Chapter VI
Artifcial Mind
Rita M. R. Pizzi
University of Milan, Italy
Copyright © 2008, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.
ABSTRACT
The advances of artifcial intelligence (AI) have renewed the interest in the mind-body problem, the
ancient philosophical debate on the nature of mind and its relationship with the brain. The new version
of the mind-body problem concerns the relationship between computational complexity and self-aware
thought. The traditional controversy between strong and weak AI will not be settled until we are able
in the future to build a robot so evolved to give us the possibility to verify its perceptions, its qualitative
sensations, and its introspective thoughts. However, an alternative way can be followed: The progresses
of micro-, nano-, and bio technologies allow us to create the rst bionic creatures, composed of biologi-
cal cells connected to electronic devices. Creating an articial brain with a biological structure could
allow verifying if it possesses peculiar properties with respect to an electronic one, comparing them at
the same level of complexity.
INTRODUCTION
The attempt to understand the nature of mind
goes back to the time of the Greek philosophers.
In modern times, the behavioral movement conzcentrated on the external behavior, excluding the
importance of the internal mental processes. Only
in the last decades the cognitive sciences and the
artificial intelligence (AI) research attracted atten-
tion on mental processes, reviving the interest in
the nature of intelligence and reexive thought.
The development of computer technology
opened a new research method by means of
computer simulations of functions typical of the
human brain.
After the enthusiasm of the first times, when
many prototypes were developed, the successes of
AI research faded in the ’80s due to the difficulty
to move the AI algorithms in the complexity of
the real world.
However, although the wide, excessively
expensive and ambitious AI systems that con-
84
Articial Mind
centrated on specic problems were once re-
nounced, in the last decade, swift developments
have produced many applications in robotics and
several useful algorithms applied to all kinds of
elds, where the complexity of the problems is
greater and traditional algorithms cannot reach
signicant results.
Nonetheless, the solutions proposed for the
problem of the nature of mind remain debat-
able.
The development of AI created a diatribe be-
tween the supporters of the so-called strong and
weak AI. Following weak AI, the computer is just
a powerful tool that allows us to verify hypotheses
and to implement useful functionalities, but it will
never have the features of a human mind.
Following strong AI, the computer can be
considered a real mind that in the future, endowed
with evolved programs, will possess all the fea-
tures of the mind and cognitive states.
This unsolved diatribe changed over time into
a dispute between materialists and nonmaterial-
ists. The purpose is not anymore to decide if the
technology will succeed in developing robots
with an intelligence similar to the human one
(we understood that this is a very far goal but
that we will reach it for all practical purposes),
but to understand the very nature of mind and its
relationship with the brain.
This problem is seen today not only as a
philosophical but also as a scientic goal, whose
application to AI development will be an impor-
tant consequence.
BACKGROUND
Weak AI vs. Strong AI
One of the most famous and sharp criticisms of
strong AI is John Searle’s work (1980) and its
“Chinese room” thought experiment. An English
man is closed in a room. He does not understand
Chinese. He receives a sheet containing a story
and some questions on the story written in Chi-
nese, and a set of rules to draw Chinese symbols
in reply to the questions. The man follows the
rules and answers the questions.
The rules correspond to a computer program.
The man corresponds to a computer that processes
the program specied by means of formal rules.
The man can become so clever that his answers are
indistinguishable from those of a Chinese man.
Strong AI maintains that the computer under-
stands the story, but the man in the room does not
understand the story at all. He does not know the
meaning of the Chinese symbols. Many objections
have been raised by Searle’s conclusion about this
thought experiment, and Searle always rejected
them with extremely smart considerations.
However, it is clear that the English man ac-
complishes in the room only a careful but stupid
copying task from a lookup table and does not
perform a learning algorithm such as those AI
(and all the more reason for the human mind) is
able to accomplish.
The man in the room could try to understand
the connection between the lookup table items and
the story, and nally he could learn Chinese. The
same could be accomplished by a machine using,
for example, an articial neural network or other
symbolic knowledge representation techniques.
Thus, it seems that the progresses of AI succeed
without difculties in giving a computer the ability
to understand the connections between informa-
tion, and to memorize and learn them.
Douglas Hofstadter (1979; see also Hofstadter
& Dennett, 1981), one of the most convincing and
famous strong-AI supporters, maintains that a
growing complexity of the computer processes,
including the interaction between different cogni-
tive levels, could lead a machine not only to the
ability of understanding and learning, but also to
the emergence of the ability of self-reection that
is the basis of consciousness.
