Dr E. J. Gorzelanczyk
Dr P. A.Wozniak
July 1, 2001
Book Review:
Cambridge University Press 2001, 490 pages
About the reviewers:
Dr Edward J. Gorzelanczyk, Laboratory of the
Applied Research at the Department of Health Sciences, Karol
Marcinkowski Medical Academy, ul. Dabrowskiego 79, 60-529 Poznan, Poland
Dr Piotr A. Wozniak, SuperMemo R&D, SuperMemo
World, ul.R.Maya 1, 61-371 Poznan, Poland
Neuronal
Mechanisms of Memory Formation edited by Christian Hpan>lscher revolves largely around the concept of long-term
potentiation (LTP) and its implications for our understanding how memory
traces are formed and sustained. The book was written by a selection of authors
that represent many prominent personalities in the field of memory research
(incl. Nobel Prize winner Dr Eric Kandel). The book is composed of nineteen
independent papers with an introduction by Christian Hpan>lscher which lays ground for critical analysis of the most
pivotal questions, on which the current research on memory formation is focused.
All individual papers, without an exception, provide a very well-written,
carefully proofed, and well generalized analysis of a selected research problem.
Although independent authoring has resulted in a slightly repetitive treatment
of introductory themes, each chapter of the book can be picked for selective
reading and forms a coherent entity on its own. On the downside, the book does
not in any way discuss the role of sleep in memory formation and
optimization (research in this field, in authors opinion, could lay
ground for breakthroughs in
understanding high-level memory systems). Secondly, it is also regrettable that
the concepts of the spacing effect and spaced repetition in learning have not as
yet attracted much attention of researchers dealing with neuronal and molecular
aspects of memory. The fact that cognitive and behavioral memory research seems
to be run in disjoint research communities is thus also reflected in the book. A
notable exception is a paper by Lisman et al. that abounds in behavioral
parallels and illustrats the richness of potential inspiration. Similarly, the
paper by Matzel and Shors, although strongly criticized in this review, illustrates the
power of behavioral research in supporting or falsifying molecular and neural
theses.
Besides the introduction, concluding remarks, and a
somewhat scanty index, the book is divided into five sections that were clearly
molded to accommodate for individual authors interests. As a result, the
overall coverage may have lost on systematic analysis, but it has certainly
gained on the in-depth creative quality of individual articles. Consequently,
this is less of a student textbook, and more of a guiding light for further
research in the field.
In the introduction, Christian Hpan>lscher emphasized that memory research is inevitably guided by
the available methodology and historical implications. He noted the need to
expand the present research beyond its traditional focus on the LTP in the
hippocampus. He also noticed that one of the lesser studied aspects of memory
formation is its time dynamics. Similarly, HFS protocols which induce LTP in a
robust way still dominate the present research despite their being quite different from
the natural firing patterns. Slicing the hippocampus deprives it from natural
input from modulatory systems (one of the authors compared hippocampal slices to
memory circuits in the NREM state). Similarly confounding can be the removal of
inputs from the raphe nuclei or the locus coeruleus, which may act as master
trigger circuits of synchronized neural activity in sleep. More precise
classification of the forms of learning is needed for the analysis of the
underlying neural structures as learning protocols might be associated with
varying molecular mechanisms. Confusion in the protocol area may obscure the
implications of experimental findings in which varying learning techniques are
used to yield different results.
Here are selected highlights of research papers
included in the book
In
one of the most captivating articles, Edmund T. Rolls describes the three
types of neural networks underlying the function of the brain: pattern
associators, autoassociators and competitive networks. Simple description of
the architecture and the rules of operation of such networks are presented
in the context of learning, recall and saturation forgetting. Network
properties are discussed: capacity, generalization, error recovery, etc. A
role of non-linear NMDA properties in forming well-defined sparse memories
is hinted at. Competitive neural nets are shown to learn to categorize input
pattern vectors. Their role in removing input redundancy is presented.
Neural input subject to, what the author calls sparsification, can
later be efficiently applied to associative networks. Competitive networks
respond to correlations in input patterns and require no teaching stimuli.
