GUTs
to propose an explanation of consciousness. I nevertheless have joined the fray with a belly flop. We
will see what kind of splash it makes.
It will soon be coming out as a chapter in a forthcoming book on
Panpsychism edited by David Skrbina [ 56 ] and presented again at the 2008
conference Towards a Science of Consciousness in Tucson in April.
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Applied Neurodynamics
...is a
consulting firm I ran that supported the detailed investigation of the behavior
of neural assemblies with biologically plausible dynamics. Such tools
will make it possible to explore on a modest scale how the temporal patterns of
activity among neurons form codes and sequences that can represent percepts,
concepts, and action oriented decisions. One example is the Silicon Cortex Board
[28, 29]. I have now moved on to UC San Deigo in the Neurobiology
Section (Cauwenberg’hs Lab for Neuromorphic Engineering known locally as the
Integrated Systems Neuroscience Lab).
Experience gained
over these many years is helping to point the way toward how to scale such a
system architecture to handle difficult problems related to motor behavior,
speech, language, vision, audition, and reasoning. A key problem turns
out to be how to engineer systems that can represent communication among
neurons and how they collectively encode and recode or chunk information [ 10, 12-15]. Similar to the address-event
representation (AER) and virtual wires,
developed at Caltech and U Delaware, respectively, Applied Neurodynamics in
1989 independently developed a communication scheme for neural event messages
called the space-time attribute code (STA, 15). The
latter is actually a generalization of the former with several scalability
advantages. It was not funded at that time (DARPA BAA) and was kept
company proprietary until about 1994 when I teamed with Douglas and Mahowald,
then at Oxford, to develop the first Silicon Cortex Board (SCX 1). It was soon after published. Current work is focused on design of
efficient scalable electronic embodiments of the key elements of biological
neural codes and how they are processed.
Metaphorically
speaking, looking at it intuitively from the top down, we can 'see' that
pattern recognition and formation is the common denominator in all human
cognitive skills [7, 8, 32, 33, 34].
The syllogism of Western logic ("All men are mortal...") is a case
study in verbalized sequential pattern recognition at multiple levels of
encoding. (The metaphysics of "patterns" has not yet been fully
appreciated in contemporary scientific ontology). Perhaps, if the
basic mechanisms of neural activity pattern formation are unraveled, then many
aspects of perception, cognition and action can be 'bound' in a
'coherent' brain theory (for starters).
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Metaphor – and What it all Means (section
evolving from 2002 to present)
Modern Cognitive
Science, especially Cognitive Linguistics teaches us that all conceptual
thinking is based upon a hierarchy of metaphors or conceptual blends [37,38,34]. For
example, “War is Hell” is a metaphor that equates or blends two entirely
different concepts, each with its own roots in other concepts and
experiences. Poets are masters of
this kind of equivocation. It
turns out that the rest of us are pretty good at it too.
The ability to
use and understand metaphors is based on our ability to see analogies between things that are not fundamentally identical
[33].
For example, “Life is like a box of Chocolates….” In a certain context, this statement is
meaningful and not an oxymoron.
Our ability to find the relevant context (sometimes with a little help, e.g., “…You never
know what you’re gonna get.”) is a key aspect of our language ability and
involves the associational nature of our memory systems in a deep way. Far out associations like the above are
often the basis for humor where the closure on the association is not
anticipated and results in a surprise ending. Surprises are fun(ny).
In order to see
that things are analogous we have to be able to use our associative skills to find a dimension of comparison between
two things where they are close together (in semantic space). Every person is a unique individual,
but “All men are mortal.” They
have at least that much in common on the dimension of mortality. Now going on to the crux of the matter,
to see that all men are mortal, we have to first understand that there is a category of things called men, and a category of things that
die. This comes from lots of
observations of people and death.
We see a pattern in
bipedal mammals that talk and stand upright, the mankind or people concept. We
also see that mammals all die one way or the other sooner or later. This pattern of termination and decay
is the concept of mortality. We
associate this pattern with mammals and other living things and say mortality
is a property they have. It is another pattern we induce from
much data or by having it passed on to us through training in our cultures and
belief systems. These patterns
‘mankind’ and ‘mortality’ are associatively bridged through ‘mammal-ness’ or
‘living-thing-ness.’ By occurring
together often a larger pattern forms naturally encompassing the two, the
conception or deduction (note - based upon induction) that “all men are
mortal.”