Hofstadter hypothesizes that consciousness
rises from the closure of a tangled loop between
the high (symbolic) level and low (neurophysi-
85
Articial Mind
ological) level, bounded to each other by a chain
of causalities. The most central and complex
symbol of the high level is the one we call I. This
closed loop allows the representational system to
perceive its own state inside a set of concepts.
Currently, many AI programs already pos-
sess the ability to know their own structures and
react to variations of these structures, exhibiting
a rudimental kind of control of the self. In con-
clusion, Hofstadter afrms that the mind can be
reproduced inside a computer because it can be
simulated by a program, thus it does not reside
in the biological structure of the brain.
The “Hard Problem”
The above considerations seem to lead to the as-
sertion that a computer could in the future pres-
ent all the features of a human mind, including
consciousness. However, the problem is not so
easily solved.
Following David Chalmers (Chalmers, 1995,
1997), to be able to develop an intelligent machine
that is aware, understands, learns, and possesses
a control of its own self as a human being does is
not equivalent to state that this machine possesses
a mind in the subjective and qualitative sense that
we experience.
He coined the term “hard problem” to indi-
cate the problem to understand the origin of the
subjective and qualitative component of the mind
experience differently from “easy problems,”
that is, the problems that concern the integration
between internal mental states and sensorial
perceptions, selective attention, emotional be-
havior, and so forth. These problems should in
principle be solved in the future by means of the
neurophysiological research and the computer
science approach.
In the future, a computer could also be able
to reproduce faithfully the human thought in a
third-person fashion, but nothing indicates that
the computer would have a rst-person experience
of what it is elaborating.
As an example, Minsky’s emotion machine
(Minsky, 2006) could soon reproduce many typi-
cally human functionalities, like emotions and
the idea of self, but there is no reason to think
that the computer would experience emotions
and the sense of self in the same subjective way
as we do.
The hard problem is not a problem about how
functions are performed, but the problem to un-
derstand why the performance of the functions
is associated with conscious experience.
We are not able to explain why, for instance,
when the brain processes a specic wavelength of
light, we have the experience of a specic color
(the “blueness” of blue).
Consciousness and Laws of Nature
Chalmers does not deny that the biological struc-
ture of the brain is heavily implied in the onset of
the phenomenon of consciousness, but he afrms
that the connection between conscious processes
and their neural correlates are not obvious. How-
ever, both Chalmers and Searle (1980) believe that
the difference between man and machine could
be connected with the specic properties of the
brain physiology. The exact reproduction of the
neural physiology, even with a different chemis-
try, could lead to reproduce also the experiential
properties of consciousness.
Another way to face the problem, as Chalmers
suggests, is to admit that consciousness is an ir-
reducible phenomenon, that is, an a priori property
of nature. In this way, consciousness would obey
laws similar to the other fundamental physical
laws like gravity or electromagnetism.
The key observation is that not all the entities
in science are explained in terms of more primitive
physical entities. For instance, space-time, mass,
and charge are considered fundamental entities
of the universe because they are not reducible to
something easier.
In the case of consciousness, the goal would
be to afrm that the brain state B produces the
86
Articial Mind
conscious state C due to the fundamental law X.
We could come to a theory of everything that
includes the laws of consciousness inside the set
of the laws of nature.
A possibility to include consciousness in the
laws of nature has been opened by quantum me-
chanics, in whose fundamentals the role of the
observer is extremely critical. Several idealistic
or interactionist theories are still competing
with the traditional Copenhagen interpretation:
Erwin Schrödinger (1956) rst approached the
Oriental monism in the frame of quantum me-
chanics, putting the accent of the indissolubility
between the physical event and observer’s mind.
After him, J. Archibald Wheeler (1983), Eugene
Wigner (1961, 1972), and in more recent times
Josephson and Pallikari-Viras (1991) and Henry
Stapp (1993) maintained quantum theories where
consciousness is crucial in the objectivity of
physical reality.
In particular, Stapp (1993) proposes an inter-
pretation where consciousness, intended as an
a priori phenomenon, is the cause of the wave
function collapse. On the other hand, Chalmers’
hypothesis is that a way to include consciousness
in the frame of the laws of nature is to develop
a theory of everything based on the concept of
information, hypothesizing that information has
two aspects: a physical one and an experiential
one. Systems with equal structural organization
include equal information. This idea is compatible
with Wheeler’s (1983) theory that information is
a fundamental concept of physics. The laws of
physics could be re-coined in informational terms,
satisfying the congruence between physical and
psychophysical structures.