As such they seem to be an excellent model for sensory filters. Upon this
neural network introduction Rolls goes on into demonstrating how this
rudimentary knowledge combined with LTP/LTD and/or LTP/LTD-like mechanisms
can greatly enhance our understanding of specific neural structures in the
brain. He presents an extensive analysis of the hippocampal circuitry and
how it can be modeled computationally with far reaching conclusions about
the importance of individual network components. For example, evidence is
presented that CA3 cells might operate as an autoassociative network
capturing episodic memories (a conclusion that differs slightly from a
similar one by Lisman et al.). Network properties combined with
neurohistological data can be used to estimate network capacity. For
example, rough approximation of autoassociative CA3 capacity based on the
number of inputs and the estimated sparseness was found to be 36,000
separate memories. Rolls also picks examples of cortical structures whose
function can be explained in terms of neural network types. For example,
face recognition in the temporal visual cortex is shown as implementable as
a multi-layer competitive network. Rolls successfully demonstrated the power
and applicability of neural computing and computational models
in bringing the understanding of the brain to new levels
John
Lisman at al. make an ingenious effort in bridging LTP research with
behavioral data in developing their model of short-term memory named the
Lisman-Idiart-Jensen model (later abbreviated to LIJ) in which the role of
theta and gamma oscillations in expressing a short-term serially-searchable
memory buffer is suggested. The model is then extended by an effort to
pinpoint individual hippocampal structures that might be involved in
short-term recall in word-list learning. Finally, the NMDA-mediated LTP is
presented as a step towards consolidating long-term memories. The authors
begin with a look at behavioral memory research in a historical perspective
through serial recall, recency and primacy effects, and the Atkinson-Shiffrin
short-memory buffer model. In the course of this analysis the authors fail
to emphasize the importance of mnemonic strategies on probability of recall
that has traditionally obfuscated research on short-term memory. This
ultimately leads to undermining some of the evidence presented in support
for their model. The authors quote research that supports the existence of
short-term memory buffer in PET and fMRI cortical scans which correlate
neural activity with memory loads in abstraction from attention levels. In
studying the effects of amnesia on the recency effect, the authors note that
hippocampal injury can selectively affect the pre-recency curve. However,
they conclude that the hippocampal region is required for LTM storage,
but not STM. This implication arrives at a time when, in support of their
own model, the authors could have equally well concluded that such
pre-recency curve deflection could indicate the damage to STM capacity in
hippocampal injury (lower capacity entails a more pronounced recency
effect). Interestingly, just a few pages later, the same authors note that
surprising cases of patients have been found that showed short-digit span
accompanied with unaffected long-term memory. Following this historical
review, the authors introduce the Sternberg probe-recognition task. The LIJ
model is presented as derived straight from the Strenberg task in which the
reaction time increases linearly with the list length. In 1966, Strenberg
suggested serial exhaustive scanning (SES) to explain this effect. However,
the authors draw a striking parallel between the reaction time increment (of
about 40 ms) in the Strenberg task and the gamma wave period at 25Hz
frequency. The LIJ model draws on this parallel by replacing a spatial
separation of neural patterns with a gamma-wave frequency-based temporal
separation of the same patterns. The model and its implications for
developing long-term memories have been analyzed by means of computer
simulation. The authors draw a bold conclusion on the link between the
magic seven and the fact that there are about seven gamma cycles per
one theta cycle. However, the dependence of the STM span on knowledge
representation extends magic seven far beyond the number of gamma
subcycles achievable at frequency limits even though the involvement of
cortical representations could argue for this to be a transgression beyond
the strictly short-term LIJ model. The repeat time in LIJ model is
determined via external theta input, while gamma oscillations arise from
feedback inhibition in which currently active patterns extinguish the
remaining memory traces until their own turn in the cycle. Using simulation
experiments, Jenson demonstrated feasibility of such superimposed
gamma-theta oscillations. Simulation also made it possible to explain the
phenomenon of multiple cueing. Incidentally, the authors use a
not-so-fortunate example to illustrate multiple cueing which refers to list learning. The
recall of the successor of N in the alphabet does not usually come easier after the
cue of LMN due to multiple cueing but rather from the fact that LMN or KLMN
are, for most people, the actual recall cues while O can be concluded as the
successor of N via reconstructive recall. In other words, the N-O link is
not weaker. It is simply rare to store it explicitly in memory (this can be
tested in each individual by simply registering the reaction time and
whether reconstructive recall could be identified). In
conclusion of their analysis, the authors make another bold proposition by
associating hippocampal layers with highly specific neural functions. The
autoassociative information is proposed to be stored in the recurrent
connections of the dentate, the heteroassociative inter-item links are found
in the recurrent collaterals of CA3, and the contextual input arrives at CA3
from the EC through the perforant path. Although the evidence for such a
claim is behavioral, derived from neural computing, and highly speculative,
the proposition itself provides a good focal point for further research that
should decide on its validity
Matthias
H.J. Munk presents a detailed and lucid analysis of possible functions of
synchronized neuronal activity in brain functions. The article begins with
separating the concepts of smart neurons and neuronal
assemblies.
Synchronized neuronal responses are shown as a code for relations in input
patterns. Gamma-frequency oscillations are of particular interest as they
correlate with pattern recognition and synchronized responses. The role of
the reticular formation in inducing synchronization is discussed. Separate
roles and modes of activity of stable feed-forward connections and plastic
reciprocal long-range tangential corticocortical connections are discussed.