So the syllogism of western logic first requires seeing that Men have the property of
mortality (associative categorical pattern match) and noting that Socrates is a
man (he fits that categorical pattern).
Then the new pattern completes itself associatively, and we “see” that
Socrates is mortal. When the
underlying categories and requisite patterns are overlearned, the pattern
completion emerges spontaneously and very rapidly (as in eureka!) Logic is more like the unconscious
processes enabling perception than the illusion we carry around about conscious
voluntary thinking [32, 39]. Naïve logic is a thought habit we learn
through embodied enculturation with language, and the latter shapes our
thinking as a perceptual and conceptual organizer [44]. Language is made possible because of
our associative skills to find bridging contexts and our pattern matching
skills to categorize. Formal
logic, discourse and other forms of conscious thought are vocalized
associations and pattern completions leading to new concepts and conceptual
blends and the sharing of them.
These are “habits of minding.”
The work of the anthropologist Leslie White is relevant [42] along with that of many others. I hasten to add that language and logic
are likely structured as they are largely as a result of the basal ganglia and
cerebellar loops that subserve the frontal lobes [46, 47]. These
circuits were evolved to serialize behavior, a kind of syntax of the
musculoskeletal system. Certain
moves (sounds) are not permitted and some can not follow others. The meaning is in the effect of the
movement and the intent behind it.
It seems likely that speech runs off the same circuits, and the rest of
language behavior owes a huge debt to that starting point.
When we
understand the meaning of
something linguistic or other signs or events in general we get a feel for (‘feeling
of understanding’) what the implications
are that follow from it. There are
implications having to do with what must have happened or what else is
happening now which we call presuppositions or assumptions. Then there are all the things that
might happen next, including what we might do, think, say or feel in response
to the message or situation. All
these implications create personal expectations by learned
associations, and those result in feelings
of competence, familiarity etc. when we understand, or it may result in
feelings of response conflict, ambiguity, or hesitation if we don’t
understand. These feelings
likely derive from past experience of rewards, punishments and their surrogates
and future ones anticipated. We
get the conscious feeling of understanding or its opposite when we are paying
attention even when we do not become consciously aware of all the expectations
that we harbor. When you get the
feeling of “uhuh – go on” or “wtf – I’m lost” or even “aha – now I get it” you are having a felt experience based
on confirmation, disconfirmation, or sudden reorganization of your
expectations. For related theory
see the works of Ellis and Newton [48, 49]. It still comes down to our ability to
complete patterns in the represented events or situations via the perceptions
we have or the conceptual messages we are deciphering. Note that they can be completed wrongly
resulting in misunderstanding.
Feelings are fallible, hence the distinction between expectations
(subjective) and implications (public and debatable). Our expectations that result from an event (linguistic or
otherwise) and sometimes expressed in the form of a story, prediction,
explanation, or other verbal report, are the meaning of the event. On many occasions we can feel we
understand something without being able to express why or how.
Beyond semiotic
meaning, understanding and feelings of understanding it turns out that this
process of assigning meaning to our sensations by associative interpretation
is, by my definition, consciousness itself. The implications of that insight are quite profound (if I
say so myself). It is now my task
to write that up, i.e., “what it all means”, and publish it.
Our pattern
forming, recognizing, completing and associating neural machinery, as
illustrated by primitive neural network models [40, 45],
is the general mechanism that makes all this possible. This is what makes neural networks a
most promising pillar underlying the field of Cognitive Science. These networks that do pattern
recognition are often called associative networks or pattern completion
networks. There seems to be a
common underlying mechanism for all these skills. There are subtle distinctions to be made here in the
detailed analysis, but that should not overshadow the breakthrough insight now
evident that pattern processing in one form or another underlies all
perception, cognition, logic, discovery and creativity.
We go beyond
cognitive science’s abstract neural network models when we acknowledge that
real neurons are physical.