Although the role of the observer in quantum
mechanics remains an extremely controversial
issue, in the last few years several quantum mind
theories have been developed (Hagan, Hameroff,
& Tuszynski, 2002; Matsuno, 1999; Tuszynski,
Trpisova, Sept, & Sataric, 1997) that intend to
connect the biophysical properties of the brain to
quantum physics. The most authoritative is the
Penrose-Hameroff theory (Hameroff & Penrose,
1996; Penrose, 1994), which hypothesizes that
in microtubules, cellular structures inside the
neuron, quantum reductions take place associated
with simple consciousness events. Microtubules
possess the physical properties suitable to obey
quantum laws, thus they could play a fundamental
role in the phenomenon of consciousness.
THE MIND-BODY PROBLEM IN THE
21ST CENTURY
Both the hypothesis that intelligence and self-
awareness could spring from the complexity of
the brain, or an articial structure perfectly ho-
mologous to the brain, and the parallel hypothesis
that consciousness is an a priori entity of nature
and could be connected to the fundamentals of
quantum physics are at the moment indemon-
strable. However, thanks to the progresses of
electronics and of computer technology, we can
start to build the bases of an empirical proof of
these theories.
During the past decade, several laboratories
in the world carried out experiments on direct
interfacing between electronics and biological
neurons in order to support neurophysiological
research, but also to pioneer future hybrid hu-
man-electronic devices, bionic robotics, biological
computation, and bioelectronic prostheses (Akin,
Naja, Smoke, & Bradley, 1994; Canepari, Bove,
Mueda, Cappello, & Kawana, 1997; Egert et al.,
2002; Maher, Pine, Wright, & Tai, 1999; Potter,
2001). Progress in this research eld is quick and
continuous.
During the early ’90s, Fromherz’s group (Max
Planck Institute of Biochemistry) rst pioneered
the silicon-neuron interface. The group keeps
developing sophisticated techniques to optimize
this kind of junction (Fromherz, 2002; Fromherz,
Muller, & Weis, 1993; Fromherz, Offenhäusser,
Vetter, & Weis, 1991; Fromherz & Schaden,
1994).
87
Articial Mind
Many other experiments have been carried out
with different aims: Garcia, Calabrese, DeWeerth,
and Ditto (2003) and Lindner and Ditto (1996) at
Georgia Tech tried to obtain simple computations
from a hybrid electronic leech creature. As the
neurons do not behave as “on-off ” elements, it has
been necessary to send them signals and interpret
the neural output using the chaos theory.
In 2000, a team of the Northwestern University
of Chicago, University of Illinois, and University
of Genoa (Reger, Fleming, Sanguineti, Alford,
& Mussa-Ivaldi, 2000) developed a hybrid crea-
ture consisting of lamprey neurons connected
to a robot. In front of light stimuli, the creature
behaves in different ways: follows light, avoids
it, and moves in circle.
In 2002, De Marse, Wagenaar, and Potter at
Georgia Tech created a hybrid creature made of
a few thousand living neurons from a rat cortex
placed on a special glass Petri dish instrumented
with an array of 60 microelectrodes, also able to
learn from its environment.
In 2003, Duke University’s group (Carmena
et al., 2003) succeeded in connecting 320 micro-
electrodes to monkey cells in the brain, allowing
them to directly translate the electrical signals into
computer instructions and to move a robotic arm.
In 2005, the SISSA group (Ruaro, Bonifazi, &
Torre, 2005) experimented with the possibility to
use neurons on MEAs (microelectrode arrays) as
“neurocomputers” able to lter digital images.
Despite these astonishing results, neurophysi-
ological research is far from understanding in
detail the learning mechanism of the brain and
fails to interpret the cognitive meaning of the
signals coming from the neurons.
Our group, the Living Networks Lab, since
2002 has carried out experiments on networks
of biological human neurons directly connected
to MEAs (Figure 1).
A Bionic Brain
The neurons, adhering directly to an MEA sup-
port, are stimulated by means of simulated percep-
tions in the form of digital patterns, and the output
signals are analyzed. In previous experiments,
we veried that the neurons reply selectively to
different patterns and show similar reactions in
front of the presentation of identical or similar
patterns (Pizzi, Fantasia, Gelain, Rossetti, &
Vescovi, 2004a; Pizzi, 2006).