Possible role of synchronized activity in massive reorganization of cortical
representation is found as of potentially monumental importance. A mechanism
for such a reorganization based on the time-dependent enhancement or
suppression of neural activity is outlined. The link between the cholinergic
systems, attention, learning and gamma waves is analyzed, esp. in the light
of determining the relative saliency and relevance of sensory input
patterns. In this context, the self-organizing nature of synchronized
activity is of particular interest for pattern recognition
Matzel
and Shors responded to the editors invitation to question the established
dogmas of memory research. In particular, LTP as the prime experimental
model of memory and learning has accumulated a collection of confusing
research outcomes that should make the scientific community pause and
reevaluate the role of LTP in learning. Most authors in the book accept LTP
as a valuable research model (e.g. Abraham, Cho, Rogan and Kandel, etc.).
Matzel and Shors took on a challenging task of proving LTP irrelevant in
associative learning. Unfortunately, the rich body of evidence presented in
their paper is highly flawed and the strongest counterevidence comes from
the areas that were hardly noticed in the presented book: spacing effect and
spaced repetition. Authors derive some of their evidence from the fact that
associative learning and its lasting expression does not require multiple
trials a claim refuted evidently by the existence of the optimum
spacing of repetitions in learning. In the section entitled Associative
memories can persist indefinitely the authors quote everyday life
evidence, anecdotal evidence, as well as the three most popular
misconceptions related to forgetting (1) using reconstructive recall,
implicit memories, and retrieval failure as a typically abductive evidence for
the persistence of memories, (2) confusing the dynamics of procedural
forgetting with the dynamics of declarative forgetting, and (3) using the
stochastic nature of forgetting to formulate inductive theses without
probabilistic evidence. Rich literature references mostly predate the
currently established research trends. Similarly flawed is the comparison of
the acquisition kinetics of LTP and associative learning. The dynamics of
the development of synaptic potentiation will depend on stimulation
protocols, which indeed in the case of LTP are often highly
un-physiological. The authors write that LTP exhibits strength through
repetition which is not a defining characteristic of associative
learning. Here the volatile short-term instantaneous acquisition of
memories in associative learning is confused with its long-term expression
that is always trial-dependent. In contrasting the spacing effect of
associative learning with the effect of inter-stimulation intervals in LTP,
the authors again confuse the short-term conditions needed to establish LTP
with the conditions that are suitable for retaining memories for months and
years. Memory reacquisition was used as another piece of evidence. It is
known that relearning takes much less effort than learning anew. Matzel and
Shors note that LTP can produce reacquisition savings only if relearning
occurs before LTP decays back to the baseline. This fact should actually be
used to draw the opposite conclusion, i.e. in support for the value of LTP
for LTM research. Long-term memories in declarative learning behave in the
exactly same manner. Relearning brings benefit only within a strictly
limited period of time (even if this period might last years depending on
the current status of memory traces). The relearning effect, which for
shorter intervals should rather be called a retrial effect, adheres to a
well-defined curve with a maximum at a strictly determined point in time. In
laboratory conditions, this point may range from minutes to days for LTP.
Finally, Matzel and Shors indicate that non-associative induction of LTP
questions its relevance in associative learning while ignoring the fact that
the actual synaptic mechanism by which LTP is generated is not well
understood. Even if LTP was indeed non-associative, its most important
component, synaptic potentiation, has been an invaluable tool in
investigating molecular cascades that result in the synthesis of proteins
thought to be responsible for the strength of memory traces. No molecular
biologist would question the relevance of this body of knowledge to memory
and learning, consequently reaffirming the immense value of LTP as a
research tool. There is little indication that associatively and
non-associatively induced memories use essentially different molecular
systems to justify discarding non-associative models in the study of
associative ones. In conclusion, the authors state that monothematic
preoccupation with LTP may deter efforts to elucidate other mechanisms that
might be better suited to subserve associative memory. It is true that
creative molds imposed by peer-review process, memetic nature of human
communication, and historical implications of scientific research process
should always be carefully taken into consideration; however, to facilitate
the suggested departure from LTP the authors will have to equip memory
researchers with alternative experimental models, which have not therein
been submitted
Rogan
et al. (incl. Eric D. Kandel) discuss fear conditioning and the LTP in the
amygdala as a valuable research model for memory formation. The authors
stress the associative nature of fear conditioning with wide implication for
learning in general. Fear conditioning circuits are presented as well as
their study with the use of single unit recording and fMRI. Methods for
evoking experimental LTP in amygdala are presented. Research into possible
molecular substrates of amygdaloid LTP is discussed. The authors conclude
that unlike the hippocampus which seems to be a well-studied structure with
a hazy relationship to overt behavior, the amygdala seems to be one of the
most promising areas with well-defined link to behavioral variables
