MindNet is no longer active.
Back to MindNet Index
================================================================
     MindNet Journal - Vol. 1, No. 62
================================================================
     V E R I C O M M / MindNet         "Quid veritas est?"
================================================================

The views and opinions expressed below are not necessarily the
views and opinions of VERICOMM, MindNet, or the editors unless
otherwise noted.

The following is reproduced here with the express permission of
the author.

Permission is given to reproduce and redistribute, for
non-commercial purposes only, provided this information and the
copy remain intact and unedited.

Editor: Mike Coyle 

Assistant Editor: Rick Lawler

Research: Darrell Bross

================================================================

MEMORY PROCESSES DESCRIBED AS BRAIN OSCILLATIONS IN THE EEG-ALPHA
AND THETA BANDS

By Wolfgang Klimesch

Copyright 1995 Wolfgang Klimesch

Reprinted by permission from: 
PSYCOLOQUY.95.6.06.memory-brain.1.klimesch.

April 1995

----------------------------------------------------------------
 
Wolfgang Klimesch University of Salzburg Department of
Physiological Psychology Institute of Psychology, Hellbrunnerstr.
34 A-5020 Salzburg, AUSTRIA 
 
ABSTRACT: This target article tries to integrate results in
memory research from diverse disciplines such as
psychophysiology, cognitive psychology, anatomy and
neurophysiology. The integrating link is seen in more recent
anatomical findings that provide strong arguments for the
assumption that oscillations provide the basic form of
communication between cortical cell assemblies. The basic
argument is that episodic memory processes, which are part of a
complex working memory system, are reflected by oscillations in
the theta band, whereas long-term memory processes are reflected
by alpha oscillations. It is assumed that alpha and theta
oscillations serve to encode, access, and retrieve cortical codes
that are stored in the form of widely distributed but intensely
interconnected cell assemblies.
 
KEYWORDS: Alpha, EEG, Hippocampus, Memory, Oscillation, Thalamus,
Theta.
 
I. INTRODUCTION AND OVERVIEW
 
1. In the following sections an attempt is made to link
convergent knowledge about memory processes from four different
fields: electrophysiology, cognitive psychology, functional
anatomy, and neurophysiology. The motivation to follow up this
interdisciplinary approach was spurred by the following two basic
findings.
 
2. The first finding is that the frequency of EEG-alpha
oscillations is positively correlated with memory performance. In
a series of four independent experiments, we (Klimesch, Schimke,
Ladurner & Pfurtscheller, 1990; Klimesch, Schimke &
Pfurtscheller, 1993; and Klimesch, Schimke, Doppelmayr, Ripper,
Schwaiger & Pfurtscheller, 1994) were able to demonstrate that
good memory performers have a significantly higher alpha
frequency than subjects with bad memory performance. In
concordance with these results, reaction time experiments have
also shown that with increasing memory performance, retrieval
time decreases (Klimesch, Schimke & Ladurner, 1988).
 
3. The second finding stems from reaction time experiments with
natural concepts. In a variety of different experiments
(summarized in Klimesch, 1994), it was found that complex
concepts can be processed much faster than less complex concepts
and it could be demonstrated that this effect is not due to
confounded variables such as typicality, word frequency or degree
of connectivity. For cognitive psychology, this fact that more
complex information can be processed faster than less complex
information is a challenge, because well established theories
(such as the well known ACT* theory from Anderson, 1983) and
related experimental results show the opposite effect. In dealing
with this challenge a new model, the connectivity model of
semantic processing, was developed (Klimesch, 1987, 1994). The
connectivity model focuses on an explicit description of
representational assumptions for the encoding of long-term memory
(LTM) codes and describes semantic search and retrieval processes
in terms of a spreading activation process. It is important to
emphasize that this positive speed effect is predicted and holds
true for integrated (interconnected) semantic LTM codes but not
for episodic short-term memory (STM) demands (Kroll & Klimesch,
1992).
 
4. Both findings point towards the importance of what will be
termed the speed effect of memory performance: High memory
performance and complex integrated knowledge speed up search and
retrieval processes. With respect to the electrophysiological
results, the interesting fact is that the connectivity model
explains LTM performance in terms of an increase in the speed of
the spreading activation process. It thus seems plausible to see
a possible link with the finding that alpha frequency increases
with increasing memory performance. Because there is evidence
that EEG-alpha activity is related to thalamo-cortical
oscillations (e.g., Steriade, Jones & Llinas, 1990) it is
tempting to postulate a specific role of the thalamo-cortical
network for memory processes.
 
5. In trying to integrate these results within a
psychophysiological perspective, we suggest the following
preliminary hypothesis which rests on three assumptions: (1)
Memory codes are stored in the form of interconnected but widely
distributed networks (cell assemblies) in the cortex. (2) Memory
codes are accessed and retrieved via "longitudinal" pathways
linking deeper brain structures such as the thalamus with the
cortex. And (3) alpha is one of the dominant rhythms reflecting
the activity of some of these pathways. This hypothesis has one
crucial implication and raises several questions. The most
important questions are:
 
  - What is the neuro-anatomical and physiological basis for
memory processes in general? (see section IV below).
 
  - Are different types of memory processes reflected by
different types of oscillations? (see sections V and VI below).
 
6. The crucial implication of the proposed hypothesis is that
brain oscillations may be considered the basic phenomenon of
cortical information processing. It should be noted that this
implication is already inherent to the finding of a positive
relationship between EEG-alpha frequency and memory performance.
Because dominant brain oscillations can be recorded with the EEG,
a summary of basic results is described in sections II.2 and II.3
below. These sections will provide us with the necessary
conceptual tools to describe memory processes in terms of
oscillations.
 
7. EEG findings that focus on the analysis of brain rhythms will
be an important source of evidence for evaluating the proposed
hypothesis. For reasons that are explained later in the text it
will be assumed that EEG-alpha oscillations are closely related
to semantic long-term memory (LTM) processes. Thus, when
contrasting LTM with STM processes, the interesting question
arises: Which frequency band in the EEG might be related to the
encoding and retrieval of new information? It is well known that
on the one hand, the hippocampal formation is of crucial
importance for the encoding of new information and that on the
other hand, the theta rhythm is the dominant oscillation of this
brain region. It is, therefore, tempting to expect a specific
relationship between the theta rhythm and the encoding and
retrieval of new information.
 
8. Besides some clinical findings (Arnolds, Lopes da Silva,
Aitink, Kamp & Boeijinga, 1980) and recent results from our
laboratory (Klimesch, Schimke & Schwaiger, 1994), there is a lack
of clear-cut findings that point towards the proposed
relationship between theta frequency and episodic STM demands in
humans. This lack most likely is due to the fact that theta is
not a dominant rhythm in the EEG of wake adults (see section V).
Most of the results in the literature that deal with EEG and STM
were reported in studies using event-related brain potentials
(ERPs). The fact that there is no obvious link between ERP
findings and brain oscillations brings us to the most speculative
part of the manuscript. In a later section it is proposed that
ERP-components that reflect certain STM demands may be due to
phase locked theta activity (see section VI).
 
II. BRAIN OSCILLATIONS: BASIC CONCEPTS AND HYPOTHESES
 
9. The basic idea proposed in this and the following sections is
that memory processes such as search, spreading activation, and
retrieval can be described as processes that modulate the
frequency of oscillatory neuronal discharge patterns. For sensory
information processing it is well established that it is the
modulation of the frequency of action potentials that encodes the
information of a sensory input. This fact will be applied to
cortical information processing. It will be assumed that the
modulation of the frequency of brain oscillations is the basic
mechanism of information transmission in the cortex.
 
II.1 ARE OSCILLATIONS MANDATORY FOR CORTICAL INFORMATION
PROCESSING?
 
