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Published online: June 30, 2019
J o u r n a l o f I n t e g r a t i v e N e u r o s c i e n c e
J. Integr. Neurosci. 2019 vol. 18(2), 181–185
©2019 Marx and Gilon Published by IMR Press. All rights reserved.
Short communication
The tripartite mechanism as the basis for a biochemical
memory engram
Gerard Marx1and Chaim Gilon2,∗
1MX Biotech Ltd., Jerusalem 95744, Israel
2Institute of Chemistry, Hebrew University, Jerusalem 91904, Israel
*Correspondence: gilon@vms.huji.ac.il (Chaim Gilon)
DOI: 10.31083/j.jin.2019.02.6101
This is an open access article under the CC BY-NC 4.0 license(https://creativecommons.org/licenses/by-nc/4.0/)
In this paper, we address the enigma of the memory en-
gram, the physical trace of memory in terms of its composi-
tion, processes, and location. A neurochemical approach
assumes that neural processes hinge on the same terms
used to describe the biochemical functioning of other bio-
logical tissues and organs. We define a biochemical pro-
cess, a tripartite mechanism involving the interactions of
neurons with their neural extracellular matrix, trace met-
als, and neurotransmitters as the basis of a biochemical
memory engram. The latter inextricably link physiological
responses, including sensations with affective states, such
as emotions.
Keywords
Cognitive information; affective biochemistry; trace metals; neuro-
transmitters; extracellular matrix; memory engram; emotive memory
1. Introduction
The notion that the basis for memory was due to physical
changes in the brain, was first proposed by Semon (∼1900), who
also coined the term “engram” to refer to the physical trace of
memory. However, subsequent generations of neuroscientists
have not succeeded in localizing the trace in the brain: the “en-
gram” seems to be distributed throughout the brain (see Lashley,
1950).
Contemporary views on the mechanisms of memory and what
the engram means have proposed that dendritic spines represent
the basic unit for memory storage, or suggested that memory re-
trieval involved gene expression leading to protein synthesis and
structural modifications of the synapse, or the idea that increased
electrodynamic signaling at the synapses was key to the emergence
of memory and learning, while retaining the engram at the level
of cell morphology (cell ensembles, spines, synapses) (Poo et al.,
2016).
Similarly, others hypothesized that clusters of stationary, lo-
cal and permanent electrical pulses are the signatures of endur-
ing memories which are imprinted through nonsynaptic plasticity
(Cacha et al.,2017). None considered molecular scaled processes
or chemo-dynamic signaling.
2. Tripartite mechanism of memory
Neuroscientists from the time of Cajal and Hebb enunciated
a theory of “synaptic plasticity” where the basis of learning and
memory was ascribed to the increased number and functionality
of neural synaptic connections (Kandel et al.,2012;Mishkin and
Appenzeller,1987;Squire and Kandel,2008). In modern clinical
medicine and neural biology, it is generally accepted that neural
mental processes are based on chemical processes (Brady et al.,
2011). But what are the details?
In a series of papers, Marx and Gilon (2012,2013) suggested a
biochemical tripartite mechanism whereby neurons could encode
the cognitive information incoming from the senses, into a chem-
ical code that serves as a basis for the molecular basis of memory.
They outlined the concept and graphic representation of the tri-
partite mechanism describing chemical reactions involved in var-
ious processes that underlie the tripartite mechanism. It specifies
that the neurons interact with the surrounding neural extracellular
matrix (nECM) with dopants (trace metals and neurotransmitters
(NTs) to generate a biochemical neural code as “cognitive units of
information” (cuinfo) within the nECM.
Hebb (1949) enunciated a theory of “synaptic plasticity” as the
basis of learning and memory, ascribed to the increased number
and functionality of neural synaptic contacts, a “reverberating cir-
cuit” which is still popular among neuroscientists (Kandel et al.,
2012,2014). Subsequently, Hebb’s was accused of seven “sins”;
failing to address many issues critical to modeling neural mem-
ory (Arshavsky,2006). To redeem these “sins”, we offered the
tripartite mechanism whereby cuinfo (the neural “memory mate-
rial”) are encoded as metal-centered complexes within the nECM,
around the neurons. .
Marx and Gilon (2014) described combinatorially diverse en-
coding options (multinary) with > 10 trace metals and > 90 neu-
rotransmitters (NTs) for “flavoring” cuinfo with emotive tags used
for retrieval. We pointed out that the NTs are a class of molecules
synthesized and secreted by neurons that elicit emotive reactions,
concomitant with physiologic responses (i.e., entangled). Thus,
NTs can be considered as the molecular embodiments of “sensa-
tions” recalled as “emotions”.