Physical systems have dynamics sometimes characterized in the prevailing scientific metaphor as “computation.” If we can better understand how neurons
collectively represent concepts and associations in their structure and dynamic
behavior, we can (with some forethought such as is rarely seen in technology)
build useful artifacts that make us smart [41]. Hence the phrase “Applied
Neurodynamics.” More important, we can demythologize our Selves [36]. We are the
last anthropomorphic frontier.
This is a risky
business we are embarked upon. It
will challenge all our sacred institutions and beliefs. It will knock the slats from under
prevailing political persuasions.
It will create the greatest crisis in Self-doubt ever faced by humanity,
and clarify what it is to be human.
As painful and dangerous as this will be in this century, there is no
turning back now. We will have to
be mindful of the need to find a new frame of reference from which humanity can
operate for its own preservation while respecting its own limitations, the
balance of natural resources and the rights of all living things. Morality and personal responsibility
will have to be thought through again, and this has obviously already begun
[e.g., 43].
In this post decade-of-the-brain era….. life really is like a box of
chocolates.
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Services (what I do to pay my bills)
For
neurocomputing, neuromorphic engineering, telecommunications and general
electronic design :
System specification,
architectural studies, and design reviews (Visio Block Diagrams)
Digital Logic design (CMOS,
ECL, PECL, TTL, impedance controlled)
Board
level design ( PCI, VME, SBUS, serial and custom busses)
Telecom
and Networking design (ATM, Sonet, serial broadband methods)
Schematic entry (ORCAD,
Cohesion)
FPGA/CPLD design and simulation
(Xilinx, Altera, VHDL)
Printed circuit board placement
and routing (PADs)
PWB fabrication management
(multilayer quick turn)
Component selection,
purchasing, and kitting for assembly (DUNS 62-525-9155)
Assembly management including
SMD techniques
Test and production of
completed designs
Application and software
development
Neural Networks and related
Communications R&D
In summary, we
(one man gang) can take your design from vague idea to production on a budget.
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Completed and in Progress!
Research continues on Neural Communication Systems including custom bus
design, protocols, routers, filters, neural coding schemes and related
issues. [
10, 12-15]
The following text highlights NN designs, protos and production runs
done by Applied Neurodynamics:
Designed and
prototyped a TMS320C50 DSP system (80 MHz) for multichip & multiboard
analog VLSI neural network systems (Silicon Cortex Chips) based upon
broadcast channels embedded in a VME chassis. Included two custom
designed high speed communication channels and eight Cypress VHDL based
CPLDs. Also did all board layout and assembly myself which was double
sided surface mount with fine pitch SMDs all over. [29]
(Partial funding from Dr. Rodney Douglas at MRC, Oxford, UK and from Dr. Carver
Mead at CALTECH, Pasadena) [see Related Web Sites]
Designed and
prototyped a microcoded 20MHz sequencer based upon 4 Xilinx 4005 series
FPGAs to drive multiple AT&T Analog neural network ALU IC's (ANNA) for
imaging/OCR applications. This architecture was placed on two
boards. One was a VME standalone, and the other was a mezzanine card for
an MVME197 (MC88000) RISC CPU board. (Ref. Dr. Charles Stenard at Bell
Labs, Holmdel) [23-24]
Designed and
produced a few dozen 50 MHz AT&T DSP32C board for ISA/AT bus neural
network character recognition applications. Personally built a few dozen
before volume production started at AT&T/NCR. (Ref. Ivan Strom at
Bell Labs, Holmdel)
Designed and
prototyped the ANNA/VME image processing and character recognition
neurocomputer board with on-board DSP32C. Also ported this design to the
ISA/AT bus and prototyped it. (Ref. Dr. Bernhard Boser formerly at
Bell Labs, Holmdel) [21-22]
Consulted in the
troubleshooting of and prototyped a multiwire VME neurocomputer board using
the NET32K image processing and recognition chip with the DSP32C.