On the basis of these results, we developed a
bionic creature able to decode the signals coming
Figure 1. MEA support and magnication of neural stem cells adhering on the MEA
88
Articial Mind
from a network of neurons stimulated by digital
patterns. The whole hybrid system is shown in
Figure 2.
We arranged on the MEA eight input channels
picked from eight electrodes, on which living
cells were attached. The cells were cultured on
the connection sites of the MEA and were con-
nected to each other as in the case of a Hopeld
(1980) articial neural network.
The rst phase of the experiment consisted of
stimulating the neurons with a set of simulated
perceptions in the form of four digital patterns
(Figure 3).
Figure 2. Block diagram of the hardware
Figure 3. The four patterns: forward, backward, left, right
89
Articial Mind
The stimulation occurs with a 100 mV positive
voltage followed by a brief -100 mV depolariza-
tion pulse. The stimulation frequency is 433 Hz,
and the sampling rate is 10 kHz.
Each pattern is constituted by a matrix of
8x8 bits. Every bit lasts 300 ms. The cells are
stimulated 2.4 seconds for each pattern. Each
stimulation is followed by a 1-second pause and
is repeated 10 times for each pattern in order to
allow the neurons to learn.
Once the training phase was nished, a testing
phase was carried out. During this phase, we sent
to the neurons several stimulations corresponding
to one of the four patterns in a random order.
The reactions of the neurons have been sent
to an articial neural network that classied
the answers on the basis of the neural reactions
recorded after the training phase. The model of
the articial neural network, a novel architecture
called ITSOM (Inductive Tracing Self Organiz-
ing Map; Pizzi, de Curtis, & Dickson, 2002),
was developed considering that a self-organizing
architecture was necessary as we had no set of
known outputs to train with.
In the described experiment, we tested the
hybrid system with 25 random patterns. The evalu-
ation of the proposed model presents an accuracy
of 80.11% and a precision of 90.50%. These results
(Table 1) allow us to consider the effectiveness of
our hybrid classier quite satisfactorily.
This research shows a way to deeply analyze
the behavior of networks of neurons and to decode
their signals in reply to simulated perceptions.
After an adequate increase of the number of
electrode connections, the system should be able
to receive real perceptions from suitable sensors
and to react to them.
Our challenge consists of studying in detail the
behavior of networks of neurons and of verifying
if this bionic system, after a suitable increase
of complexity, will allow the emergence of be-
haviors typical of a human mind in an articial
structure with features homologous to the brain
structure.
Possible Quantum Processes in
Cultured Neurons
Another research line of our group concerns the
search for possible quantum processes inside the
neurons. Our system is constituted by networks
of human neural stem cells cultured on a set of
MEAs. We veried that weak electromagnetic
stimulations are able to produce action potentials
in networks of neurons under extremely strict
conditions of optical and electrical shielding.
The rst results showed very high values of
cross-correlation and frequency coherence during
electromagnetic pulses (Pizzi, Fantasia, Gelain,
Rossetti, & Vescovi, 2004b); these results encour-
aged us to continue the experimentations.
During the last 3 years, we prepared and car-
ried out several other experiments, improving both
the hardware detection and controlling system
and the shielding techniques. We also took the
maximum care in preparing the experimental
Table 1. Performance of the hybrid classier
Pattern F Pattern B Pattern L Pattern R Total
Sensitivity 100% 45.45% 75% 100% 80.11%
Specicity 94.44% 100% 83.33% 84.21% 90.50%
90
Articial Mind
protocols devoted to exclude possible biases and
alternative hypotheses.
All the experiments conrm the presence of
spikes in neurons under tested conditions of opti-
cal and electrical shielding. On the basis of the
experimental ndings and the bench tests, it is
possible to state that the spikes appearing in the
neural signals simultaneously with the electro-
magnetic pulses are not due to interference.
The reactivity of neurons to the electromag-
netic pulses could be due to the presence of mi-
crotubules in their cellular structure. The microtu-
bules, formed by wrapped tubulin molecules, are
structurally similar to carbon nanotubes. Actually,
the structures are empty cylinders; the diameter
of a microtubule is around 20 nm and its length is
around some micron, whereas a carbon nanotube’s
dimensions can be similar to or less than those
of the microtubule. Interesting optical, electrical,
and quantum properties of carbon nanotubes are
known (Andrews & Bradshaw, 2005; Gao, Cagin,
& Goddard, 1998; Katura, 1999; Lovett, Reina,
Nazir, Kothari, & Briggs, 2003; Wang et al., 2005).
