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1
Teil B Grundlagen kortikaler Informationsverarbe
itung The substrates of information processing
Neurons, structural functional processing
principles the flow of information in the
brain. When studying information processing in
the brain, knowledge about neurons, neuronal
signal processing and neural networks is of
outstanding importance. The structure of
neurons and type of cells
2
Figure 2.2 Idealized mammallian neuron. The cell
body contains the cellular machinery for the
production of proteins and other cellular
macromolecules. Like other cells, the neuron
contains a nucleus, endoplasmatic reticulum,
ribosomes, mitochondria, Golgi apparatus, and
other intracellular organelles. These are
suspended in the intracellular fluid - cytoplasm
- and are contained by a cell membrane. Extending
form the cell body are various processes that are
extensions of the cell membrane and contain
cytoplasm that is continuous with that in the
cell body. These processes are called dendrites
and axons.
3
Figure 2.3 A dendritic tree of a Purkinje cell
from the cerebellum. The Purkinje cells are
arrayed in rows in the cerebellum. They have a
large dendritic tree that is wider in one
direction than the others. Adapted from
Carpenter (1976).
Figure 2.4 Diagrammatic ventral horn motor
neuron. The multipolar neurons are located in the
spinal cord, and send their axons out the ventral
root to make synapses on muscle fibers.
4
Figure 2.6 Various forms that mammalian neurons
may take. Some have few and others many processes
extending from their cell bodies. These
diagrammatic neurons are shown with short axons,
which is not intended to be illustrative of all
neurons. For example, motor neurons in the spinal
cord have axons that are a meter or more in
length, depending on the muscle innervated or the
animal or human in question. Adapted from Kandel
et al. (1991).
5
Different types of glial cells
Figure 2.7
6
BioII Part B, SS03, Klimesch, Page 7
Central NS
Peripheral NS
Figure 2.8
7
Figure 2.10
8
Figure 2.12
9
Active and passive current conductions
Figure 2.13
Figure 2.14
Figure 2.15
10
Page 37
11
Ohms law V IR V..... Voltage
I...... Strength of current R......
Resistance I V/R
Figure 2.16
12
Further examples of electrical properties of
neurons
Figure 2.17
13
Figure 2.18
Figure 2.19
14
Figure 2.21
15
Figure 2.23
16
BioII Part B, SS03, Klimesch, Page 18
17
Excitatory Postsynaptic Potential (EPSP)
See Textbook, p.55 Fig. 2.30
18
Figure 2.33 Activity of two cortical interneurons
in the rat somatosensory cortex connected by
electrical synapses. (a) Electrodes were placed
in two coupled neurons, and a current whose
amplitude varied at about 40Hz was injected into
cell 1 while cell 2 was maintained below
spontaneous spike threshold with constant
current. Cell 2 showed a tendency to spike at the
peaks of the depolarizing phases in cell 1. (b)
Depolarizing current was injected into two
different neurons simultaneously, and the neurons
were found to spike in synchrony. (c) To test
the synchrony of the firing of the two neurons,
the cross-correlogram method was used. The
activity of cell 2 was cross-correlated with the
firing of cell 1. If they were firing in
synchrony, the cross correlogram should show a
strong peak around 0 msec, and this was indeed
observed. The peak is at 2 msec, demonstrating a
very close coupling between the electrical
activity in the two neurons. These neurons did
not have chemical synaptic interactions. Adapted
form Gibson et al. (1999).
19
S1
S2
t1 0
t2 10
t3 20
40
- 20
-40
0
20
20
BioII Part B, SS03, Page 23
The Action Potential (AP) and principles of
neuronal encoding
50 40 30 20 10 0 - 10 - 20 - 30 -
40 - 50 - 60 - 70
Membrane potential in millivolt
Depolarization
1 millisecond
Resting potential
Hyperpolarization
21
The Nernst equation allows the calculation of the
potential difference across the membrane that
will exist if a certain type of ion has reached a
stable concentration on both sides of the
membrane.
