X-Message-Number: 10418
Date: Sat, 12 Sep 1998 13:13:40 -0700
From: Jeff Davis <>
Subject: Holographic memory in ye olde braine panne

For Thomas Donaldson and the Cryonet,

	Thomas, you wrote me about the scrambled salamander brain experiments,
wherein you said:

	 "Years ago an interesting
experiment was done with salamanders: their cortex was removed,
chopped into several pieces, and then replaced. The salamander was
comatose for some time afterwards, but then recovered its memory.
(Cf A Hershkowitz et al "The acquisition of dark avoidance by
transplantation of the forebrain of trained newts", BRAIN RESEARCH
48(1972) 366ff; and P Pietsch, "Scrambled salamander brains: a test
of holographic theories of neural program storage" ANATOMICAL RECORD
172(1972) 383)."

	I followed it up, and found the scrambled salamander brain experiments
documented in greater depth than one could hope for, and quite
entertainingly, by Paul Pietsch in his book "Shufflebrain", which can be
found at:



	Looking for more recent work (the "Shufflebrain" work dates back to 1972),
I found on the internet:

	Comparison between Karl Pribram's "Holographic Brain Theory" and
                 more conventional models of neuronal computation

                                By Jeff Prideaux
                        Virginia Commonwealth University


	Most recently I found the following, which I enclose in its entirety.  It
starts out as an optical computing article but transitions rapidly into a
neuroholograpy article.  As is often the case, the last paragraph is rich:

	        Optical Processing & Computing

        Biomorphic neural networks are new links for information processing

        In 1985 researchers from Caltech and the University of Pennsylvania
(Penn) drew
        attention to a valuable link between optics and the neural paradigm
for information
        processing. In a seminal paper that appeared in Applied Optics 1,
they demonstrated
        the advantages offered by optics for implementing the large number of
        interconnections between neurons in an artificial neural network
that functioned as
        associative memory. Since that time, the use of optical
interconnects in hardware
        implementation of neural net architectures has developed into an

        Now Nabil Farhat and his students at Penn are finding another
potentially useful link
        between optics and neurocomputing, this time heavily involving
nonlinear dynamical
        systems, that could prove beneficial for all three fields. The new
link is a conceptual
        connection between holography and neurocomputing which they have
        "neuroholography." The idea of a "holographic model of the brain"
is not new. It has
        been around since the early sixties. What is new now, is that
Farhat's concept of
        neuroholography emerges directly from a biomorphic (biology-like)
model neuron
        conceived to mimic the behavior of cortical neurons2. (The cortex
is that part of the
        brain that carries out all higher-level brain functions like
cognition and complex motor

        Their biomorphic-model neuron exhibits phase-locking between
periodic firing
        patterns it produces and a periodic potential, that forms under
certain "input"
        conditions, at the neuron's "hillock." The hillock is that part of
a biological neuron
        where action potentials (nerve impulses or spikes) are initiated to
be broadcast from
        the neuron via its axon and axon branches to other neurons. When
the incident spike
        wavefront (ISW)-which is the aggregate of all trains of nerve
impulses (spike trains)
        impinging on the neuron from other neurons in a prescribed time
window (the input)-is
        coherent or partially coherent, i.e. the trains of incident nerve
impulses from other
        neuron are correlated or partially correlated, the neuron's
dendrites act to form a
        periodic potential at the hillock reflecting the coherence in the ISW.