Marx and Gilon (2016) described a physiologically credible
code for emotions psychic states (emotions) as well as physiologic
responses (e.g., sensation). Both are encoded with biomodulators
(neurotransmitters) which can bind to metal-centered cuinfo, lit-
Figure 1. Chemo-graphic representations of the reaction of an nECM
binding site for a metal cation, an "address". The binding of a NTs to
the metal-centered cuinfo confers an emotive context (Adapted from
Marx and Gilon,2016).
erally embodying “emotional memory”. The evolution of a brain
operating in emotive and logical modes of memory, suggests a
“phase” diagram. It starts with the (emotive) signaling of feel-
ings with biomodulator molecules. Logic emerges as a new talent,
coincident with the evolution of skull size, brain capacity, neu-
ral signaling modalities and molecular complexity (Tosches et al.,
2018). Thus, the tripartite mechanism permits a chemical perspec-
tive on the evolution of “logical” and “emotive” modes of neural
memory. As well, the proposed tripartite mechanism describes a
chemical code for affective states that is not linguistic but presents
the molecular correlates of the epigenetic “engram” (Cacha et al.,
2017) that render the neural synapse operative for the function of
recall.
We expect that the molecular-scale encoding/decoding process
must be faster than the rate of neural firing (<100 ms) (see Ta-
ble 1). Also, the rates of macromolecule elongation or translation
are too slow to account for the speed of memory acquisition or
recall. Clearly, the rates of protein synthesis and DNA/RNA elon-
gation are too slow to serve as effectors of neural memory, which
must be faster than 0.1 sec, at least for short term memory. Thus,
structural modifications of the synapse do not account for short-
term memory formation. By contrast, metal complexation reac-
tions and the binding of NTs are fast reactions and do not require
high energy (Brady et al.,2011).
The neuron is suspended in a web of nECM. It instigates the
formation of metal-centered complexes within the nECM, which
are equivalent to “cuinfo” (This is because cuinfo has already been
defined), represented as chemo-graphic icons (see Fig. 1). This is
a very rapid process that requires little energy (see Table 1). For
the purposes of our brief report, we merge all cellular entities (as-
trocytes, glia as well as neurons) under the umbrella term of “neu-
ron”. Admittedly, astrocytes and glia cells are important for proper
neural functioning. But their synaptic contacts must be considered
in light of the fact that most neural dendrites do not make synaptic
contact with other neurons, but establish non-synaptic signaling
(ephaptic) contacts through the nECM (Arellano et al.,2007;Vizi
et al.,2010;Vizi,2013).
Table 1. Kinetics of various neural processes.
Process Time scale
Protein chain synthesis 10−1sec per amino acid
RNA elongation 10−2sec per base
DNA elongation 10−3sec per base
Neural firing rate 10−2sec
Neuro-electric impulse 1-100 m/sec
Neural GPCR receptor diffusion 10−1to 10−3
µ
m2sec−1
Ca+2diffusion in nECM 2.3 ×10−6cm2/s
Molecular binding events 10−7sec
Protein turnover (replacement) 3 months
Mosaic diffusion over neural surface 10−1to 10−3
µ
m2/sec
Ionic memory chip byte encoding 10−7sec
3. The neural extracellular matrix
Unlike depictions in most textbooks, neurons are not “naked”.
They are surrounded by a web of hydrated glycosaminoglycans
(GAGs) termed neural extracellular matrix (nECM) comprised
of a mix of polysaccharides (chondroitin, hyaluronates heparans,
GAGs) admixed with accessory proteins (i.e. tenascins, colla-
gen) (Bogoch, 1968; Cserr, 1986; Iwata and Carlson, 1993;
Kamali and Nicholson, 2013; Schmitt et al., 1969). Thus, the
neuron is not “naked”, but clothed in a filigree of GAGs. The
intimate con-tacts of the extended neural surface with the nECM
permits signal recognition between the neuron and the nECM
(Katchalski, 1992). It should be noted that the nECM is much
less prone to biodegra-dation than proteins and protected from
such decay (Golgolla et al., 2009). For example, the backbone
of DNA is comprised of a protected polysaccharide (poly-
deoxyribose) which lasts a life-time.
More than ten trace metal cations are dispersed within the gross
brain tissue as well as within the individual neurons to mM levels
(Becker et al., 2005; Becker, 2010; Popescu et al., 2009). Most
metals were found within the neuron, particularly the nucleus.