Values of I (in millimol/liter) for Na and K
in the squid Na K Inside 60
400 Outside 436
20
I out
RT
log
V
I in
kF
58
For K the equation gives a value of V - 75
mV For Na the equation gives a value of V
50 mV
22
6
5
4
3
2
1
Sodium (Na) channels
Potassium (K) channels
23
The gill retraction reflex (a), habituation (b)
and dishabituation (c) A model for learning in
Aplysia (cf. Squire Kandel , 1999. Gedächtnis.
Heidelberg Spektrum Verlag). a) Sensory
stimulation (S1) of syphon leads to retraction of
gills (gill retraction reflex). b) After
repeated sensory stimulation no retraction takes
place. c) If - after habituation - a strong
and/or painful stimulus (S2) precedes
stimulation of syphon (S1), a strong gill
retraction can be observed.
Syphon, sensory neuron
Motoneuron Gill
Postsynaptic membrane Receptors
Vesicles holding Glutamat
24
(a) Gill retraction reflex
(b) Habituation
25
(c) Dishabituation
Modulating Neuron Interneuron
RELEASE OF SEROTONIN at Axo-axonal synapse in
response to S2 closes K-channels.
S2 Painful stimulus preceding S1
S1
The closing of K-channels (potassium channels)
leads to a widening of the AP and, thus, to a
stronger depolarization, which in turn leads to
a stronger release of transmitter substance.
26
Encoding processes and action potentials (APs)
Basic findings The sequence of (interval
between) APs encodes information. The example
hows APs recorded from five neurons. The APs
apparently occur in irregular intervals and,
thus, each neuron encodes different information.
27
In this example, each neuron encodes the same
information
28
This oscillation enables information processing.
It is the driving force of triggering APs.
29
BioII Part B, SS03, Page 32
This oscillation blocks information processing
30
Long Term Potentiation (LTP), Learning and Hebb
role When an axon of cell A is near enough to
excite a cell B and repeatedly or persistently
takes part in firing it, some growth process or
metabolic change takes place in one or both cells
such that As efficiency, as one of the cells
firing B, is increased. Today it is well
established that complex, enriched environments
induce experience-dependent neuroplasticity
(morphological changes) in the hippocampus of
rodents which include - increase in volume of
hippocampus - increase in dendritic
arborization - increase in the number of glia
cells and an - increase in the number of granule
cells (Körnerzellen) throughout the entire life
span c.f. Kemperman et al. (1997), Nature, Vol.
386, 493-495.
31
Spike time-dependent plasticity (STDP) in
cortex Synapses undergo long-term potentiation
(LTP) when the excitatory postsynaptic potential
(EPSP) precedes the postsynaptic spike.
LTP
Time
Synapses strengthen activity that is causally
related to postsynaptic firing. Froemke RC, Dan
Y (2002) Spike-timing-dependent synaptic
modification induced by natural spike trains.
Nature 416433 438.
32
Fig. 51. Comparison of the frequency
distribution of the different lengths of
post-synaptic thickenings in adult (black
columns) and just prenatal guinea pigs (white
columns). The two distributions are not
signigicantly different. (Schüz 1981b).
33
Fig. 52. Frequency distribution of the number of
vesicles per synapse in adult (black coumns) and
just prenatal guinea pigs (white columns). There
is a significant increase in the number of
visicles with age. Note the bimodal distribution
in the adult histogram which may indicate two
kinds of synapses, perhaps such that have learned
and such that have not yet learned. (Schütz 1981
b).
34
Fig. 53. Average and standard deviation of the
number of vesicles per synapse in prenatal and
adult guinea pigs. Note the increase in the
standard deviation. (Schütz 1981 b).
35
Fig. 57. Frequency distribution of the size of
the spine heads in adult (black columns) and
newborn (white columns) guinea pigs. Teh ticker
heads are much more common in the adult. (Schütz
1981 b).
36
Fig. 58. Frequency distribution of the thickness
of spine necks in adult (black columns) and
newborn guinea pigs (white columns). The thinnest
necks are more frequent in the newborn sample.