It is also known that both microtubules and nano-
tubes behave as oscillators (Marx & Mandelkow,
1994; Sept, Limbach, Bolterauer, & Tuszynski,
1999), and this could make them superreactive
receivers able to amplify the signal.
The reported experimental ndings need
further exploration and conrmation. Neverthe-
less, they constitute an attempt to investigate on
possible quantum processes in the brain: Experi-
mental proofs are necessary to yield a validation
of the quantum mind theories and to try to nd an
empirical solution of the mind-body problem.
FUTURE TRENDS
These preliminary results encourage us to con-
tinue our research with more and more complex
experiments. As regards the research on possible
quantum processes in neurons, many other ex-
periments will be needed that must be conrmed
by analogous experiments carried out by other
groups. On the other hand, several theoretical
physicists and cognitive scientists are developing
new theories on the ontological implications of
quantum physics and in particular on the theme
of the quantum mind.
As regards our experiments on learning in the
bionic brain, better performance can be reached
in the future with a better tuning of the articial
neural network that decodes the neural signals.
During an off-line experiment, we already tested
a new procedure able to reach better performance.
In the future, it will be possible to test the system
with a higher number of electrodes and endow it
with sensors that allow real perceptions instead of
the simulated ones. Our goal is to create a bionic
creature that reacts autonomously to environmen-
tal stimulations, and to improve the complexity
of its neural networks.
Only the growth in complexity of the system
could give rise to nonstereotyped behaviors, and
make possible new answers to the problem of
the relationship between mind, brain, and ma-
chines. On the other side, several scientists are
implementing complex software systems able to
emulate functionalities of the human brain. Also
in this case, only a sharp increase of complexity
of these intelligent systems can yield some indica-
tions on the real possibility to simulate the most
evolved features of the human mind. Currently,
several intelligent systems are able to exhibit
performance similar or better than human ones,
but this performance does not concern the sphere
of emotions and self-awareness, and they are only
partially able to perceive and learn autonomously
from the environment and to improve their abili-
ties over time.
Although the way toward the development
of an articial mind is still quite long, certainly
AI is taking many important technological con-
tributions in all the elds of our lives, by means
of software embedded in instrumentations and
91
Articial Mind
computers, and of more and more evolved ro-
bots that facilitate many tasks in the past only
pertinent to humans: from health to industry to
domestic life.
In the particular case of our Living Networks
Lab, we hope that the by-products of our research
activity could be useful to deepen neurophysi-
ological knowledge, test the possibility of biologi-
cal computation, and develop bionic prostheses
useful to people who suffer from neurological
damage.
CONCLUSION
The success of the cognitive sciences, of AI, and
of the neurosciences removed the problem of the
nature of the mind and consciousness from the
category of the philosophical problems and put it
at the center of the attention of science.
The lack of ultimate answers on this issue is
in part due to the poor complexity of the current
articial systems in comparison with the complex-
ity of the brain, making it impossible to compare
articial and human performance and features.
Therefore, a huge amount of work is still to be
done, but some important courses have been drawn
out, both in the eld of the development of the
software simulation of intelligent behavior, and
in the eld of the development of bionic systems
that reproduce the neurophysiological structure
of the brain.
Also, theoretical physics has still to yield an
ultimate answer on its fundamentals in order to
clarify the role of the person in the objective re-
ality in such a way as to shed light on the nature
of consciousness.
Despite the difculty of the problems that
neurosciences, informatics, and physics have to
solve in this frame, the quantity of scientic ma-
terial related to the issue of consciousness and to
the implementation of AI hardware and software
systems has grown exponentially during the last
few years. This trend will certainly contribute to
consider the birth of a real articial mind and the
solution of the mystery of consciousness as not
so far and not so impossible events.
ACKNOWLEDGMENTS
I feel deeply indebted to all the members of the
Living Networks Lab (D. Rossetti, G. Cino, D.
Marino, and many others), and to Professor A.
L. Vescovi (SCRI DIBIT S. Raffaele, Milan) and
Professor G. Degli Antoni (University of Milan)
for their valuable support.
REFERENCES
Akin, T., Naja, K., Smoke, R. H., & Bradley, R.