They are also present in the nECM, at levels ranging from 10−6
to 10−9 M. Trace metals cations are transported into the neurons
via metallothioneins (Fischer and Davie, 1998; Kägi and Schäf-
fer, 1988; Suzuki et al., 1993). In the nucleus, they participate
in the processing of DNA into RNA into proteins which also in-
volves tubulins (Watson et al., 2013). In the neuron’s cytoplasm,
they are involved in cell metabolism. They are also loaded into
vesicles for eventual release. To the extent that intracellular met-
als are collected into vesicles to be expelled into the extracellular
nECM, both metal pools can be considered to work in combina-
tion with the nECM. As well the chemistry of the nECM (many
anionic pockets) predisposes it to react with metal cations. In ef-
fect, the neuron employs the nECM as a matrix wherein it can use
metal cations and NTs to encode cognitive information, by ejecting
vesicles containing metal cations and NTs, to form cuinfo.
Monovalent metals form short-lived, unstable complexes. Di-
valent and polyvalent metals form complexes that are inherently
more stable. For example, we provide chemo-graphic represen-
tations of cuinfo undergoing “tagging” and crosslinking reac-
tions, essential for indexing cuinfo for organized storage and re-
trieval. Monovalent metals form relatively unstable nECM com-
182 Marx and Gilon
plexes (might be associated with short term memory); whereas
polyvalent metals are generally more stable (might be associated
with long term memory). Metal complexes of “memory units” are
much more stable than any achieved by H-bonds in an aqueous en-
vironment. Thus, the suggestion that H-bonding encode memory
(Amtul and Rahman, 2016) is not appropriate. The entire neural
net is immersed in an aqueous environment. Indeed, hydrogen
bonds are involved in establishing the structures of proteins and
DNA. But hydrogen bonding for polysaccharides in an aqueous
system is too labile to serve as a coding system. Also, hydrogen
bonding is bereft of emotional content. It is like the binary code
locked in 2-D materiality.
The terms "neurotransmitter" and "biomodulator" are often
used interchangeably. But they are in fact distinct classes of signal-
ing molecules (Reith, 2002; Roshchina, 2010). The original bacte-
ria signaling modulators comprise the nine modulators including
biogenic amines and amino acids, with molecular weights smaller
than 200 Daltons. Neuropeptides, which were employed by later
evolved neural creatures, are generally heavier than 200 Daltons
and may act directly in synaptic as well as non-synaptic signaling.
They can also operate as switches which turn on or off signals in-
stigated by the original bacterial biomodulators (Roshchina, 2010).
It is an established fact that NTs inextricably link (entangle) phys-
iologic responses (e.g., sensations) with affective states, termed
“emotions” (Mesulam, 1998).
The mature neuron is surrounded by nECM during its develop-
ment. To “write”, the stimulated neuron containing dopants ejects
into the already existing surrounding nECM (Budnik et al., 2016;
Davis and Muller, 2015; Kay et al., 2006; van Niel et al., 2018). The
neuron thereby forms sets of metal-centered complexes within the
surrounding nECM, therein rapidly encoding cuinfo (see Fig. 2).
To the extent that the cuinfo is physical (chemical complexes), they
Figure 2. The neuron is surrounded by nECM (GAG lattice not shown)
which serves as a neurochemical "library" wherein units of encoded
memories are stored as cuinfo. The colored boxes representing the
individual cuinfo described in Figure 1, are not to scale, as they are of
molecular dimension (i.e. 10 nm) compared to the 10-100
µ
m scale
of the neuron and its parts. The different colors indicate complexes
with different combinations of NTs and metal cations.
embody the “engram”, the physical trace of memory first hypothe-
sized by Semon (Kim et al.,2018;Lashley,1950;Santoro and Fran-
kland,2014;Schacter,2011;Semon,1923). The epigenetic cuinfo
(engrams) of memory (Cacha et al.,2017) are distributed over the
whole brain as a guiding template. While there may be specific
cells involved in encoding and retrieving the engrams (Roy et al.,
2016), the tripartite mechanism posits that the physical traces of
the engram are distributed in the nECM around the neurons (see
Fig. 2).
To “read” or decode the cuinfo, the neuron employs at 3 types of
“sensors”, aggregates of proteins (i.e. mosaics embedded within
its membrane, examples being GPCR mosaics, K2P channels and
acetylcholine receptors (AcCholR)), all which number many thou-
sands per neuron (Corringer et al.,2012;Juliano and Haskill,
1993;Katchalski,1992;Lobmaier et al.,2001;Zhang et al.,2012).