10. Braitenberg and his coworkers have shown that some of the
conventional ideas about the anatomy of the cortex are wrong (see
the comprehensive review in Braitenberg & Schuez, 1991). The
question he and his group addresses refers to the issue of
specificity or randomness of neuronal connections in the cortex.
They demonstrate that the probability for an axonal synapse to
have a particular neuron as postsynaptic partner is p = .001. The
probability that more than one contact is made with a particular
cell is extremely small (e.g., for three contacts the probability
is p = .0000001). Given this enormous divergence in
interconnectedness, one cell could never excite any other neuron.
Due to the principle of temporal and spatial summation
(summarized, e.g., in Koestner, 1985), a single cell will respond
with an outgoing signal (an action potential) only if many
convergent inputs are received at the same time (within a narrow
time window). This means that converging neuronal signals must
arrive synchronously in order to trigger an outgoing signal.
 
11. The crucial question now is, which mechanisms operate to
synchronize the neuronal input for each neuron? It is proposed
that oscillations reflect this synchronizing mechanism. If
signals come in synchronized bursts of action potentials, that
is, in the form of oscillations, single neurons even in a
distributed, randomly wired network will respond with an outgoing
signal. Oscillations may be induced into a neuronal network by
pacemaker cells and/or by endogenous membrane properties of
individual cells (e.g., the reviews in Steriade, Jones & Llinas,
1990; and Basar & Bullock, 1992). It is interesting to note that
mathematical analyses indicate that, particularly in biological
systems, oscillators tend to synchronize if their frequencies are
not too different from each other (Strogatz & Stewart, 1993).
 
II.2 THE FUNCTIONAL MEANING OF EEG SYNCHRONIZATION AND
DESYNCHRONIZATION
 
12. One of the best known results obtained with the EEG documents
the functional importance of brain oscillations. Since the
pioneering work of Berger in the late 1920s and early 1930s
(e.g., Berger, 1929), it is known that the most dominant rhythm
in the EEG, the alpha rhythm, can best be seen under conditions
of relative mental inactivity, but it is blocked or
desynchronized by attention and/or mental effort. The fact that a
strong rhythm, such as the alpha rhythm, can be recorded from
scalp electrodes means that millions of cortical neurons must
oscillate synchronously with the same phase and within a
comparatively narrow frequency band. Desynchronization, that is,
the disappearance of the dominant alpha rhythm is functionally
related to mental activity and means that different oscillators
within the alpha band are no longer coupled. They oscillate with
different phase lags and probably with different frequencies.
This basic EEG-phenomenon of synchronization (during mental
inactivity) and desynchronization (during mental activity)
provides us with a preliminary but nonetheless important
understanding of how information may be processed in the brain:
The synchronization of very large populations of neurons
oscillating with the same phase and frequency reflects a state in
which no information is transmitted.
 
II.3 TWO DIFFERENT TYPES OF SYNCHRONIZATION
 
13. It is of crucial importance to emphasize that synchronization
has two different meanings. Synchronization within the
traditional context of EEG research reflects a state of mental
inactivity. More recent research with microelectrodes implanted
in the cortex, however, have shown that synchronous oscillatory
discharge patterns in high frequency bands (such as the broad
gamma band from 30 - 70 Hz) are related to rather localized
cortical processes, reflecting cognitive activity such as visual
encoding processes (e.g., Gray & Singer, 1987). Only at first
glance do these two different meanings of neuronal
synchronization seem to contradict each other. From the
standpoint of EEG research, it  is a matter of resolution whether
or not we may speak of synchronization or desynchronization.
Desynchronization of the EEG is interpreted in terms of frequency
and/or phase shifts of a large population of oscillators that
become progressively uncoupled. Thus, recorded from EEG
macroelectrodes, neuronal activity appears desynchronized.
Nonetheless, within small cortical areas, neuronal activity may
still exhibit a synchronous discharge pattern. To avoid
confusion, we will call the synchronous activity of large
cortical areas reflecting mental inactivity type 1
synchronization. With type 2 synchronization we denote the
synchronous oscillatory discharge pattern of selected and
comparatively small cortical areas.
 
14. In summarizing these three terms, we have to keep in mind
that on the microscale, a frequency change (possibly over a broad
frequency range) of a comparatively small group of neurons (or
cell assembly) occurring synchronously within the different
neurons of this group (cell assembly) reflects the processing of
information (see also section III.2). Because information
processing generally is considered a distributed process, a great
number of different, distributed cell assemblies will show type 2
synchronization in response to cognitive demands. On the
macroscale, however, the behavior of many different cell
assemblies responding with type 2 synchronization will show up as
desynchronization in the EEG. The main reason for this is that
each cell assembly will respond with its own frequency and this
synchronization may not be coupled between cell assemblies.
Because the synchronous discharge of small cell assemblies is a
very weak signal for the EEG, type 2 synchronization can be
detected primarily by microelectrodes but is difficult to detect
with EEG macroelectrodes. Thus, if many different cell assemblies
show uncoupled type 2 synchronization, the EEG will be
de-synchronized. In contrast, type 1 synchronization is a very
strong signal for the EEG, showing the synchronous, phase coupled
oscillatory discharge pattern within a narrow frequency band of
very large cell populations.
 
15. EEG-frequencies are conventionally subdivided in frequency
bands such as the theta (4 - 8 Hz), alpha (8 - 13 Hz), beta (14
to about 30 Hz) and gamma bands (30 - 70 Hz). It is important to
note that the traditional terms of EEG (type 1) synchronization
and desynchronization apply for the alpha and beta bands only.
The gamma band seems to synchronize in response to cognitive
demands (Pfurtscheller, Flotzinger & Neuper, 1994) and seems to
reflect real type 2 synchronization in the EEG (see Pulvermueller
et al., 1994)., The theta band clearly synchronizes in response
to cognitive demands.
 
16. This basic behavior of the EEG generally is very similar for
animals and humans (see e.g., the review in Lopes da Silva, 1992)
with the exception that the frequency range of the theta rhythm
is much wider in animals (lower mammals). Thus, to avoid
confusions with the human EEG, the term rhythmic slow activity
(RSA) was introduced to denote synchronized theta activity (see,
e.g., Vanderwolf & Robinson, 1981) whereas the term irregular
slow activity (ISA) is used to denote desynchronized theta
activity. In contrast to ISA, RSA reflects a state of mental
activity in animals. In humans too, theta synchronization is
related to mental activity and to the encoding of episodic
information in particular (see Klimesch, Schimke & Schwaiger,
1994; and Arnolds et al., 1980).
 
17. In the human EEG of wake adults, theta is a weak rhythm that
most likely is induced into the cortex via a small but
distributed set of longitudinal hippocampo-cortical pathways (see
sections III, V and the review in Lopes da Silva, 1992). In wake
adults, theta synchronization seems to have the meaning of
coupled type 2 synchronization. This sort of synchronization is
explained in terms of a small subset of hippocampo-cortical
feedback loops responding to an appropriate event or signal with
synchronized phase locked theta activity. As a result, selected
and distributed cortical cell assemblies will start to respond
with synchronous theta activity. According to this
interpretation, theta desynchronization (ISA in animals) simply
is the lack of type 2 synchronization (RSA in animals). It is
well known, however, that during certain sleep stages, theta
becomes a dominant rhythm in the EEG, reflecting type 1
synchronization. Note that this is not in contradiction to the
proposed interpretation: Type 2 theta synchronization reflects
the processing of information whereas type 1 theta
synchronization reflects the lack of information processing or a
state of "functional inhibition".
 
18. A good example to document the meaningfulness of type 1
synchronization for memory processes is the general issue of
inhibition. Any memory theory trying to explain search processes
is confronted with the fundamental problem of how it can be
explained that spreading activation is confined to the relevant
parts of the (cortical) memory network (Klimesch, 1994). The most
obvious way of handling this problem is to assume strong
inhibitory processes that allow a search process to spread only
within certain regions of the network. According to Braitenberg &
Schuez (1991), however, the assumption of powerful inhibitory
processes is not plausible, given the fact that about 85% of all
cortical neurons are pyramidal cells with excitatory synapses.
Inhibitory synapses are comparatively rare (comprising only 11 to
15% of all cortical synapses) and reside on stellate cells that
primarily make local connections. Thus, in the cortical network,
inhibitory processes are more likely operating locally and
probably do not have far reaching effects. Synchronous (type 1)
oscillations within a narrow frequency band, induced in large
areas or even in the entire cortex (e.g., in sleep), may have
powerful inhibitory effects. When synchronous (type 1)
oscillations within a narrow frequency band are selectively
induced in certain cortical areas from a part in the brain that
operates as some kind of "monitoring unit" or "control unit", the
basic theoretical framework for explaining inhibitory processes
by type 1 synchronization is at hand. According to this idea,
type 1 synchronization could act to block a search process
(reflected by type 2 synchronization) from entering irrelevant
parts of the network.