           Frequency of Driving Signal, fs [Hz]

        The appearance of such "periodic activation" dramatically alters
the neuron's
        behavior as an information processing element by enabling it to
exhibit a variety of
        complex periodic firing patterns that can be phase-locked to the
periodic activation or
        can be chaotic depending on the nature of the periodic activation
(e.g. its amplitude
        and frequency if it is cosinusoidal). Under these circumstances the
neuron acts as a
        detector and encoder of coherence in its input. When however the
ISW is incoherent,
        the activation potential becomes steady or slowly varying in time
and the neuron's
        behavior reverts to the usual sigmoidal dependence of firing
frequency on activation
        without any phase-locking involved. Their model neuron is able thus
to produce a
        wide range of phase-locked, nonphase-locked, and chaotic firing
modalities and to
        bifurcate between them depending on the nature of the ISW and the
        activation potential. For this reason they are calling it the
"bifurcation model neuron."
        It is interesting to note that behavior very similar to that of the
bifurcation model
        neuron has been observed in several biophysical experiments
performed with
        periodically stimulated biological neurons and excitable membranes
(see reference 3
        for example).

        Numerical simulations of a network of bifurcation neurons, show
that neurons in the
        network tend to usually phase-lock their firing and this results in
the emergence of
        coherence in the ISWs of the individual neurons. The feasibility of
something similar
        occurring in cortical networks of the brain is supported by the
relatively recent
        discovery of long-range oscillations and correlations in the
spiking activity and
        local-field potentials (extracellular potentials) in the visual
cortices of cat and
        monkey (see for example reference 4). This discovery has aroused
intense interest in
        understanding its underlying neuronal mechanism and in exploring
        applications that seek to emulate higher-level brain functions such
as scene
        segmentation, feature-binding, cognition, complex motor control and
        development of dynamical computing (computing with all three types
of attractors:
        point, periodic, and chaotic exhibited by high-dimensional
dynamical systems like the

        Bifurcation model neurons and networks fit well with an oscillation
theory of cortical
        networks which hypothesizes that timing, phase-locking,
synchronicity, and chaos
        might underlie higher-level brain functions. At first glance such
hypothesis may induce
        skepticism. How could timing, phase-locking, and synchronicity
occur in cortical
        networks when biological neurons are known to be noisy processing

        Noise in cortical neurons, as in other neurons, originates in the
synapses, the very sites
        of communication between neurons. Synaptic noise has two causes.
One is the
        probabilistic nature of "exocytosis" that describes events leading
to the release of
        neurotransmitter molecules into the synaptic cap. These events
occur with probability
        of less than one, meaning that not every nerve impulse reaching the
terminal point of
        an axonal branch forming a synapse junction with another neuron,
succeeds in the
        release of neurotransmitters into the synaptic gap separating a
presynaptic neuron from
        a postsynaptic neuron. Neurotransmitter molecules released into the
synaptic gap
        activate ionic channels in the postsynaptic membrane allowing ions
to flow into the
        postsynaptic neuron altering thereby its potential. This,
incidently, is the basic
        mechanism for electrochemical communication employed by neurons.
The second
        cause of synaptic noise is the stochastic nature of the opening and
closing (gating) of
        activated ionic channels.

        Recent work carried out by Farhat and Hernandez5, shows bifurcation
        networks exhibit a phenomenon they call clustering that neutralizes
the noise caused
        by the probabilistic nature of exocytosis. In reference 5 they show
that an externally
        driven network of bifurcation neurons exhibits under certain input
        clustering, synchronicity, and phase-locking. The processing
elements (neurons) in
        their network, group themselves into clusters of unequal size.
Neurons within a cluster
        fire in unison, i.e. neurons within a cluster are synchronized.
Different clusters have
        distinct period-m firing patterns but all such patterns are
phase-locked i.e. there is a
        fixed temporal relation between them. (Period-m firing is cyclic
firing in which the
        neuron fires repeatedly a pattern of m spikes that are not
necessarily equally spaced).
        What this means for the probabilistic exocytosis issue, is that
neurons in such a
        network would be receiving at their synapses identical spike trains
from all other
        neurons belonging to the same cluster. The ISWs of the neurons are
now not only
        coherent but also highly redundant because of clustering. The
redundancy means that if
        exocytosis at one synapse does not occur when a nerve impulse
arrives, there is more
        than ample chance that the same spike in the identical spike train
arriving at another
        synapse will. This redundancy suppresses the noise caused by
        exocytosis and the effectiveness of suppression improves rapidly
with redundancy,
        i.e. with the size of the clusters.