(Ref. Dr. Hans Peter Graf at Bell Labs, Holmdel) [ 17]
Designed and
prototyped a VME neurocomputer board based upon a new Stochastic Pulse Train
network chip. This board had 22 neural network IC's on a single wide
VME card. (Ref. Dr. Stan Tomlinson formerly at Orincon Corp., San Diego)
[27]
Designed and
produced the EMB Prototyping board that holds up to 8 Intel ETANNs
(Electronically Trainable Analog Neural Network ). Over 70 were hand
built. (Ref. Mark Holler at Intel Corp., Santa Clara) [25]
Designed and
prototyped a VME/VSB i860 processor board and parallel processing
system. (Ref. Dr. Cedric Armstrong at Science Applications International
Corp., San Diego)
Designed two
different stand-alone boards for Neural Semiconductor based upon the DNNA
(stochastic nets) architecture. (Ref. Stan Tomlinson formerly at Neural
Semiconductor, Carlsbad) [26]
In addition to the above Neural Network Architectures Applied
Neurodynamics has gained relevant experience by doing other state-of-the-art
digital and analog designs for data acquisition and telecommunications from DC
to OC48. I helped create three bus
standards for FASTBUS, VME Bus, and Scalable Coherent Interface. I am now designing scalable brain emulation
systems in the Cauwenbergh’s lab at UCSD.
Please refer to
the resume’ link.
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Publications, TRs, and References (chronological and annotated):
1.
Abraham & Shaw, Dynamics: The Geometry of Behavior (Vol.
I-IV ), Aerial Press, (Santa Cruz),
1984. [A highly visual introduction to the notions of Dynamical Systems.]
2.
Deiss, S., Downing R.W., Gustavson, D.B., Larsen, R.S. , Logg, C.A., Paffrath,
L.," Applicability of the Fastbus Standard to Distributed Control,"
presented at the Particle Accelerator Conference in Washington, D.C.,
1981. Also SLAC-PUB-2703, Stanford Linear Accelerator Center, Menlo
Park. [Overview of Fastbus.]
3. Deiss, S.,
"A Fastbus Controller Using a Multibus MPU," Proceedings of the
Nuclear Science Symposium in Washington, D.C., IEEE, (New York), 1982.
Also SLAC-PUB-2994, Stanford Linear Accelerator Centor, Menlo Park.
[Described the SLAC Fastbus controller which adapted any Multibus master board
to the 10K ECL Fastbus.]
4. Deiss, S.,
Gustavson, D.B., " Software for Managing Multicrate FASTBUS Systems,"
Proceedings of the Nuclear Science Symposium in Washinton, D.C., IEEE, (New
York), 1982. Also SLAC-PUB-2995, Stanford Linear Accelerator Centor,
Menlo Park. [Detailed description of a microprocessor algorithm that used
VM simulation to manage a very large data acquisition system data base for HEP
experimental galleries.]
5. (Deiss, S. as
one Working Group Member), IEEE Standard FASTBUS Modular High-Speed
Data Acquisition and Control System (ANSI/IEEE 960-1986), IEEE,
(New York), 1986. [Contributed software for initializing and managing
multi-segment data acquisition systems including a solution to the broadcast
tree initialization problem.]
6. (Deiss, S. as
one Working Group Member), IEEE Standard for a Versatile
Backplane Bus: VMEbus (ANSI/IEEE 1014-1987), IEEE, (New York),
1987. [Reviewed for final ballot and helped set up VITA Trade Association
User Groups.]
7.
Deiss, S., "Artificial Neural Systems Engineering and Analysis,"
abstract of poster in Proc. 1st Annual Meeting of the International Neural
Network Society in Boston, Pergamon, (Boston), 1988. Available as TR on
request. [Discussed a range of issues including need for understanding
sequential cognitive processes via dynamics.]
8.
Deiss S., & Works, G., "Application of SAIC's Neurocomputer and Neural
Networks Software," invited paper in Proc. 4th Annual Artificial
Intelligence and Advanced Computing Technologies Conference in Long Beach
(Murray Teitell, ed.), Tower Conference Management, Glen Ellyn, Ill.,
1988. [Emphasized pattern recognition based on sub-symbolic computation
(as in neural nets rather than rule-based systems) as a better approach for
AI.]