M. (1994). A micromachined silicon electrode for
nerve regeneration applications. IEEE Transac-
tions of Biomedical Engineering, 41, 305-313.
Andrews, D. L., & Bradshaw, D. S. (2005). Laser-
induced forces between carbon nanotubes. Optics
Letters, 30(7), 783-785.
Canepari, M., Bove, M., Mueda, E., Cappello, M.,
& Kawana, A. (1997). Experimental analysis of
neural dynamics in cultured cortical networks and
transitions between different patterns of activity.
Biological Cybernetics, 7, 153-162.
Carmena, J. M., Lebedev, M. A., Crist, R. E.,
O’Doherty, J. E., Santucci, D. M., Dimitrov, D. F.,
et al. (2003). Learning to control a brain-machine
interface for reaching and grasping by primates.
M.A.L. PLoS Biology, 1, 193-208.
Chalmers, D. (1995). Facing up the problem of
consciousness. Journal of Consciousness Stud-
ies, 2(3), 200-219.
Chalmers, D. (1997). The conscious mind: In
search of a fundamental theory. Oxford Univer-
sity Press.
92
Articial Mind
De Marse, T. B., Wagenaar, D. A., & Potter, S. M.
(2002). The neurally controlled articial animal:
A neural computer interface between cultured
neural networks and a robotic body. Proceedings
of SFN 2002, Orlando, FL.
Egert, U., Schlosshauer, B., Fennrich, S., Nisch,
W., Fejtl, M., Knott, T., et al. (2002). A novel
organotypic long-term culture of the rat hippo-
campus on substrate-integrated microelectrode
arrays. Brain Resource Protocol, 2, 229-242.
Fromherz, P. (2002). Electrical interfacing of nerve
cells and semiconductor chips. Chemphyschem,
3, 276-284.
Fromherz, P., Muller, C. O., & Weis, R. (1993).
Neuron-transistor: Electrical transfer function
measured by the patch-clamp technique. Physical
Review Letters, 71.
Fromherz, P., Offenhäusser, A., Vetter, T., & Weis,
J. (1991). A neuron-silicon-junction: A Retzius-
cell of the leech on an insulated-gate eld-effect
transistor. Science, 252, 1290-1293.
Fromherz, P., & Schaden, H. (1994). Dened
neuronal arborisations by guided outgrowth of
leech neurons in culture. European Journal of
Neuroscience, 6.
Gao, G., Cagin, T., & Goddard, W. A., III. (1998).
Energetics, structure, mechanical and vibrational
properties of single walled carbon nanotubes
(SWNT). Nanotechnology, 9, 184-191.
Garcia, P. S., Calabrese, R. L., DeWeerth, S. P.,
& Ditto, W. (2003). Simple arithmetic with ring
rate encoding in leech neurons: Simulation and ex-
periment. Proceedings of the XXVI Australasian
Computer Science Conference, 16, 55-60.
Hagan, S., Hameroff, S., & Tuszynski, J. (2002).
Quantum computation in brain microtubules?
Decoherence and biological feasibility. Physical
Reviews E, 65.
Hameroff, S. R., & Penrose, R. (1996). Orches-
trated reduction of quantum coherence in brain
microtubules: A model for consciousness? In
S. R. Hameroff, A. W. Kaszniak, & A. C. Scott
(Eds.), Toward a science of consciousness: The
rst Tucson discussions and debates (pp. 507-540).
Cambridge, MA: MIT Press.
Hofstadter, D. R. (1979). Gödel, Escher, Bach: An
eternal golden braid. New York: Basic Books.
Hofstadter, D. R., & Dennett, D. C. (1981). The
mind’s I: Fantasies and reections on self and
soul. New York: Basic Books.
Hopeld, J. J. (1984). Neural networks and physical
systems with emergent collective computational
abilities. Proceedings National Academy of Sci-
ences US, 81.
John, A., Wheeler, J. A., & Zurek, W. H. (1983).
Quantum theory and measurement. Princeton
University Press.
Josephson, B. D., & Pallikari-Viras, F. (1991).
Biological utilisation of quantum nonlocality.
Foundations of Physics, 21, 197-207.
Katura, H. (1999). Optical properties of single-
wall carbon nanotubes. Synthetic Metals, 103,
2555-2558.
Lindner, J. F., & Ditto, W. (1996). Exploring the
nonlinear dynamics of a physiologically viable
model neuron. AIP Conference Proceedings, 1,
375-385.