III. COMPONENTS OF MEMORY PROCESSES: THE WORKING MEMORY AND
LONG-TERM MEMORY SYSTEM
 
19. Two basic aspects of memory processes are emphasized. The one
refers to a close interaction between the working memory system
(WMS, see, e.g., Baddeley, 1992) with the long-term memory system
(LTMS). This interaction plays an important role for encoding,
searching, retrieving, and recognizing information. The other
aspect refers to the meaning of monitoring processes which are
considered a heterogeneous class of processes within the WMS that
operate under voluntary control. The close interaction between
the WMS and LTMS can be demonstrated by considering a fundamental
cognitive process such as recognizing a familiar object. The
crucial idea here is that after a sensory code is established,
bottom up processes access semantic information in LTM that is
used to identify the perceived object. If the process of
identification which is considered a matching processes yields a
positive result, the object is recognized which in turn leads to
the creation of a STM code. The matching process activates
pathways that are similar or identical to those that serve to
retrieve information from LTM. Top down processes which are
guided by expectancy and selective attention are capable of
directing the matching process towards a certain outcome by
preactivating or preselecting appropriate templates or prototypes
in LTM. Recent models, such as Grossberg's adaptive resonance
theory (ART), also proceed from the basic assumption that
templates or prototypes stored in LTM are activated during a
matching process which is characterized by a close interaction
between STM and LTM (e.g., Grossberg, 1980; Grossberg & Stone,
1986; Carpenter & Grossberg, 1993).
 
20. Note that encoding has two different meanings. The encoding
of sensory information (as a process of recognition) aims at the
semantic understanding of perceived information. LTM holds that
information which is essential for this encoding process. Within
the framework of STM, encoding means the creation of a new code
that primarily comprises episodic information. According to
Tulving (e.g., Tulving, 1984), episodic information is that type
of contextual information which keeps an individual
autobiographically oriented within space and time.
 
III.1 MONITORING PROCESSES, EPISODIC INFORMATION AND THE
HIPPOCAMPUS
 
21. The necessity for monitoring processes is related to the
permanent need to update episodic information. An episodic code
which is created through the action of monitoring processes
reflects primarily subjective information, such as context,
expectancy, emotion, and certain autobiographic aspects. Because
time changes the context permanently, there is a permanent need
to update and store episodic information. STM serves this vital
need to store episodic information within certain capacity
limits. Beyond these limits episodic information may be stored
into a more permanent memory system. In contrast to episodic
information, the encoding of new semantic information requires in
most cases special mnemo-techniques ("learning"). Because
"learning" is guided by complex monitoring processes of the WMS,
it is assumed that new semantic and episodic information use
identical encoding pathways into LTM.
 
22. As the classical case of patient H.M. (Scoville & Milner,
1957; and the reviews in, e.g., Markowitsch, 1983, 1984, and
Markowitsch & Pritzel, 1985, for similar cases) as well as a
variety of more recent evidence demonstrates, the hippocampus
(and other parts of the limbic system) are responsible for
encoding (or retrieving) any new declarative information beyond
the capacity limits of STM (Squire, 1992; Squire, Knowlton &
Musen, 1993). Because of the permanent need to update episodic
(but not semantic) information, the loss of freshly encoded
episodic memory is such a dramatic symptom for anterograde
amnesia that the concurrent failure to encode new semantic
information appeared to be of minor significance (c.f. Baddeley's
commentary to Tulving's target article in BBS for a similar
statement; Baddeley, 1984, p. 239). The importance of the
hippocampus for contextual encoding was proposed by several
researchers. As an example, Teyler and DiScenna (1986) assume
that the hippocampus stores at least initially some sort of
"index" pointing towards those neocortical modules or cell
assemblies (i.e., those LTM structures) that have been activated.
In an interesting theory, Miller (1991) proposes that the
hippocampus is important for contextual representations and might
be involved in forming global cell assemblies. Squire (1992)
emphasizes the "binding" function of the hippocampus and
emphasizes that it is needed to bind together distributed cell
assemblies (representing features) that together form the
information of a single code. If the hippocampus is involved in
the process of consolidation, contextual encoding and binding, we
have to expect that extensive and widespread projections exist to
the association cortices. This is indeed the case (e.g., Lopes da
Silva, Witter, Boeijinga & Lohman, 1990).
 
23. These conceptions of hippocampal functions fit very well with
the hypothesis (proposed in section V, paragraph 42, see also
paragraph 17) that the selective type 2 synchronization of a
small percentage of hippocampo-cortical feedback loops actually
reflects the encoding of new (episodic) information. The
synchronization of selected, distributed cortical cell assemblies
might allow for establishing a binding process that links
features in a new way to create a new context. Carpenter and
Grossberg (1993) add another interesting aspect to this view of
hippocampal functions. They assume that the hippocampus
represents something like an orienting system that allows one to
orient towards the encoding of a new stimulus. It is important to
note that all of the functions ascribed to the hippocampus are
central functions of the WMS.
 
III.2 SEARCHING LTM CODES AS A CORTICAL PROCESS OF SPREADING
ACTIVATION
 
24. It is assumed that LTM codes are represented by a distributed
structure of nodes that establish a complex network or cell
assembly in the neocortex. Even a single node is considered a
structure of features that may be widely distributed throughout
different regions of the neocortex. Features may be represented
by smaller cell assemblies such as cortical columns or modules
which serve as feature detectors when activated by perceptual
processes (c.f. the close interaction between LTM and perception
emphasized in paragraph 19). The assumption of highly distributed
codes may explain why any attempt to localize a particular engram
resulted in a failure (c.f. Lashley, 1950). This structural
encoding assumption leads to the crucial question of how the
different features belonging to a single code can be activated
together as a functional unity without activating features of
other overlapping but irrelevant codes. According to the
traditional view as first proposed by Hebb (e.g., Hebb, 1949),
one may assume that the features of a code are represented by a
cell assembly of interconnected cells that are functionally
characterized by a concurrent elevation of their average firing
rate. Unlike more recent models, Hebb's conception has the
disadvantage that in a particular cortical region and within a
given time span, only a single code or feature can be activated,
because the enhanced firing rate is the only cue which allows it
to distinguish the relevant code from irrelevant information.
During a search process in LTM, a huge variety of codes will be
activated at the same time and possibly in the same brain region.
Thus, different and topographically overlapping cell assemblies
will be activated at the same time. Consequently, it will be
impossible to distinguish between different codes. In trying to
avoid this problem, one may instead assume that assemblies may be
functionally defined by a state of synchronous firing of cortical
neurons, rather than by an enhanced average firing rate. This
means that in a particular cortical region and within a given
time span, all of the cells may be highly active, but only those
cells firing synchronously represent the relevant information
comprised by a single code.
 
25. Gray and Singer (1987) together with other researchers at the
Frankfurt MPI (e.g., Gray, Koenig, Engel & Singer, 1989; Engel,
Koenig, Kreiter, Schillen & Singer, 1992) have provided
convincing evidence that a visual code, established through a
perceptual process, can be described as a cell assembly which
responds with a synchronous oscillatory discharge pattern within
a broad frequency range of about 30 to 70 Hz which is termed
gamma band. They assume that the synchronous oscillatory firing
pattern of distributed cortical cells reflects a stage of
cortical integration in the sense that the information provided
by different feature detectors is integrated into a single visual
code. This assumption is substantiated by the important finding
that even widely distributed but synchronously oscillating cell
assemblies fire with zero phase lag. Feedback loops, connecting
different cell groups of the cortex, obviously are the means
which enable this surprising ability. Oscillations are considered
carrier signals for the relevant information which might be
encoded by the synchronous modulation of frequencies.
 