        Farhat's group is now focusing their attention on noise caused by
the stochastic gating
        of activated ion channels: Ongoing modeling and simulation work is
        amazingly enough, that channel noise helps rather than hinders the
conversion of
        coherence in the ISW into a periodic activation potential at the
neuron's hillock
        because of Stochastic Resonance (SR). SR is a mechanism by which
noise in a
        bistable or thresholding process such as believed to exist in ion
channels or in
        excitable biological membranes, can amplify a weak periodic signal
rather than
        degrade it6.

        Withstanding further scrutiny, this latter result would validate
the bifurcation neuron
        concept which ignored synaptic noise from the start, and would go a
long way towards
        explaining the nature of the "neuronal code," i.e. the way cortical
neurons encode
        information they receive from other neurons while participating in
carrying out
        higher-level brain functions. Furthermore, the availability of a
periodic activation
        potential at each neuron can be viewed as a local reference signal
serving to
        phase-lock the periodic spike pattern produced by the neuron. This,
together with the
        possibility that the efficiency with which synapses transmit
information can be
        modified by the degree of correlation between the neuron's spike
train and the spike
        train received at each synapses from other neurons, i.e.
correlations between the pre-
        and post-synaptic spike trains, furnish the basic ingredients
needed for the formulation
        of a self-consistent neuroholography concept. This hopefully will
be useful for both
        optical information processing, neurocomputing, and for the optical
implementation of
        adaptive nonlinear dynamical systems.


        1. N. Farhat, D. Psaltis, A. Prata, and E. Paek, "Optical
implementation of the
        Hopfield Model," Appl. Opt., vol. 24, pp. 1469-1475, 1985.

        2. N. Farhat, S-Y Lin and M. Eldefrawy, "Complexity and chaotic
dynamics in a
        spiking neuron embodiment," in Adaptive Computing, S. Chen and J.
Caulfield, (Eds.),
        vol. CR55, pp. 77-88, SPIE, Bellingham, Wash., (1994).

        3. K. Aihara and G. Matsumoto, "Chaotic oscillations and
bifurcation in squid giant
        axon," in Chaos, A.V. Holden (Ed.), Princeton Univ. Press,
Princeton, N.J., pp.
        257-269, (1986).

        4. R. Eckhorn, et. al., "Coherent oscillations: A mechanism of
feature linking in the
        visual cortex," Biol. Cybern., vol. 60, pp. 121-130, (1988).

        5. N. Farhat and E. Del Moral Hernandez, "Recurrent neural networks
with recursive
        processing elements," presented at SPIE '96, Denver, August (1996).

        6. J. Collins, et. al., "Stochastic resonance without tuning,"
Nature, vol. 376, pp.
        126-135, July (1995).

                           SPIE Web Home | OE Reports Feb.

                   =A9 1997 SPIE - The International Society for Optical

  	Holographic storage and retrieval is an analog process.  The brain is an
analog processor. Holographic storage provides the ultimate in redundancy
by the "equipotential" distribution of the "data set", and consequently the
ultimate in durability/survivability.  Survivability is THE fundamental
evolutionary fitness criteria.

	I read somewhere--unfortunately I can't find it again--that cells from
tissues frozen with cryoprotectant and thawed, and then cultured, showed
survival rates between 50% and 95%.  Higher order structures might be
unable to recover viability, but individual cells did, in high percentages.
(Could someone help me to relocate the source for this?)

	How is this relevant for cryonics?  High cell survival rates and an
information storage paradigm characterized by high inherent durability,
bodes well for the prospect of memory/personality preservation.  Does this
"prove" anything?  Nope.  Should anyone ease up in their efforts to achieve
reversible suspension, damage-free suspension, or cell-repair technologies?
  I don't think so Tim!  Nevertheless, I find it quite heartening.

			Best, Jeff Davis

	   "Everything's hard till you know how to do it."
					Ray Charles			=09

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