(Schütz 1981 b)
37
Plastizität des Gehirns
38
BioII Part B, SS03, Page 39
Kortikale Vernetzung und Codierung im
Langzeitgedächtnis (LZG) Es wird heute
allgemein davon ausgegangen, dass der Kortex der
Ort des Langzeitgedächtnisses ist (Markowitsch
Pritzel, 1985 Alkon, 1988). Bei der Erklärung
von Gedächtnisprozessen sind zumindest die
folgenden zwei Annahmen unvermeidlich. Einerseits
müssen wir annehmen, dass irgendein Inhalt bzw.
Code im Gedächtnis gespeichert ist und
andererseits, dass irgendwelche Prozesse bzw.
Suchvorgänge zur Auffindung gespeicherter
Information führen. Dabei zeigen sich zwei Arten
von Repräsentationsannahmen, Strukturannahmen,
die den Aufbau eines Codes bzw. des
Speichernetzwerkes betreffen und Prozessannahmen,
die sich auf Eigenschaften des Suchvorganges
beziehen. Selbst dann, wenn beide Arten von
Annahmen nicht näher definiert werden, erfolgen
alleine durch den Mangel an Spezifizierung
Festlegungen auf impliziter Ebene by default
immer dann, wenn Interpretations- oder
Erklärungsversuche unternommen werden. Jeder, der
von Codes und Suchvorgängen spricht und keine
weiteren Festlegungen triff, kann Codes nur als
ganzheitliche Einheit auffassen, auf die
Suchprozesse zugreifen. Dies ist eine höchst
unrealistische Betrachtungsweise, wie man heute
weiß. Trifft man zusätzliche Annahmen und geht
dabei z.B. von der Vorstellung aus, dass ein Code
aus Komponenten besteht, so stellen sich sofort
weitere Fragen (z.B. welche Struktur sie
verbindet und ob z.B. Suchprozesse auf einzelne
Komponenten Zugriff haben, die zusätzliche
Spezifizierungen nach sich ziehen. Der
zentrale Gesichtspunkt bei der Festlegung von
Strukturannahmen ist, dass im Langzeitgedächtnis
(LZG) integrierte und zusammenhängende Faktoren
gespeichert sind. Die Annahme einer integrierten
Speicherung bedeutet, dass jeder Code nicht
isoliert, sondern in direkter Relation zu anderen
Codes des LZG repräsentiert ist. Je mehr
Relationen bzw. Verbindungen ein Code zu anderen
Codes aufweist, desto besser ist die Information
dieses Codes im Wissen des LZG integriert.
Integrierte Codes können am besten durch
vernetzte Strukturen, isolierte Codes hingegen
durch hierarchische Strukturen dargestellt
werden. Da der Aufbau vernetzter Strukturen Zeit
benötigt, ist kaum davon auszugehen, dass bereits
im Arbeits- oder Kurzzeitgedächtnis (KZG) stabil
vernetzte Codes vorliegen. Die Simulation von
Suchprozessen auf der Basis des
Vernetzungsmodells (Klimesch, 1994) hat gezeigt,
dass Abrufprozesse aus dem Gedächtnis umso
schneller werden, je mehr vernetzte
Informationskomponenten gespeichert sind Die
Grundidee kann dabei anhand der Abbildung auf der
nächsten Seite sehr einfach erklärt werden. Der
Suchprozess kann an einem oder mehreren Knoten
mit einer bestimmten Aktivierungsstärke a
ausgehen und fließt in der ersten
Aktivierungsstufe entlang der Kanten des
Netzwerkes zu allen n-1 Knoten (n die Anzahl
aller Knoten eines Codes). In einer zweiten
Aktivierungsstufe fließt von jedem der n-1 Knoten
zu allen anderen n-2 Knoten Aktivierungen zurück
und addiert sich mit dem Faktur n-2 auf. In der
dritten konvergierenden Aktivierungsstufe
fließt die um den Faktor n-2 verstärkte
Aktivierung von allen n-1 Knoten zum
Ausgangsknoten zurück und addiert sich dort
weiter auf. Strukturannahmen A1.) Codes sind