They perform as mobile chemo-dynamic sensors (reported diffu-
sion: 10−1to 10−3
µ
m2/sec) (Choquet and Triller,2013;Triller
and Choquet,2005), which transform the chemical code of cuinfo
around individual neurons, into neural signals (Roy et al.,2016),
ultimately processed by the neural net and experienced as coherent
emotive memory. The neural code which forms memory is insti-
gated by the signal input of cognitive information from each of the
senses, with each input encoded by a tripartite mechanism. The
neural net integrates (consolidates) all the tripartite units to gen-
erate a composite mental pattern manifested as comprehensible,
emotive memory.
4. Discussion
Earlier attempts have been made to locate the engram in a brain
structure concluding that the engram is not localized, but dis-
tributed throughout the brain (Lashley,1950). Others identified
groups of cells termed “engram neurons”, as their electrical fir-
ing correlated with recall. Some considered “resonating circuits”
as the basis of the engram. That is, neural synaptic contacts are
“strengthened” when a signal continues to impact them. These cir-
cuit provides stronger and stronger resonant signals as time pass.
All these are predicated on a model of exclusive synaptic signal-
ing between neurons, to which objections have been raised (Ar-
shavsky,2006). In particular, it ignores the non-synaptic (ephap-
tic, volume transmission) signaling that is also a feature of neural
communication (Agnati et al.,2004;Vargova and Sykova,2014;
Vizi et al.,2010;Vizi,2013).
The concept of “resonance” is of interest to chemists as it re-
lates the idea that in some molecules the electrons are not located at
a specific bond (i.e. conjugated double bonds) but can be “delocal-
ized” (smeared out). The “resonance” phenomenon is not limited
only to molecules with conjugated double bonds (e.g. benzene)
but also to electron-rich ligands (e.g. acetate, sulfate, etc) and
their metal complexes. These form the addresses in the nECM,
which attract and bind the trace metal cations much like proteins
and other electron-rich substrates do ( c f., Lehninger,2008;Eom
and Song,2019). As applied to the tripartite mechanism, “res-
onance” or “de-localization” means that the individual cuinfo is
located in the nECM near a neuron (see Fig. 2), but that the re-
lated set of cuinfo constituting memory, are distributed in differ-
ent anatomic compartments of the brain (Huk and Hart,2019). The
neural circuit consolidates these into a comprehensible pattern, ex-
Volume 18, Number 2, 2019 183
perienced as memory. Thus, the engram is both local and “delo-
calized”. That is, the basic memory units (cuinfo) are pixels that
make up the pattern of memory are located near a specific neuron
(see Fig. 2). But the set of cuinfo which make up the totality of
the memory pattern is stored in different anatomic compartments
of the brain. Thus, excising one particular region may not result
in total memory loss or identify a specific locale where it is stored
(see Lashley,1950;Poznanski et al.,2019;Pribram and Meade,
1999).
Trace metals are quite reactive in terms of binding to electron-
rich sites in proteins and GAGs and are critical for the activities of
enzymes (Eom and Song,2019). When released into the nECM,
the metal cations interact with the electron-rich sites and help
bind NTs to form relatively stable metal complexes (i.e. cuinfo),
much like their complexation with enzymes (Warshel and Levitt,
1976). Some polyvalent metal complexes could also engage in re-
dox (Fenton) reactions, with attendant covalent modifications in-
volving new condensation or cross-linking reactions that further
increase stability. For example, metals with multiple oxidation
states (Al, Co, Cu, Fe , Mn), can engage in Fenton reactions to
generate reactive hydroxyl (OH.) radicals (Kim et al.,2007;Ward-
man and Candeias,1996). In turn, these oxidize and modify (tag
and cross-link) the cuinfo. Thus, such reactions greatly enlarge
the encoding repertoire available to the neuron and affect the con-
tent and stability of the derived cuinfo (Das et al.,2015;Marx and
Gilon,2013).
5. Concluding remark
We have identified NTs as the elicitors of physiologic responses
entangled with emotive states, as the basis for a biochemical mem-
ory engram. In short, the tripartite mechanism provides a physio-
logically credible rationale for the phenomenon of emotive mem-
ory, consonant with the morphology of the neuron and the mate-
rials available to it.
Acknowledgment
We appreciate Randy Gallistel’s remarks, which drew our at-
tention to “memory” as the proper focus of our speculations. We
also thank a reviewer for suggesting improvements and additional
references.
Conflict of interest
GM is a founder of MX Biotech Ltd., with the commercial goal
to develop new “memory materials”.
Notwithstanding, the ideas forwarded here are scientifically
genuine and presented in good faith, without commercial cloud-
ing of the concepts expressed therein.
Submitted: February 12, 2019
Accepted: June 10, 2019
Published: June 30, 2019
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