9.
Deiss, S., Hicks, W., Kasbo, R., Morse, K., Muenchau, E., Works, G., "The
SAIC Delta Neurocomputer Architecture," abstract of invited talk in Proc.
1st Annual Meeting of the International Neural Network Society in Boston,
Pergamon, (Boston),1988. See also US Patent No. 4,974,146 entitled
"Array Processor," by G. Works et. al., Nov. 27, 1990. [Talked
mainly about neurocomputer 'specsmanship' which continues to be a glass bead
game for braggarts.]
10.
(Deiss, S. as Study Group Chairman), "Neural Systems Interface and IEEE
Standards," a report to the IEEE Microprocessor Standards Committee, 1989.
Available as TR on request. [Suggested directions and constraints on
future use of Futurebus+ and Scalable Coherent Interface in neurocomputing
applications.]
11.
(Deiss, S. as one Working Group Member), IEEE Standard for Scalable
Coherent Interface (SCI) (ANSI/IEEE 1596-1992), IEEE, (New York),
1992. [Contributed to definition of a broadcast capability with NN
applications in mind.]
12.
Deiss, S., "Communication Architectures for Large Neural Network
Implementations," abstract of poster in Program for Snowbird Neural
Networks for Computing Conference, AT&T, 1993. [Showed how to
simulate a large network of spiking neurons with a raster version of the
space-time-attribute code. No paper.]
13. Deiss, S.,
"Event Broadcast Speed, Latency, and Variability in VLSI
Neuromorphs," abstract of poster in Program for Snowbird Neural Networks
for Computing Conference, AT&T, 1994. [Explored problems of TDMA bus
use. No paper.]
14.
Deiss, S., "Temporal Binding in Analog VLSI," poster in Proc. World
Conference on Neural Networks in San Diego, INNS, (Boston), 1994. [Looks
at time representation issues from multiple levels.]
15.
Deiss, S., "Connectionism without the Connections," invited paper in
Proc. World Congress on Computational Intelligence in Orlando, IEEE, (New
York), 1994. [Explains space-time-attribute code and timing
considerations in scaling large neural networks of spiking neurons.]
16.
Domany, E., van Hemmen, J.L. Schulten, K., Models of Neural
Networks II, Springer-Verlag, (New York),
1994. [Subtitled: Temporal Aspects of Coding and Information in
Biological Systems; good coverage of the coherent firing view of networks with
excellent contributors.]
17.
Graf, H.P., Janow, R., Nohl, C.R., Ben, J., "A Neural Network Board System
for Machine Vision Applications," in Proc. International Joint Conference
of Neural Networks in Seattle, IEEE, (New York), 1991. [Describes
wire-wrap proto later improved and redone with multiwire technology.]
18.
Hebb, D.O., The Organization of Behavior, Wiley, (New York), 1949. [A brilliant neural
theory for its time sometimes characterized by the tough-minded as a lucky
guess.]
19.
Padulo, L., Arbib, M.A., System Theory, Saunders, (Philadelphia), 1974. [Well
balanced basic text.]
20.
Paffrath, L. et al., "FASTBUS Demonstration Systems," invited paper
in Proceedings of the Nuclear Science Symposium in San Francisco, IEEE (New
York), 1981. Also SLAC-PUB-2835, Stanford Linear Accelerator Center, Menlo Park.
[Describes FASTBUS demos given at NSS Meeting including software detailed in 4
above.]
21.
Sackinger, E., Boser, B.E., Bromley, J., LeCun, Y., Jackel, L.D.,
"Application of the ANNA Neural Network Chip to High-Speed Character
Recognition," in IEEE Trans. on Neural Networks , Vol. 3, No. 3,
May 1992. [Shows the VME version of the 1st ANNA design.]
22. Sackinger,
E., Boser, B.E.. Jackel, L.D., "A Neurocomputer Board Based on the Anna
Neural Network Chip," in Advances in Neural Information Processing Systems
4 , (Moody et. al., ed.), Morgan Kaufman, San Mateo, 1992. [More on
the VME and the ISA/AT ANNA boards.]