26. When applying the encoding principle described above to
search processes in LTM, the interesting conclusion is that the
search process would have to find cell assemblies that are
capable of establishing a synchronous oscillatory firing pattern
in response to the initiation of a search process. According to
this concept, the relevant sought-after information would be
characterized by a synchronous oscillatory discharge pattern.
However, unlike a visual encoding process, where all the relevant
information is given at the same time, during the course of a
search process thousands of different codes will be activated at
different times. Each code may respond with a synchronous
oscillatory discharge pattern, but what should be the criterion
to distinguish the sought-after relevant from irrelevant
information? Establishing a synchronous oscillatory firing
pattern along the search pathway would not allow the search
process to selectively retrieve the relevant information.
 
27. This question of how a search process finds the relevant
information is called the search problem. It will be explained in
terms of a spreading activation process that was described within
the framework of the connectivity model, outlined in detail
elsewhere (Klimesch, 1994). This model describes spreading
activation in abstract terms of different activation values
moving from one node to another. In the context of cortical
activation the term "activation value" is translated into
"frequency of an oscillatory neuronal discharge pattern". As an
example, let us consider a completely interconnected code (with n
= 5 nodes), in which each node is connected to each of the other
nodes. At the beginning of an activation process each node
representing a cell assembly (or cortical module) has zero
activity which means that it oscillates with some low (resting)
frequency. Activating a node means to put it in a state of
oscillation with a frequency that is higher than its resting
frequency. Now, if activation starts at one of the n = 5 nodes
with frequency f, this activation spreads to all of the other n -
1 nodes of that code. Accordingly nodes 2, 3, 4 and 5 are also
put in oscillation with frequency f. Now in a second activation
stage, the n - 1 nodes activate each other. Thus, each node
receives activation from the remaining n - 2 nodes. With each
additional activation, the n - 1 nodes increase their
responsiveness which means that they increase their frequency
proportional to the number of times they were activated. Note
that all of the n - 1 nodes are completely interconnected and are
thus n - 2 times activated which results in an increase in
frequency from f to f'. In a third step, the increased frequency
f' is fed back to that node where the activation process was
initiated. Note that the increase from f to f' reflects the
complexity of a code. The more nodes there are, the higher
frequency f' and consequently, the faster the spreading
activation process will be. Furthermore and most important, due
to the interconnections between the n - 1 nodes, which are
considered the features of a code, a synchronous oscillatory
discharge pattern is established within all of the components of
a code. Thus, in accordance with the findings of Gray & Singer
(1987) which are partly summarized in Engel et al. (1992), a
memory code can be characterized by a pattern of features
oscillating synchronously. However, during the spreading
activation process each code is activated at different times and,
even more important, each code will respond with a different
frequency, because frequency f' depends on geometric properties
which differ between codes.
 
28. According to the connectivity model, a search process
terminates with a positive result if activation (i.e., some
frequency f' which must be higher then input frequency f) spreads
back as "echo" to one of those nodes where the search was
initiated. Monitoring processes of the WMS do not guide the
spreading activation process which follows automatically by local
mechanisms. Their task is to select access points, when
initiating a search process, and to retrieve the relevant
information if a search process terminates with a positive
result. The result of a search process can be judged by the
strength of activation equaling frequency f' of activated codes.
That code responding with the highest frequency represents the
relevant information to be retrieved.
 
29. It is important to remember the two different types of
synchronization, outlined in section II.3. Synchronous (type 1)
oscillations of large cell populations are considered to reflect
a state of inhibition. As an example, at the beginning of a
search process, large cortical areas may oscillate synchronously
with resting frequency f. Because information is encoded by the
modulation of frequencies, large cell populations oscillating
with the same (low resting) frequency (within a narrow band) are
not capable of transmitting information. However, as a result of
a search (or spreading activation) process, different cortical
cell assemblies change their frequency and establish a type 2
synchronous oscillatory discharge pattern over a broad frequency
range which may comprise the entire frequency band (comprising
the theta, alpha, beta and gamma bands). This modulation or
change in frequency reflects the process of transmitting
information but is restricted to those cortical areas that are
relevant for processing a task.
 
30. With respect to the most dominant rhythm in the EEG, a
spreading activation process of the type explained above should
be reflected by a desynchronization of the alpha rhythm. And,
indeed, as a variety of studies have shown, alpha
desynchronization indicates a state of cortical activation (for
recent reviews see, e.g., Pfurtscheller, 1992; Pfurtscheller &
Klimesch, 1992; Pfurtscheller & Klimesch, 1991), whereas alpha
synchronization reflects a state of cortical inactivity or
"idling" (Pfurtscheller, 1992). Thus, alpha desynchronization
might very well reflect the actual encoding process which is
based on a frequency modulation in the relevant cell assemblies.
The fact that during desynchronization a complex pattern of
changes in EEG coherences can be found, as the interesting work
of Petsche and his group demonstrates (e.g., Petsche and
Rappelsberger, 1992) is well in line with the proposed
interpretation.
 
III.3 RETRIEVING LTM CODES AND THE POSSIBLE ROLE OF
THALAMO-CORTICAL FEEDBACK LOOPS
 
31. In the identification of the relevant nodes or codes,
feedback loops may play a decisive role. During the course of a
search process, the activation status of the searched network is
constantly transmitted back by means of feedback loops to a
control system. The basic idea is that a control network
converging in a particular control system is mapped onto the
storage network. Consequently, the control system should be
connected with the cortex by a dense network of axonal
connections. Besides the basal ganglia, the thalamus with its
thalamo-cortical projections to virtually all different cortical
regions (e.g., Hoehl-Abrahao & Creutzfeldt, 1991) is one of those
brain structures that fulfills this requirement. It is important
to see that as compared to the thalamo-cortical network, the
cortical network is orders of magnitude denser. Thus, each
feedback loop serves a relatively large cortical field which also
will be termed "alpha field".
 
32. To initiate a LTM search requires some vague information of
where in the storage network to look for the relevant
information. Because it would be highly inefficient to search the
entire network once a search process is initiated, it is
necessary to delimit the search area. In a theoretical sense,
retrieval cues that give a rough description or some details of
the relevant information can serve this purpose. In an anatomical
sense, the thalamus might be a good candidate for delimiting the
search area in the neocortex because the thalamo-cortical network
may allow direct access to certain parts of the neocortex. Based
on the current context of the WMS, retrieval cues are provided
that enable the thalamus to activate particular thalamo-cortical
pathways which start the search process in the neocortex.
Thereby, specific and unspecific thalamic projections might
provide access to specific sensoric or more abstract information,
respectively. However, it should also be noted that in contrast
to the traditional belief, the thalamus shows a much more complex
pattern of different types of projections (e.g., Steriade et al.,
1990, p. 40).
 
33. The thalamus and hippocampus probably are involved in quite
different functions. Whereas the thalamus might serve as a relay
station for searching and retrieving pure LTM information, the
hippocampus (as well as other parts of the limbic system) might
be important for the encoding and retrieval of concomitant
episodic information. Note, however, that any search and
retrieval process is embedded within a particular autobiographic
context which defines the particular episodic meaning of that
information which is retrieved from LTM. Thus, it must be
expected that the functions of the hippocampus and the thalamus
are closely interrelated.
IV. ALPHA FREQUENCY AND SEMANTIC MEMORY PROCESSES
 
34. Some researchers have suggested that the EEG frequency within
the alpha band (of about 8-13 Hz) stems from the thalamus and
induces synchronized neuronal activity in the cortex (Andersen &
Andersson, 1968). In terms of EEG frequencies, the ideas
described so far can be summarized as follows. A search processes
in LTM starts if thalamo-cortical pathways are selectively
activated, which means that a particular subset of these pathways
shift their resting frequency within the alpha band. This shift
in frequency serves as input frequency f to the cortical storage
network where the search process spreads between different access
points. In response to the search process, different cell
assemblies start to oscillate with different frequencies f'
within the alpha, beta and gamma bands. The status of the search
process is constantly fed back by each activated cortical field
(alpha field) that is served by a thalamo-cortical feedback loop
oscillating within the range of alpha frequency. The higher
cortical frequency f' is, the stronger is the concomitant
increase in alpha frequency. The relevant information can be read
out via those feedback loops responding with the highest
frequency. Because we have assumed that alpha oscillations
reflect the information processing in thalamocortical feedback
loops, whereas gamma oscillations reflect pure cortical
processing, a specific transition or interface between alpha and
gamma oscillations must be postulated. The following two facts
support this idea. First, Steriade et al. (1990, p. 147) report
that the gamma rhythm (the 40 Hz rhythm in particular) is driven
by neurons located in that cortical layer, which receives
thalamic afferents. They conclude that thalamic input to the
cortex serves as a trigger for rhythmic activation of specific
cortical columns. Second, Pfurtscheller et al. (1994) observed a
reciprocal relationship between alpha and the 40 Hz rhythm: If
the 40 Hz rhythm synchronizes, alpha desynchronizes and vice
versa.
 