vernetzte Strukturen aus elementaren
Informationskomponenten. A2.) Speichernetzwerke
sind vernetzte Strukturen aus Informationskomponen
ten, in denen Codes als zyklische Substrukturen
(mit Feedback- oder Reentrant-Schleifen
Edelmann, 1989) eingebettet sind, in denen
Aktivierung wieder auf den Ausgangsknoten
zurückfließen kann. Prozessannahmen B1) Mit
zunehmender Aktivierungsstärke nimmt die
Aktivierungsgeschwindigkeit zu. B2) Die
Aktivierung erfolgt getaktet, d.h. nach einem
bestimmten, synchronen Aktivierungsmuster. B3)
Konvergierende Aktivierung führt zu einer
verstärkten und deswegen rascheren Aktivierung
39
Beispiel eines semantisch-en Netzwerkes, das nach
den Annahmen des Ver-netzungsmodells aufge-baut
ist. Codes sind zyk-lische Strukturen, die durch
ihre Vernetzung mit anderen Codes ein komp-lexes
Speichernetzwerk ergeben. Ein Suchvorgang beginnt
mit einer Akti-vierung an einem oder mehreren
Ausgangsknoten (gekennzeichnet durch einen
zusätzlichen schwarzen Ring) und endet sobald
indirekte (zyklische) Aktivierung zu dem(n)
Ausgangsknoten zurückfließt. Die Zahlen in den
Knoten geben Aktivierungswerte an, die bei einem
Suchprozess entstehen, der vom Aus-gangsknoten
BIRD ausgeht und sich über die Merkmalsknoten
(beak, fly, feather and f(wing1)) ausbreitet.
Die Aktivierungsstärke der zum Ausgangsknoten
zurückfließenden indirekten, zyklischen
Aktivierung I wird nach Formel 8.2 (Klimesch,
1994) berechnet I a b (n 2) (n 1)
wobei n die Anzahl der Knoten eines Codes, a
die Aktivierungsstärke in der 2.Aktivierungsstufe
(Taktung) bedeutet. Der Code für BIRD hat n 5
Knoten. Wenn für a b 1 gesetzt wird und
angenommen wird, dass die Ausgangsaktivierung (a
b 1) an allen Knoten erhalten bleibt, dann
ist das Ausmaß der indirekten, zyklischen
Aktivierung I 17.
40
Information Flow in the Brain Differences in the
cell architecture of the cortex (cf. example in
Fig. 3.6) has led to Brodmanns classification
Figure 1.7 The fifty-two distinct areas described
by Brod-mann based on cell structure and
arrange-ment. Adapted from Brodmann (1909).
41
Figure 3.18 A projection map of the cerebral
cortex can be derived with anatomical tracing
techniques. The blue regions show the primary
projection areas of the sensory pathways and the
primary output region to the spinal cord. The
secondary sensory and motor areas are colored
green. Anatomical projections overlap extensively
in the tertiary areas (pink regions).
Adapted form Kolb and Whishaw (1996).
42
The speed of an AP in the visual pathway (over
long axons) may be estimated with about 20 m/sec
2 cm /ms. For a long axon of about 10 cm (eye
to LGN and LGN to Ctx) the speed of transmission
then is 5 ms. Transmission time over a neuron is
estimated by 2 ms (including synaptic
transmission). These of course are rough
estimates. On the basis of these estimates the
activation of the visual cortex does not take
place before about 50 ms. The measurement of
ERPs, however, shows that early perceptual
components can be observed around 100 ms (P1,
N1).
43
Figure 3.14 The visual cortex is located in the
occipital lobe. Area 17 of Brodmann, also called
the primary visual cortex (V1), is located at the
occipital pole, and extends onto the medial
surface of the hemisphere where it is largely
buried within the calcarine fissure.