23. Sackinger, E., Graf, H.P., "A System for High-Speed
Pattern Recognition and Image Analysis ," in Proc. of the 4th
International Conference on Microelectronics for Neural Networks and Fuzzy
Systems in Turin, IEEE, (New York), 1994. [2nd generation ANNA board
described.]
24. Sackinger,
E., Graf, H.P., "A Board for High-Speed Image Analysis and Neural
Networks," in IEEE Trans. on Neural Networks , Vol 7, No. 1, Jan
1996. [More on high speed sequencer ANNA board applications.]
25.
Tam, S., Holler, M., Brauch, J., Pine, A., Petterson, A., Anderson, S., Deiss,
S., "A Reconfigurable Multi-Chip Analog Neural Network; Recognition and
Back-Propagation Training," in Proc. International Joint Conference on
Neural Networks in Baltimore, IEEE, (New York), 1992. [Describes ETANN
Multichip Board and its use.]
26.
Tomlinson, M.S., Walker, D.J., Sivilotti, M.A., "A Digital Neural Network
Architecture for VLSI," in Proc. International Joint Conference of Neural
Networks in San Diego, Lawrence Earlbaum, (Hillsdale, NJ), 1990. [Two
boards were done for the DNNA chip architecture described here.]
27.
Tomlinson, M.S., "ORINCON's VLSI Chip Executes Neural Nets Faster,"
in "The Wave" by Orincon Corp., (San Diego), Nov./Dec. 1991.
[Board was done for Orincon's DARPA contract. Similar to DNNA chip.]
28.
Sheu, B., Choi, J., Chang, R., Neural Information Processing and VLSI, Kluwer,
(New York), 1995. [See the section on the Silicon Cortex (SCX) board.]
29.
Deiss, S., Douglas, R., Whatley, A., "A Pulse-Coded Communications
Infrastructure for Neuromorphic System," in Pulsed
Neural Networks, W. Maass, ed., MIT
Press, 1999. [Good Overview of SCX with emphasis on biological
motivation behind it.]
30. Fujii, H., Ito, H., Aihara, K., Ichinose, N., Tsukuda, M., "Dynamical Cell Assembly Hypothesis - Theoretical Possibility of Spatio-temporal Coding in the Cortex," in Neural Networks, Vol. 9, No. 8, p. 1303, Pergamon, 1996. [A good contemporary overview of the viewpoint taken here.]
31. Crutchfield, J., The Calculi of Emergence: Computation, Dynamics, and Induction, Physica D 75 (1994) 11-54.
32. Mitchell, M., Analogy-Making as Perception, MIT Press, 1993.
34. Fauconnier, G., Turner M., The Way We Think: Conceptual Blending and the Mind's Hidden Complexities, Basic Books, 2002.
35. Llinas,
R., I of the Vortex: From Neurons to Self, MIT Press, 2002.
36. Quartz, S.,
Sejnowski S., Liars, Lovers and Heroes: what the new brain science reveals about
how we become who we are,
Morrow, 2002.
37. Lakoff, G., Johnson, M., Philosophy in the Flesh, Basic Books, 1999.
38. Lakoff, G., Nunez, R. Where Does Mathematics Come
From, Basic Books, 2000.
39. Wegner, D. The
Illusion of Conscious Will, MIT Press, 2002.
40. Haykin, S., Neural Networks: A Comprehensive Foundation, Prentice-Hall, 1998.
41. Norman, D., Things that Make Us Smart: Defending Human Attributes in the Age of the Machine, Perseus Publishing, 1994.
42. Service, E., Leslie Alvin White 1900-1975 (Obituary), American Anthropologist, 78:612-617, 1976.
43. Juarrero, A., Dynamics in Action: Intentional Behavior as a Complex System, MIT Press, 2002.
44. Donald, M., A Mind So Rare: The Evolution of Human Consciousness, Norton & Co., 2002.
45. Principe, J., Euliano, N., Lefebvre, W., Neural and Adaptive Systems: Fundamentals through Simulations, Wiley, 2000.
46. Lieberman, P., Human Language and Our Reptilian Brain, Harvard Univ. Press, 2000.
47. Houk, J., Davis, J. & Beiser, D. Models of Information Processing in the Basal Ganglia, MIT Press, 1995.
48. Ellis, R. Questioning Consciousness, John Benjamin, 1995.
49. Newton, N., Foundations of Understanding, John Benjamins, 1996.
50. Deiss, S., “What It All Means,” Draft, 2004-6. [The most complete so far of my writing about the place of consciousness in nature.]