IV.1 EEG ALPHA FREQUENCY REFLECTS SEMANTIC AND THETA EPISODIC
MEMORY PROCESSES
 
35. If we proceed from the idea that memory codes are retrieved
via longitudinal pathways linking thalamic nuclei with the
cortex, and that alpha is the predominant rhythm reflecting the
activity of these pathways, we arrive at the hypothesis that
alpha frequency should be related to memory performance (see
section I). We have tested this hypothesis (Klimesch, Schimke,
Ladurner & Pfurtscheller, 1990; Klimesch, Schimke &
Pfurtscheller, 1993) and found that - as compared to bad memory
performers - good performers show a significantly higher alpha
frequency. This result was found even in a resting state where
subjects relaxed with eyes closed, but was most pronounced during
actual retrieval attempts (Klimesch et al., 1990, Experiments 1
and 2). Furthermore, it could be demonstrated that attentional
demands or interindividually different responses to increasing
memory load (as an indicator of STM-span) are not responsible for
the higher alpha frequency of good memory performers (Klimesch et
al., 1993).
 
36. In a recently performed study, Klimesch, Schimke and
Schwaiger (1994) were able to demonstrate that alpha power
selectively responds to semantic LTM demands, whereas theta power
selectively responds to episodic STM demands. In this study, an
experimental design was used, that already proved useful in
distinguishing semantic LTM from episodic STM (Kroll & Klimesch,
1992). The experimental design consisted of two parts. Subjects
first performed a semantic congruency task in which they had to
judge whether or not the sequentially presented words of
concept-feature pairs (such as "eagle-claws" or "pea-huge") were
semantically congruent. Then, without prior warning, they were
asked to perform an episodic recognition task. This was done in
an attempt to prevent subjects from using semantic encoding
strategies and thus to increase episodic memory demands. In the
episodic task, the same concept-feature pairs were used as
targets and were presented together with distractor pairs
(generated by repairing known concept-feature pairs). Now
subjects had to judge whether or not a particular concept-feature
pair was already presented during the semantic task. The results
of Kroll and Klimesch (1992) indicated that semantic features
speeded up semantic, but slowed down episodic decision times (for
an extensive review on this topic see also Klimesch, 1994). With
respect to the purpose of the present study, this result
indicates that semantic and episodic memory processes can
effectively be differentiated by using the design underlying
Experiment 4 in Kroll and Klimesch (1992). According to the
proposed hypothesis, it is expected that only in the semantic
task should the most pronounced desynchronization (decrease in
alpha band power) be observed in the alpha band. Because pairs of
items are presented and because a subject can only perform the
episodic and semantic task after the second item of a pair (i.e.,
the feature) is presented, a decrease in alpha band power as a
response to increasing semantic task demands is expected only for
the time period following the presentation of the feature. It is
important to keep in mind that alpha power is well known to
decrease (desynchronize) with increasing task difficulty and
attentional demands. From the results found in Kroll and Klimesch
(1992) we know that the episodic task is much more difficult than
the semantic task. Thus, if task difficulty would be the only
factor which is reflected by a decrease in alpha power, we would
expect the most pronounced response to be observed during the
presentation of the feature in the episodic task. This, however,
was not the case. In support of our hypothesis it was found that
alpha desynchronizes during the presentation of the feature in
the semantic task. In the episodic task, on the other hand, theta
power increased (synchronized) during the processing of the
feature.
 
V. THETA FREQUENCY AND EPISODIC MEMORY PROCESSES
 
37. Since Scoville and Milner (1957) reported a severe
anterograde amnesia for patient H.M. who had undergone a
bilateral temporal lobectomy, including the hippocampal
formation, and since Green and Arduini (1954) have found a
dominant rhythmic electrical activity within the theta band in
the hippocampus of rats, it has become obvious that theta
activity of the hippocampus might be related to the encoding
and/or retrieval of new information. Positive evidence came from
studies which have documented that there is a preference for
long-term potentiation (LTP) to occur in the hippocampal
formation, and that theta activity induces or at least enhances
LTP (e.g., Larson, Wong & Lynch, 1986; and Greenstein, Pavlides &
Winson, 1988). The fact that LTP is considered the most important
electrophysiological correlate for encoding new information,
underlines the potential importance of hippocampal theta for
memory processes in the WMS.
 
38. In trying to explain the possible functional significance of
the theta rhythm in the human EEG, we assume that synchronized
bursts of a small set of hippocampal pyramidal cells induce theta
activity in selected but distributed cortical regions which are
relevant for performing a particular task. Empirical findings
support this view and indicate that theta band power increases
with increasing (episodic) task demands (Klimesch, Schimke &
Schwaiger, 1994). Research in animals also indicates that during
behavioral activity, theta power increases (e.g., the review in
Lopes da Silva, 1992).
 
39. One of the first questions that may arise when considering
the proposed hypothesis is, why - in contrast to animals - theta
is not a dominant rhythm in the human EEG. In an attempt to
answer this question we first proceed from a theoretical
consideration that is similar to the mechanisms that were
proposed for accessing and retrieving LTM codes. It is assumed
that hippocampo-cortical feedback loops induce synchronized
rhythmic theta activity onto different regions of the neocortex
where (e.g., by means of LTP) new information is encoded or
freshly encoded information is retrieved. Given the basic
assumption that new information always will be "added" or
"attached" to related but already encoded information, only a
small subset of the hippocampo-cortical feedback loops which are
related to relevant cortical areas will be needed and thus will
actually show synchronized theta activity. Because the human
cortex is much larger than those in lower mammals and, as a
consequence, holds much more LTM-information, the encoding of new
information is a much more distributed process than in animals.
Thus, if the percentage of synchronized hippocampo-cortical
feedback loops is related to the size of the cortex (and to the
hippocampus too, which is relatively much smaller in humans),
this percentage will be orders of magnitudes smaller for humans
as compared to animals.
 
40. Evidence for the view that only a small percentage of
hippocampo-cortical feedback loops is synchronized comes from a
re-examination of the pacemaker role of the septum in the
production of the hippocampal theta rhythm (Petsche, Stumpf &
Gogolak, 1962; Stewart & Fox, 1990). In addition to cholinergic
projections, a large fraction of the septo-hippocampal
projections terminate on inhibitory (GABA-ergic) hippocampal
interneurons (Freund & Antal, 1988; see also the reviews in Lopes
da Silva, 1992; and Stewart & Fox, 1990). Based on these and
related findings, Stewart and Fox (1990) assume that the septal
input might organize the hippocampal theta activity via rhythmic
inhibition of hippocampal interneurons. This view is in agreement
with the fact that hippocampal interneurons are more likely to
behave as theta cells (Fox & Ranck, 1981) than burst firing
pyramidal neurons. In agreement with this fact, a simulation
model (Traub, Miles & Wong, 1989) reveals that in contrast to
interneurons, only a small percentage of the pyramidal cells
display synchrony.
 