44
Figure 3.15
45
Gross and functional anatomy
Figure 3.7
46
Figure 3.20 Major connections of the limbic
system shown diagrammatically in medial view of
right hemisphere. Adapted form Kandel et al.
(1991).
47
(a) Cross-sectional drawings through the brain at
two anterior-posterior levels (as indicated)
showing the basal ganglia. (b) Corresponding
high-resolution, structural magnetic resonance
image (4-tesla scanner) taken at the same level
as the more posterior drawing. This image also
shows the brainstem, and the skull and scalp,
which are not shown in (a). (a) Adapted form
Carpenter (1976). (b) Couresy of Dr. Allen Sond,
Duke University.
Figure 3.21
48
? Klimesch
49
The unidirectional feedforward excitatory
pathway, classical view originally proposed by
Ramon y Cajal, 1899 see Buzsaki, 1996
Black arrows indicate feedforward paths
CA1 Pyramidal Cells
Red arrows indicate recurrent (against the flow)
paths
CA3 Pyramidal Cells
Subiculum
CA4 Dentate hilar Cells
Co r t e x
Mossy Fibres
Output to
Layer V Entorhinal Cortex Layer IIIII
Granule Cells, Dentate Gyrus
Input from
Perforant pathway
SEPTUM Distributed input to all parts of
hippocampal formation
50
Figure 11. Multi unit discharges and local field
potentials in the hippocampal dentate layer. A
and B are examples of irregular discharge
patterns during alert immobility. C and D reflect
tone presentation during alert immobility. E and
F show rhythmic discharges during walking and
hopping. Note the increase in frequency from LIA
to type 1 theta and the lack of unit discharge
failures in type 1 theta. From Bland (1985).
51
Neuronal encoding of the environment (SPACE
TIME CONTEXT) by hippocampal place cells Place
cells were discovered by OKeefe Dostrovsky
(1971) For a summary see e.g., Burgess, Recce
OKeefe (1994). A model of hippocampal function.
Neural Networks, Vol. 7, Nos 6/7,
1065-1081. Microelectrodes are implanted in CA1,
Multi Unit Activity and the Local Field Potential
(EEG) are recorded. A rat is placed in a dark
cage, containing a food pellet. The rat is
exploring the cage.
52
Left Corner
Y
FOOD
Z
Right Corner
X
The basic finding is that those cells whose place
field the rat is entering will fire late in a
theta cycle, whereas those whose place field the
rat has traversed will fire earlier in the cycle.

Hippocampal Theta
0
180
Phase encoding shown in color
Z
X
Y
-
Past Present Future
Preferred phase of firing
53
Oscillations play an important role in the timing
of action potentials (APs)
APs Threshold Subthreshold oscillation
Hyperpolarizing Depolarizing
APs Threshold Suprathreshold oscillation
Hyperpolarizing Depolarizing
54
On

-
55
Experimental evidence for encoding processes in
the hippocampal formation. In an intersting study
Wilson Mc Naughton (1994 Science, Vol. 265,
676-679) trained rats to find food pellets in a
maze. Rats were implanted with microelectrodes in
the area CA1 of the hippocampus. In each rat, the
activity of 50 to 100 cells was recorded during
three conditions (i) SWS sleep before testing
( pre-behavioral sleep PRE) (ii) running (RUN)
when rats actually were searching for food (iii)
post-behavioral SWS sleep (POST) An example of
the experimental situation is shown schematically
below. X-shaped four arm maze
56
Example for place fields in different types of
mazes
Rectangular maze
Four arm maze
Rectangular maze
57
Cross-correlations be-tween pairs of CA1 cells
overlapping and non-overlapping place fields.
Note that theta oscillations can be observed
during the RUN, but not during PRE and POST SWS
sleep. Thus, during RUN the firing pattern of
encoding cells is modulated by rhythmic theta
activity. However, during POST, cells with
overlapping place fields show a highly correlated
pattern, with irregular bursts possibly modulated
by delta activity.