Stephen
Maren compares the hippocampal and amygdaloid LTP in the context of
emotional learning. The author notes that Pavlovian fear conditioning is
robust and rapidly acquired in a similar way in various mammals, and indeed
it has formed the core of the associative learning theory. Those
characteristics make it an ideal candidate for studying time-dependent
memory processes such as memory consolidation. The basolateral amygdaloid
complex (BLA) is presented as the primary sensory interface of the amygdala,
while the central nucleus of amygdala (CEA) is depicted as the final common
pathway for the generation of acquired fear responses. Consequently, BLA
makes up the system of sensory convergence while CEA a system of executive
divergence in fear conditioning. The role of the hippocampus in the same
learning tasks is then discussed with a clear indication of its contextual
and temporally-limited role. The hippocampus is seen as complementing the
amygdala as an accessory decoding unit without actually taking an intimate
part in the learned reflex
Wickliffe
C. Abraham analyses differences in LTP in layers of the EC-hippocampus
circuit and in the neocortex, and how these could be involved in
autoassociation, decoding steps for complex memories, and the development of
sparse memory representation characteristic of higher learning. The role of
hippocampal place cells is discussed as well as the possible role of hard
and soft synapses. The paper includes a presentation and discussion of the
three families of LTP decay curves for dentate gyrus cells
Sabrina
Davis at al. discuss the role of gene activation in investigating synaptic
plasticity. The article begins with presenting the behavioral evidence for
the role of LTP in learning (e.g. McNaughtons dentate gyrus saturation
experiments, AP5 antagonist experiments, measurements of molecular learning
correlates, etc.), and the current understanding of the way memories are
encoded in distributed networks. The role of LTD in preventing memory
overload is stressed. Then the series of molecular events leading to memory
formation is described. From calcium influx to the protein synthesis in 3-6
hours window with protein delivery via synaptic tagging. A longer section is
devoted to the characterization of immediate early genes activated in
learning and the resulting upregulation of protein synthesis (CaMKII,
syntaxin, synapsin, NMDA subunits, etc.). Finally a separate section is
devoted to gene deletion technique in developing a wide range of knock-out
mice deprived of specific genes thought to play a role in learning
Donald
P. Cain presents a discussion of recent research on spatial learning with
the water maze, esp. the unclear role of NMDA-dependent LTP. The article
emphasizes the importance of the classification of task difficulty as
essential for comparing data from various labs, as well as detailed
behavioral analysis of individual tasks. Only by thus minimizing the number
of variables in the equation can the physiology of spatial learning be
elucidated and the role of NMDA stated as either central or rather related
to non-spatial functions
Paul
F. Chapman presents a detail analysis of genetic research techniques
applicable to studying LTP and learning with an emphasis on a recent flurry
of new data obtained with transgenic mice, as well as the promising future
applications of gene-altering technology
Christian
Hpan>lscher, the chief editor,
proposes a novel stimulation protocol able to reliably induce LTP that is
closer to natural activity of the living brain in contrast to HFS frequently
used in LTP research. Superiority of the new protocol is demonstrated by a
better match in the profile of glutamate receptor agonists effect on LTP and
actual learning tasks. The status quo of research methodology is analyzed.
An outline of desirable yet realistic prospects for improvement looming on
the horizon is presented
Kathryn
J. Jeffery suggests how we can circumvent our inability to record the input
onto individual neurons by studying cell responses to strictly defined
environmental stimuli that result in learning. The role of the hippocampus
in representing space and in spatial learning is discussed
In a
refreshing departure from the standard mold, Neil McNaughton (do not confuse
with Bruce McNaughton from the University of Arizona) presents his
unorthodox model of the hippocampal function which is based on preventing
rather than promoting the formation of connections within the brain.
McNaughton lists a dozen of theories on the actual function of the
hippocampus and shows that they cannot explain why amnesics show little
recovery from memory interference in paired-associate learning. Instead
McNaughton proposes his own model in which the hippocampus works to detect
negative associations (associations of conflict). The model in part entails
a controversial separation of the program from its data in a neural network
along a far-reaching metaphor derived from linear digital computers. In
conclusion, McNaughton compares hippocampal LTP to programming but still
insists on tagging his model as nonmemorial
Conclusions
Neuronal
Mechanisms of Memory Formation provides an excellent review of the current
state of research into memory formation and will greatly satisfy both
researchers in the field as well as students with a particular interest in
memory and learning. At the same time, with minor exceptions (e.g. as found in
papers by Munk, Rolls or Lisman), the book cannot claim to take a wide
inter-disciplinary effort of holistically exploring lesser tested hypotheses and
models of memory and cognition.
In concluding remarks, the editors note that it is
possible that LTP is only one of many memory mechanisms used in the brain.
We suggest that this conclusion should be reformulated to: LTP is only one of many
expressions of the same memory mechanism used in the brain. Once the common
denominator is understood, the confusion coming from the effort to equate LTP
with memory mechanisms should be cleared out