41. With respect to the question, whether theta activity can be
observed in the EEG, these findings which were obtained from
microelectrodes in the hippocampus are of outstanding importance.
Biophysically, theta frequency in the hippocampus, deep inside
the brain, would be difficult to detect from scalp electrodes.
The crucial condition to detect theta as a dominant rhythm in the
EEG would be that most of the burst firing hippocampal pyramidal
cells that project to other parts of the cortex would fire in
synchrony. However, as we have already noted, according to Leung
(1980), Traub et al. (1989) and Lopes da Silva (1992), this is
not the case. And indeed, as judged by visual inspection but in
contrast to spectral analysis, theta activity usually is absent
in the EEG of normal, wake adults.
 
42. The fact that only a small percentage of the pyramidal
neurons displays synchrony, agrees with the idea that
hippocampo-cortical feedback loops induce synchronous theta
activity into selected cortical areas where new information is
encoded or fresh information is retrieved. This is type 2 or
selective synchronization that means activation. Given the fact
that theta frequency induces or at least enhances LTP (see also
Lopes da Silva, 1992), it seems tempting to assume that theta
activity, induced into selected cortical areas, reflects a
process to encode or retrieve new information by keeping or
putting selected cortical areas into a state of resonance. This
assumption comes very close to a theory of resonant phase-locked
hippocampo-cortical loops, proposed by Miller (1991).
 
VI. THETA FREQUENCY AND EVENT RELATED POTENTIALS
 
43. A possible objection against our central hypothesis that
brain oscillations are the basic phenomenon of cortical
information processing may come from researchers using event
related potentials (ERPs) to study cognitive and memory processes
in particular. They may argue that late components of ERPs
reflect completely different types of cortical processes such as,
for example, time locked threshold changes that regulate the
degree of excitability in neuronal networks (e.g., Birbaumer &
Elbert, 1988; Elbert, 1992). Given the fact that almost all of
the memory studies in cognitive electrophysiology focus on late
components of ERPs, this objection could seriously threaten the
general validity of our hypothesis.
 
44. However, it may also be argued that late components of ERPs
are the result of synchronous oscillations that are transiently
phase locked in response to a relevant event or stimulus. Summed
up over several trials, a waveform (the ERP) would be generated
that shows the typical succession of positive and negative
"peaks" (or ERP components). If we proceed from this idea, it
becomes evident that only (or at least primarily) those types of
oscillations would be capable of generating late ERP components
that indeed respond with coupled type 2 synchronization to an
increase in respective task demands. Note that alpha and beta
tend to desynchronize with increasing task demands.
Hypothetically, there are only two possible candidates (see
section II.3; paragraph 15): theta and gamma frequency. Because
gamma frequency is much too high and its amplitudes much too
small to generate a typical ERP, theta frequency remains the most
plausible candidate.
 
45. This proposal, of course, does not mean that theta is the
only generator for ERPs. In addition, there may be other
processes such as very slow oscillations in the delta band (below
4 Hz) and/or threshold changes in large parts of the cortical
network that also may have a strong influence on the waveform of
ERPs. Weak influences may even be due to type 2 synchronizations
within the alpha band.
 
46. The most obvious ERP component that might reflect phase
locked evoked theta activity is the P300 for the following two
reasons: First, because of the (typical) latency and form of the
P300, this ERP component shows the most significant power in the
theta band as frequency decompositions indicate (Basar &
Stampfer, 1985). Second, the (typical) functional meaning of the
P300, particularly the process of "updating" (Donchin & Coles,
1988), is well related to central functions of the WMS.
 
47. Now, let us consider the most speculative part of our
proposal which links phase locked theta activity to the P300
component of event related potentials (ERPs). Keeping in mind
that a single cycle of the theta rhythm consists of an inhibitory
and a disinhibited or excitatory phase and that only in the
latter, bursts of action potentials are sent to selected cortical
areas, the question arises: With which phase of the cycle does
task related (episodic) processing start? Does it start with the
inhibitory or the excitatory phase? In referring to the argument
that theta is induced in selected parts of the cortical network,
it seems plausible to assume that episodic processing starts with
the inhibitory phase in order to maximize the impact of the
distributed activation of the relevant parts of the network by
reducing irrelevant background activity through the inhibitory
phase. As a result of this assumption, and because only a small
percentage of the burst firing pyramidal cells are synchronized
through the excitatory phase, the outcome should be a positive
going deflection in the EEG, time locked to the presentation of
an adequate stimulus.
 
48. If this is true, it should be possible to record evoked theta
activity from the scalp in response to an appropriate stimulus or
event. Based on theoretical considerations and experimental
evidence (e.g., Leung, 1980), Lopes da Silva (1992, p. 93)
concludes that appropriate stimuli or events induce evoked
responses that depend on the phase of theta frequency. Therefore,
evoked theta activity may be viewed as synchronized phase locked
and thus amplified theta frequency which occurs in response to an
appropriate event. The issue of interest is whether evoked or
event-related theta activity can be detected as a response to
increased episodic memory demands.
 
49. We have emphasized that one of the most important functions
of the WMS that we have related to hippocampal information
processing is the encoding of contextual or episodic information.
Thus, if the P300 really stems from phase-locked hippocampal
theta activity, the (typical) functional meaning of the P300
should be related to the encoding of contextual and the encoding
of new information.
 
50. There is some evidence for this view. Donchin's updating
hypothesis (e.g., Donchin & Coles, 1988) is one of the best
examples. It is well established that the P300 amplitude is
related to the degree of contextual encoding (e.g., Donchin &
Coles, 1988), expectancy (or subjective probability), and the
amount of effort which is also reflected by the amount of
information transmitted to a subject (see e.g., Johnson's
triarchic model in Johnson, 1986; and the summary in Verleger,
1988, p. 351). It is important to note that Verleger (1988), who
is challenging Donchin's updating hypothesis, is not challenging
the significance of the P300 with respect to contextual encoding.
His argument basically is that the P300 does not reflect the
"updating" but instead the "closure" of expectancies.
51. A positive relationship between the P300 and the
consolidation of memory codes (as a typical hippocampal function)
was demonstrated by some of those studies reporting a Dm-effect.
Several studies have shown that ERPs recorded during the encoding
of words (or pictures) that were later remembered were more
positive than ERPs to words (or pictures) that were not
remembered (Sanquist et al., 1980; Karis et al., 1982, 1984;
Johnson, et al., 1985; Neville et al., 1986; Fabiani et al.,
1986; Paller et al., 1987; Fabiani et al., 1990; Friedman, 1990a,
b; and see also the indirect evidence provided by e.g., Noldy et
al., 1990; and the review in Paller, 1993). This difference in
ERPs during encoding which was found within the region of the
typical P300 or a late positive component was termed "Dm" (for
Difference based on later memory performance; Paller et al.,
1987) or Dm-effect. When reviewing this research it is
interesting to see that particularly the P300 does not reflect
the processing of semantic information (i.e., the encoding of a
stimulus per se) but instead the processing of episodic
information. Results reported by Karis et al. (1984) and Fabiani
et al. (1986, 1990) are in good agreement with this
interpretation. They presented subjects with different series of
words which had to be recalled immediately after a list was
presented and found that words later recalled elicited larger
P300s than words not recalled. In addition, Fabiani et al. (1990)
were able to demonstrate that this relationship between the P300
amplitude and episodic memory performance holds only if subjects
use rote learning (which is based on the encoding of contextual
and thus episodic information) but not if subjects use semantic
encoding strategies (such as organizing the words into meaningful
sentences). Thus, the Dm (with respect to the P300) most likely
reflects episodic encoding processes and as a result, this type
of Dm-effect which is based on the P300 component becomes the
weaker; the more semantic encoding processes predominate.
 
52. Electrophysiological recordings with electrodes implanted in
the hippocampus have not provided clear evidence for the view
that the P300 is generated in the hippocampus (e.g., Polich &
Squire, 1993; and the literature reviewed in this article).
Because theta is generated in the septum and because other parts
of the limbic system also exhibit theta frequency, the crucial
question is, whether or not it can be demonstrated that theta
activity (or the P300) varies as a function of (episodic) memory
performance and that at the same time the hippocampus is involved
in the modification of theta activity (or the P300).
 