Lovett, B. W., Reina, J. H., Nazir, A., Kothari,
B., & Briggs, G. A. D. (2003). Resonant transfer
of excitons and quantum computation. Physics
Letters A, 315, 136-142.
Maher, M. P., Pine, J., Wright, J., & Tai, Y. C.
(1999). The neurochip: A new multielectrode de-
vice for stimulating and recording from cultured
neurons. Neuroscience Methods, 87, 45-56.
93
Articial Mind
Marx, A., & Mandelkow, E. M. (1994). A model
of microtubule oscillations. European Biophysics
Journal, 22(6), 405-421.
Matsuno, K. (1999). Cell motility as an entangled
quantum coherence. BioSystems, 51, 15-19.
Minsky, M. (2006). The emotion machine: Com-
monsense thinking, articial intelligence, and the
future of the human mind. Simon & Schuster.
Penrose, R. (1994). Shadows of the mind. Oxford
University Press.
Pizzi, R., de Curtis, M., & Dickson, C. (2002).
Evidence of chaotic attractors in cortical fast
oscillations tested by an articial neural network.
In J. Kacprzyk (Ed.), Advances in soft computing.
Physica Verlag.
Pizzi, R., Fantasia, A., Gelain, F., Rossetti, D., &
Vescovi, A. (2004a). Behavior of living human
neural networks on microelectrode array support.
Proceedings of the Nanotechnology Conference
and Trade Show 2004, Boston.
Pizzi, R., Fantasia, A., Gelain, F., Rossetti, D.,
& Vescovi, A. (2004b). Non-local correlations
between separated neural networks. Proceedings
of the SPIE Conference on Quantum Information
and Computation, Orlando, FL.
Pizzi, R., Rossetti, D., Cino, G., Gelain, F., &
Vescovi, A. (in press). Learning in human neural
networks on microelectrode arrays. BioSystems.
Potter, S. M. (2001). Distributed processing in
cultured neuronal networks. In M. A. L. Nicolelis
(Ed.), Progress in brain research. Elsevier Sci-
ence B.V.
Reger, B., Fleming, K. M., Sanguineti, V., Alford,
S., & Mussa-Ivaldi, F. A. (2000). Connecting
brains to robots: An articial body for studying
the computational properties of neural tissues.
Articial Life, 6, 307-324.
Ruaro, M. E., Bonifazi, P., & Torre, V. (2005).
Toward the neurocomputer: Image processing and
pattern recognition with neuronal cultures. IEEE
Transactions on Biomedical Engineering, 3.
Schrödinger, E. (1956). Science and humanism.
Cambridge University Press.
Searle, J. R. (1980). Minds, brains, and programs.
In The behavioral and brain sciences (3). Cam-
bridge University Press.
Sept, D., Limbach, H.-J., Bolterauer, H., & Tuszyn-
ski, J. A. (1999). A chemical kinetics model for
microtubule oscillations. Journal of Theoretical
Biology, 197, 77-88.
Stapp, H. (1993). Mind, matter, and quantum
mechanics. Springer-Verlag.
Tuszynski, J. A., Trpisova, B., Sept, D., & Sataric,
M. V. (1997). The enigma of microtubules and
their self-organizing behavior in the cytoskeleton.
BioSystems, 42, 153-175.
Wang, Y., Kempa, K., Kimball, B., Carlson, J. B.,
Benham, G., Li, W. Z., et al. (2004). Receiving
and transmitting light-like radio waves: Antenna
effect in arrays of aligned carbon nanotubes. Ap-
plied Physics Letters, 85, 2607-2609.
Wigner, E. (1961). Remarks on the mind-body
question. In I. J. Good (Ed.), The scientist specu-
lates. London: W. Heinemann.
Wigner, E. (1972). The place of consciousness
in modern physics. In C. Muses & A. M. Young
(Eds.), Consciousness and reality. New York:
Outerbridge & Lazard.
Citations (0)
References (52)
Article
Jan 1995
Show abstract
Chapter
Jan 1995
Show abstract
Article
Apr 1982
John J Hopfield
Show abstract
Article
Jan 2007
Stuart Hameroff
Show abstract
Article
Jan 2003
Article
Jan 1984
Show abstract
Article
Nov 1981
Article
Jan 1980
Show abstract
Article
Mar 1996
Article
Jan 1996