51. Deiss, S., “What It All Means: on the feeling of understanding,” (Conference Poster), McDonell Conference in Neurophilosophy, CalTech, Pasadena, CA, June, 2005.
52. Deiss, S., “Where is awareness of meaning and understanding in the brain,” (Conference Poster), Society for Neuroscience annual meeting, Washington, DC, Session 771, November, 2005.
53. Deiss, S., “Why the natural world does not work without qualia and the consciousness of them,” (Conference Poster), Towards a Science of Consciousness, Tucson, AZ, April, 2006
54. Deiss, S., “UNCC: The Universal Correlates of Consciousness,” Draft, 2006. [The most succinct statement of my current unfolding view of consciousness.]
55. Deiss, S., “UNCC: The Cure for Chronic ‘Zombie Blues,’ (Conference Poster), ASSC 11, Las Vegas, NV, June, 2007
56. Deiss, S., “The Universal Correlates of Consciousness,” to appear in Mind That Abides: Classic and Contemporary Readings in Panpsychism, Skrbina, D., Ed., (2007?)
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Related Web Sites:
General Neural Net Related Sites
Salk Research Groups (CNL, SNL, etc)
UCSD Institute for Neural Computation
UCSD Swartz Center for Computational Neuroscience
UCSD Institute for Nonlinear Science
UCSD Center for Theoretical Biological Physics
Cognitive Neuroscience Sources
International Neural Network Society
Assoc. for the Scientific Study of Consciousness
Specifically Related Research/Theory
Cauwenbergh’s Web page pending release of new web site
The work of Newton and Ellis on language, consciousness, and embodied cognition
IU Center for Research on Concepts and Cognition
Melanie Mitchell’s Analogy Work
UCSD Conceptual Blending Group
UCSD Center For Research in Language
Crutchfield on how "computation" arises from neural behavior
Eth Zurich Silicon Cortex Project
CIT Center for Neuromorphic Systems Engineering
Clark Lindsey's overview of NN Hardware Technology (recently updated)
Dan Hammerstrom at OGI - Cognitive Architecture Project
Kindred Spirits
Jim Miller's Living System Theory
Ervin Laszlo's Systems Philosophy
Other Interest Areas
HRP on Zero Point Fields and Inertia
Last but Not least
Quality, Values, Metaphysics, Motorcycles and the Mind
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A Personal Note
Educational Background: MS in CS (Purdue), BA in Philosophy & in Psychology (Michigan), Advanced work in Neuroscience
Philosophy of Mind (Free Will,
Mind-Body Problem, Consciouness)
Cognitive and Sensory Psychology (Neural Modeling, Info.
Proc. Models, Memory & Attention)
Artificial Intelligence (Semantic Nets, Analogical
Reasoning)
Computer Software Engineering (Programming Language Design
and Operating Systems)
Self-trained in electronic circuits, buses & telecommunications,
logic, FPGA, and PWB design.
Work Experience:
30 years in hardware design, software engineering, and education. Senior Member IEEE. Member Society for Neuroscience, Association for the Scientific Study of Consciousness and others.
(HTML Resume
available for clients and headhunters)
Interests:
Neurodynamics of Consciousness
and Analogical Perception/Reasoning
Neurobiology of Stress and Mindfulness-based Stress
Reduction (MBSR)
Basal Ganglia, Orbito-frontal support of sequences in movement, thought, reason and
language
Neurocomputer Design, Neuromorphic Engineering, Robots with
feelings
Fundamental Physics
Neural Population Coding and Temporal Coding
General Systems
Principles and Complex Adaptive Systems
Brain Food ( “New
Paradigm Diet” ):
Click above to see what I have read, what I am reading now, and plan to read.
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