53. An interesting study by Smith and Halgren (1989) who focused
on the word repetition effect in a recognition task provided
evidence for this view. It is well known that old words (repeated
words) elicit a larger P300 than new words (e.g., Sanquist et
al., 1980; and Johnson et al., 1985). Smith and Halgren (1989)
repeated the targets in the recognition task in each of a set of
nine blocks of 20 words (consisting of 10 targets and 10 new
words) and found that the amplitude difference between repeated
and new words did not change with the number of repetitions
(i.e., the number of blocks). Recognition performance, of course,
increased with the number of blocks, but this increase in
performance was not reflected by the amplitude differences
between the repeated and not repeated words which remained
constant with the number of repetitions. Because these results
were found for normal subjects as well as for patients with
unilateral (left or right) anterior temporal lobectomy, it was
concluded that the hippocampus is not involved in the increase of
recognition performance over different blocks, which can be
explained as an increase in implicit memory performance. Most
important, however, the baseline recognition performance was
significantly lower for the patients with a left temporal
lobectomy who from the very beginning also failed to show a
significant P300 amplitude difference between new and old words.
This latter result is consistent with the hypothesis that the
hippocampus (in the dominant left hemisphere) might be capable of
modifying a P300 that reflects explicit, episodic memory
performance.
 
VII. CONCLUDING REMARKS
 
54. The main purpose of this article is to encourage an
integrative and interdisciplinary view on memory processes. As a
result of this attempt, new experiments can be performed that
will be capable of critically evaluating the proposed hypotheses.
A promising empirical approach would be to analyze event-related
shifts in EEG power within the theta and alpha bands in amnesic
subjects who perform different types of memory tasks.
 
55. If it is true that oscillations are the mandatory basis for
information transmission in the cortex and possibly in the entire
brain, a better understanding of the nature of oscillations would
be essential for an integrative view in cognitive neuroscience.
For cognitive psychology this finally would mean to describe
cognitive processes in terms of oscillations and for cognitive
psychophysiology this would mean to focus primarily on the
analysis of certain, carefully selected frequency bands in
addition to the study of event-related potentials.
 
ACKNOWLEDGEMENTS
 
This research was supported by the Austrian "Fonds zur Foerderung
der wissenschaftlichen Forschung", Project S-4904 and Project
P-10235.
 
I wish to thank Stevan Harnad and anonymous reviewers for their
helpful suggestions. In particular, I am grateful for the
insightful critical comments of Niels Birbaumer on an earlier
draft of this manuscript.
 
REFERENCES
 
Andersen, P. & Andersson, A.A. (1968). Physiological basis of the
alpha rhythm. New York: Century Crofts.
 
Anderson, J.R. (1983). A spreading activation theory of memory.
Journal of Verbal Learning and Verbal Behavior, 22, 261-295.
 
Arnolds, D., Lopes da Silva, F., Aitink, J., Kamp, A. &
Boeijinga, P. (1980). In G. Pfurtscheller & Lopes da Silva
(Eds.), Rhythmic EEG activities and cortical functioning (pp.
91-102). Amsterdam: Elsevier.
 
Baddeley, A. (1992). Working Memory. Science, 255, 5044, 556-559.
 
Baddeley, A. (1984). Neuropsychological evidence and the
semantic/ episodic distinction (Commentary to Tulving). The
Behavioral and Brain Sciences, 7, 238-239.
 
Basar, E. & Bullock, T.H. (Eds.). (1992). Induced rhythms in the
brain. Boston: Birkhaeuser.
 
Basar, E. & Stampfer, H.G. (1985). Important associations among
EEG-dynamics, event-related potentials, short-term memory and
learning. International Journal of Neuroscience, 26 (3-4),
161-180.
 
Berger, H. (1929). Ueber das Elektroenkephalogramm des Menschen.
Archiv fuer Psychiatrie und Nervenkrankheiten, 87, 527-570.
 
Birbaumer, N. & Elbert, Th. (1988). P3: Byproduct of a byproduct.
Behavioral and Brain Sciences, 11(3), 375-377.
 
Braitenberg, V. & Schuez, A. (1991). Anatomy of the cortex. New
York: Springer Verlag.
 
Carpenter, G.A. & Grossberg, S. (1993). Normal and amnesic
learning, recognition and memory by a neuronal model of
cortico-hippocampal interactions. TINS, 16, 131-137.
 
Donchin, E. & Coles, M.G.H. (1988). Is the P300 component a
manifestation of context updating? Behavioral and Brain Sciences,
11, 357-374.
 
Elbert, T. (1992). A theoretical approach to the late components
of the event-related brain potential. In A. Aertsen & V.
Braitenberg (Eds.). Information processing in the cortex
(225-245). Berlin: Springer Verlag.
 
Engel, A.K., Koenig, P., Kreiter, A., Schillen, Th. & Singer, W.
(1992). Temporal coding in the visual cortex: new vistas on
integration in the nervous system. TINS, 15(6), 218-226.
 
Fabiani, M., Karis, D. & Donchin, E. (1986). P300 and recall in
an incidental memory paradigm. Psychophysiology, 23 (3), 298-308.
 
Fabiani, M., Karis, D. & Donchin, E. (1990). Effects of mnemonic
strategy manipulation in a Von Restorff paradigm.
Electroencephalography and clinical Neurophysiology, 75, 22-35.
 
Fox, S.E. & Ranck, J.B. Jr. (1981). Electrophysiological
characteristics of hippocampal complex-spike cells and theta
cells. Experimental Brain Research, 41, 399-410.
 
Freund, T. & Antal, M. (1988). Hippocampal interneurons. Nature,
336, 170-173.
 
Friedman, D. (1990a). Cognitive event-related potential
components during continuous recognition memory for pictures.
Psychophysiology, 27, 136-148.
 
Friedman, D. (1990b). ERPs during continuous recognition memory
for words. Biological Psychology, 30, 51-87.
 
Gray, Ch.M., Koenig, P., Engel, A.K. & Singer, W. (1989).
Oscillatory responses in cat visual cortex exhibit inter-columnar
synchronization which reflects global stimulus properties.
Nature, 338, 335-337.
 
Gray, C. & Singer, W. (1987). Stimulus-dependent neuronal
oscillations in the cat visual cortex area 17. Neuroscience
(Supplement), 22, 434
 
Green, J.D. & Arduini, A.A. (1954). Hippocampal electrical
activity in arousal. Journal of Neurophysiology, 17, 533-547.
 
Greenstein, Y.J., Pavlides, C. & Winson, J. (1988). Long-term
potentiation in the dentate gyrus is preferentially induced at
theta rhythm periodicity. Brain Research, 438, 331-334.
 
Grossberg, S. (1980). How Does a Brain Build a Cognitive Code?
Psychological Review, 87, 1-51.
 
Grossberg, S. & Stone, G. (1986). Neuronal Dynamics of Word
Recognition and Recall: Attentional Priming, Learning, and
Resonance. Psychological Review, 93, 46-74.
 
Hebb, D.O. (1949). The first stage of perception. In D.O. Hebb
(Ed.), The Organization of behavior (pp. 60-78). New York: Wiley.
 
Hoehl-Abrahao, J.C. & Creutzfeldt, O.D. (1991). Topographical
mapping of the thalamocortical projections in rodents and
comparison with that in primates. Experimental Brain Research,
87, 283-294.
 
Johnson, R.Jr. (1986). A triarchic model of P300 amplitude.
Psychophysiology, 23, 367-384.
 
Johnson, R., Pfefferbaum, A. & Kopell, B.S. (1985). P300 and
long-term memory: Latency predicts recognition performance.
Psychophysiology, 22 (5), 497-507.
 
Karis, D., Bashore, T., Fabiani, M., & Donchin, E. (1982). P300
and memory. Psychophysiology, 19, 328.
 
Karis, D., Fabiani, M. & Donchin, E. (1984). "P300" and memory:
Individual differences in the von Restorff effect. Cognitive
Psychology, 16, 177-216.
 
Klimesch, W. (1987). A connectivity model for semantic
processing. Psychological Research, 49, 53-61.
 