58
PRE
Connectivity matrix of a network of 42 CA1 cells
(represented as dots) which were selected at
random. Lines indicate positive correlations,
color strength of correlation (red high, blue
low). The first column shows all correlations,
the second shows correlations above 0.05. Note
that most of the highly correlated pairs during
RUN appear also during POST.
RUN
POST
59
Learning and Plasticity in the cortical
areas Occipital cortex (layers I-IV). Rats in
complex environment (EC), pairs in social cages
(SC), or individual cages (IC) From Squire, L.R.
(1987). Memory and brain.
Number of neurons (1 mm3) 88.000 82.000 76.0
00 70.000
Number of synapses per neuron 9000
8000 7000 6000
5000
SC
IC
EC
Number of synapses (1 mm3) 9000
8000 7000 6000
5000
EC
SC
IC
EC
SC
IC
60

• Hippocampal cell death and stress.
• Until recently it was believed that in most brain
  regions of higher mammals, neurogenesis occurs
  during a discrete period of early development
  ONLY. However, most recent evidence indicates
  that even in primates (male marmoset monkeys)
  granule cells continue to be added to the dentate
  gyrus (Gould et al., 1998, Proc. Natl. Acad. Sci
  USA, 95, 3168-3171).
• DESIGN Experimental male animals were transfered
  to an unfamiliar cage which was housed by another
  male. This resulted in an aggressive encounter
  wherein the intruder monkey assumed a subordiante
  position. The intruder monkey was then removed
  after 1hr of exposure to the resident monkey,
  injected with BrdU (Bromodeoxyuridine Uridine is
  countained in the messenger RNA and is
  incorporated into the DNA of dividing cells where
  it can be detected immunohistochemically), and
  perfused after a 2 hr survival time. These
  experimental animals were compared with controls
  that were treated similarly but were not exposed
  to a resident monkey. From the control animals
  one group (control 1) was perfused after 2 hr of
  injection of BrdU, a second group (control 2)
  after 3 weeks.
• RESULTS A single exposure to social subordinate
  stress results in a significant decrease in the
  number of BrdU-labeled cells in the dentate gyrus
  of experimental animals.
• Experimental animals 200 BrdU labeled cells
• Control 1 animals 330 BrdU labeled cells (7
  mm3)
• Decrease of 40
• Control 2 animals After 3 weeks, about 80 of
  the labelled precursors were grown up granule
  cells.
• CORTISOL MEMORY Research with humans show that
  elderly subjects with a year-to-year increase in
  cortisol level (and with explicit memory
  impairments) show a 17 reduction of hippocampal
  volume as measured by MRI (Lupien, S. 1997,
  Psychophysiology, Vol. 34, Supplement 1, S. 16).

61
Functional Anatomy of Memory - Basic terms and
definitions Memory may be considered any
neuronal system that stores information in the
human brain. In the brain, however, a huge
variety of different kinds of information is
stored. As an example, there is information
stored in the brain that determines the way we
perceive. This type of information underlies and
enables a recognition process we call
perception and not memory. Obviously, there
are two basic aspects that define memory Memory
may be defined as that neuronal system which
allows us to i) retrieve information from a
storage device and that allows us to ii) modify
stored and encode new information by
restructuring stored and/or encorporating new
information. The first aspect refers to memory
in a narrower sense that is close to the
everyday use of this term, the second aspect
refers to learning. Thus, this definition makes
clear that learning and memory are closely
interrelated terms that cannot be described
independently MEMORY Encoding retrieval of
information into and from a storage
device. LEARNING Modification of stored
information by restructuring and encoding of new
information. Empfohlene Literatur Eichenbaum,
H. (1997) Declarative Memory Insights from
Cognitive Neurobiology. Annual Review of
Psychology, 48, 547-572. Aggleton, J. P. Brown,
M. W. (1999). Episodic memory, amnesia and the
hippocampal-anterior thalamic axis. Behavioral
and Brain Sciences, 22, 425-489.
