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     MindNet Journal - Vol. 1, No. 62a * [Part 1 of 4 parts]
     V E R I C O M M / MindNet         "Quid veritas est?"

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Editor: Mike Coyle 

Assistant Editor: Rick Lawler

Research: Darrell Bross



By Wolfgang Klimesch

Copyright 1995 Wolfgang Klimesch

Reprinted by permission from:

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,


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

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,

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

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).


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.


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).


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.


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.

[Continued to part 2]
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