Klimesch, W. (1994). The structure of long-term memory: A
connectivity model of semantic processing. Hillsdale, NJ,
Lawrence Erlbaum.
 
Klimesch, W., Schimke, H. & Ladurner, G. (1988). Die Suchzeit
fuer episodische und semantische Information. Sprache &
Kognition, 7, 129-143.
 
Klimesch, W., Schimke, H., Ladurner, G. & Pfurtscheller, G.
(1990). Alpha frequency and memory performance. Journal of
Psychophysiology, 4, 381-390.
 
Klimesch, W., Schimke, H. & Pfurtscheller, G. (1993). Alpha
frequency, cognitive load and memory performance. Brain
Topography, 5, 1-11.
 
Klimesch, W., Schimke, H., Doppelmayr, M., Ripper, B., Schwaiger,
J. & Pfurtscheller, G. (1994). Event-related desynchronization
(ERD) and the Dm-effect: Does alpha desynchronization during
encoding predict later recall performance? Submitted paper.
 
Klimesch, W., Schimke, H. & Schwaiger, J. (1994). Episodic and
semantic memory: An analysis in the theta and alpha band.
Electroencephalography and clinical Neurophysiology, in press.
 
Koestner, J. (1985). Functional consequences of passive membrane
properties of the neuron. In E.R. Kandel & J.H. Schwartz (Eds.),
Principles of neuronal science (pp. 66-74). New York: Elsevier.
 
Kroll, N.E.A. & Klimesch, W. (1992). Semantic memory. Complexity
or connectivity? Memory & Cognition, 20, 192- 210.
 
Larson, J., Wong, D. & Lynch, G. (1986). Patterned stimulation at
the theta frequency is optimal for the induction of hippocampal
long-term potentiation. Brain Research, 368, 347-350.
 
Lashley, K.S. (1950). In Search of the Engram. Society for
Experimental Biology, Symposium, No. 4, 454-482.
 
Leung, L. (1980). Behavior-dependent evoked potentials in the
hippocampal CA1 region of the rat. I. Correlation with behavior
and EEG., Brain Research, 198, 95-117.
 
Lopes da Silva, F.H. (1992). The rhythmic slow activity (theta)
of the limbic cortex: An oscillation in search of a function. In
E. Basar & T.H. Bullock (Eds.), Induced rhythms in the brain (pp.
83-102). Boston: Birkhaeuser.
 
Lopes da Silva, F.H., Witter, M.P., Boeijinga, P.H. & Lohman,
A.H.M. (1990). Anatomic organization and physiology of the limbic
cortex. Physiological Reviews, 70, 453-511.
 
Markowitsch, H.J. (1983). Transient global amnesia. Neuroscience
and Biobehavioral Reviews, 7, 35-43.
 
Markowitsch, H.J. (1984). Can amnesia be caused by damage of a
single brain structure? Cortex, 20, 27-45.
 
Markowitsch, H.J. & Pritzel, M. (1985). The neuropathology of
amnesia. Progress in Neurobiology, 25, 189-288.
 
Miller, R. (1991). Cortico-hippocampal interplay and the
representation of contexts in the brain. Berlin: Springer Verlag.
 
Neville, H.J., Kutas, M., Chesney, G. & Schmidt, A.L. (1986).
Event-related brain potentials during initial encoding and
recognition memory of congruous and incongruous words. Journal of
Memory and Language, 25, 75-92.
 
Noldy, N.E., Stelmack, R.M. & Campbell, K.B. (1990).
Event-related potentials and recognition memory for pictures and
words: The effects of intentional and incidental learning.
Psychophysiology, 27 (4), 417-428.
 
Paller, K.A. (1993). Elektrophysiologische Studien zum
menschlichen Gedachtnis. EEG & EMG, 24, 24-33.
 
Paller, K.A., Kutas, M. & Mayes, A.R. (1987). Neuronal correlates
of encoding in an incidental learning paradigm.
Electroencephalography and clinical Neurophysiology, 67, 360-371.
 
Petsche, H. & Rappelsberger, P. (1992). Is there any message
hidden in the human EEG? In E. Basar, & T.H. Bullock (Eds.),
Induced rhythms in the brain (pp. 103-116). Boston: Birkhaeuser.
 
Petsche, H., Stumpf, C. & Gogolak, G. (1962). The significance of
the rabbit's septum as a relay station between the midbrain and
the hippocampus. Electroencephalography and Clinical
Neurophysiology, 19, 25-33.
 
Pfurtscheller, G. (1992). Event-related synchronization (ERS): an
electrophysiological correlate of cortical areas at rest.
Electroencephalography and clinical Neurophysiology, 83, 62-69.
 
Pfurtscheller, G. & Klimesch, W. (1991). Event-related
desynchronization during motor behavior and visual information
processing. In C.H.M. Brunia, G. Mulder, & M.N. Verbaten (Eds.),
Event-related brain research (pp. 58-65). Amsterdam: Elsevier.
 
Pfurtscheller, G. & Klimesch, W. (1992). Functional topography
during a visuoverbal judgment task studied with event-related
desynchronization mapping. Journal of Clinical Neurophysiology,
9, 120-131.
 
Pfurtscheller, G., Flotzinger, D. & Neuper, Ch. (1994).
Differentiation between finger, toe and tongue movement in man
based on 40-Hz EEG. Electroencephalography and clinical
Neurophysiology, 90, 456-460.
 
Polich, J. & Squire, L.R. (1993). P300 from amnesic patients with
bilateral hippocampal lesions. Electroencephalography and
clinical Neurophysiology, 86, 408-417.
 
Pulvermueller, F., Preissl, H., Eulitz, C., Pantev, Ch.,
Lutzenberger, W., Elbert, Th. & Birbaumer, N. (1994). Brain
Rhythms, Cell Assemblies and Cognition: Evidence From the
Processing of Words and Pseudowords. PSYCOLOQUY 5(48)
brain-rhythms.1.pulvermueller.
 
Sanquist, Th.F., Rohrbaugh, J.W., Syndulko, K. & Lindsley, D.B.
(1980). Electrocortical signs of levels of processing: Perceptual
analysis and recognition memory. Psychophysiology, 17, 568-576.
 
Scoville, W.B. & Milner, B. (1957). Loss of recent memory after
bilateral hippocampal lesions. Journal of Neurology, Neurosurgery
and Psychiatry, 20, 11-21.
 
Smith, M.E. & Halgren, E. (1989). Dissociation of Recognition
Memory Components Following Temporal Lobe Lesions. Journal of
Experimental Psychology: Learning, Memory, and Cognition, 15,
50-60.
 
Squire, L.R. (1992). Memory and the hippocampus: A synthesis from
findings with rats, monkeys, and humans. Psychological Review,
99, 195-231.
 
Squire, L.R., Knowlton, B. & Musen, G. (1993). The structure and
organization of memory. Annual Review of Psychology, 44, 453-495.
 
Steriade, M., Jones, E. & Llinas, R. (1990). Thalamic
oscillations and signaling. New York: John Wiley.
 
Stewart, M. & Fox, S.E. (1990). Do septal neurons pace the
hippocampal theta rhythm? Trends in Neuroscience, 13, 163-168.
 
Strogatz, St. & Stewart, I. (1993). Coupled oscillators and
biological synchronization. Scientific American, 269(6), 68-75.
 
Teyler, T.J. & DiScenna, P. (1986). The hippocampal memory
indexing theory. Behavioral Neuroscience, 100, 147-154.
 
Traub, R., Miles, R. & Wong, R. (1989). Model of the origin of
rhythmic population oscillations in the hippocampal slice.
Science, 243, 1319-1325.
 
Tulving, E. (1984). Precis of elements of episodic memory. The
Behavioral and Brain Sciences, 7, 223-268.
 
Vanderwolf, C. & Robinson, T. (1981). Retico-cortical activity
and behavior: A critique of the arousal theory and a new
synthesis. Behavioral and Brain Sciences, 4, 459-514.
 
Verleger, R. (1988). Event-related potentials and cognition: A
critique of the context updating hypothesis and an alternative
interpretation of P3. Behavioral and Brain Sciences, 11, 343-427.