62
Different types of memories and learning
processes Important criteria for distinguishing
different types of meories and learning processes
are based on a) the capacity of memory with
respect to time and the amount of stored
information, b) the type of information stored in
memory, c) the extent of modifying encoding and
retrieval by will. ad a) The best known and
widely accepted distinction is that into a short
and long-term memory (STM and LTM). This
distinction reflects a sequence of complex
encoding processes. Because this sequence of
encoding processes requires monitoring functions
that lie within the capacity limits of STM, the
term working memory (WM) is used in more recent
literature instead of STM. Ad b) Well known
examples are visual or verbal memory. Note that
type of information also plays an important role
for criteria listed under c). ad c) Automatic
versus controlled encoding retrieval Another
important issue is to what extent the encoding
and retrieval of information can be consciously
controlled and influenced by will. Our everyday
understanding of memory and learning is that we
have more or less full control over encoding and
retrieval. This, however, is not generally true
because of several different reasons (i)
Encoding (as a process of recognition) is to a
large degree automatic because the activation of
information in LTM is an automatic process.
Example (tachistoscopic) perception. (ii)
Encoding in the classical sense of conditioning
is an automatic process. For a long time in the
history of psychology conditioning was almost
synonymous with learning. (iii) Retrieval of
other types of information, such as instincts may
also be automatic.
63
Thus, criterion c) leads to the distinction of
Explicit versus implicit memory (or information).
Explicit infomation is encoded more or less by
controlled processes, implicit information by
automatic processes. Thus, explicit information
is directly accessible to conscious
retrieval. EXPLICIT INFORMATION
IMPLICIT INFORMATION (MEMORY) (MEMORY) ? ?
episodic and semantic Conditioned responses
information Motor skills, Instincts, Procedu
ral knowledge, Priming episodic (and
autonoetic memory in particular) reflects the
highest degree of controlled encoding
and retrieval processes.
Similar Classification DECLARATIV
E INFO. NON DECLARATIVE
INFO. (MEMORY) (MEMORY) (Information that is
directly accessible to conscious retrieval and
can be declared. Thus, it can easily expressed
verbally) ? ? Episodic and semantic
information Procedural knowledge,
Priming The terms declarative and procedural
knowledge were first used in artificial
intelligence (e.g., Winograd, 1975), then in
cognitive psychology (e.g., Anderson, 1976)
before they were applied to biological memory
studies. See the review in Squire (1987), Memory
and Brain, Oxford Univ. Press)
64
Memory systems Illustrations
Explicit Memory
Implicit Memory Declarative
Memory Non Declarative Memory
Episodic Semantic M. Procedural
M. Priming Memory
(Knowledge S.)
For a review see Markowitsch, H. (1996).
Neuropsychologie des Gedächtnisses. Spektrum d.
Wiss., Sept., 52-61
Coding ( Hippocampus and other parts of
Basal ganglia Cortex Consolidation)
limbic system
Cerebellum
Cortex
Storage Cortex
(association areas) Basal ganglia
Cortex
Cerebellum Retrieva
l Frontal right Frontal left Basal
ganglia Cortex
Cerebellum
) In addition to frontal, fronto-temporal areas
might also be involved
65
Tulvings HERA (hemispheric encoding/retrieval
asymmetry) model. (e.g. Tulving, E. et al.
(1994), Pr. N.A.C, 91, 2016-2020) The left
prefrontal cortex is differentially more invloved
in retrieval of information from semantic memory
and in simultaneously storing novel aspects of
the retrieved information into episodic memory,
than is the right prefrontal cortex. The right
prefrontal cortex is differentially more invloved
in retrieval of information from episodic memory,
than is the left prefrontal cortex
Semantic retrieval Storage (encoding) of new
episodic info.
Episodic retrieval
66
Brain circuits for encoding and
consolidation Papez Circuit
Basolateral
Circuit
Gyrus cinguli
Cingulum Anterior Thalamus
Fornix Mammilary
body Hippocampus
Mediodorsal Thalamus
Basal Forebrain
Amygdala