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connectionism and models of memory and amnesia

Connectionism and models of memory and amnesia

Jaap Murre

University of Amsterdam


The French neurologist Ribot discovered more than 100 years ago that in retrograde amnesia one tends to loose recent memoriesMemory loss gradients in RA are called Ribot gradients

  • Catastrophic interference and hypertransfer
  • Brief review of neuroanatomy
  • Outline of the TraceLink model
  • Some simulation results of neural network model, focussing on retrograde amnesia
  • Recent work:
    • Mathematical point-process model
    • Detailed, more biological, neural network model
  • Concluding remarks
catastrophic interference
Catastrophic interference
  • Learning new patterns in backpropation will overwrite all existing patterns
  • Rehearsal is necessary
  • McCloskey and Cohen (1989), Ratcliff (1990)
  • This is not psychologically plausible
osgood surface 1949
Osgood surface (1949)
  • Paired-associates in lists A and B will interfere strongly if the stimuli are similar but the responses vary
  • If stimuli are different, little interference (i.e., forgetting) occurs
  • Backpropagation also shows odd behavior if stimuli vary but responses are similar in lists A and B (hypertransfer)

Learned responses

Stimuli Target responses (after three learning trials)

Phase 1: Learning list A

rist munk twup

gork gomp toup

wemp twub twup

Phase 2: Learning interfering list B

(after five learning trials)

yupe munk muup

maws gomp twup

drin twub twub

Phase 3: Retesting on list A

rist munk goub

gork gomp tomp

wemp twub twub

problems with sequential learning in backpropagation
Problems with sequential learning in backpropagation
  • Reason 1: Strongly overlapping hidden-layer representations
  • Remedy 1: reduce the hidden-layer representations
    • French, Murre (semi-distributed representations)
problems with sequential learning in backpropagation8
Problems with sequential learning in backpropagation
  • Reason 2: Satisfying only immediate learning constraints
  • Remedy 2: Rehearse some old patterns, when learning new ones
    • Murre (1992): random rehearsal
    • McClelland, McNaughton and O’Reilly (1995): interleaved learning
final remarks on sequential learning
Final remarks on sequential learning
  • Two-layer ‘backpropagation’ networks do show plausible forgetting
  • Other learning networks do not exhibit catastrophic interference: ART, CALM, Kohonen Maps, etc.
  • It is not a necessary condition of learning neural networks; it mainly affects backpropagation
  • The brain does not do backpropagation and therefore does not suffer from this problem
models of amnesia and memory in the brain
Models of amnesia and memory in the brain
  • TraceLink
  • Point-process model
  • Chain-development model
neuroanatomy of amnesia
Neuroanatomy of amnesia
  • Hippocampus
  • Adjacent areas such as entorhinal cortex and parahippocampal cortex
  • Basal forebrain nuclei
  • Diencephalon

Hippocampus has an

excellent overview

of the entire cortex

system 1 trace system
System 1: Trace system
  • Function: Substrate for bulk storage of memories, ‘association machine’
  • Corresponds roughly to neocortex
system 2 link system
System 2: Link system
  • Function: Initial ‘scaffold’ for episodes
  • Corresponds roughly to hippocampus and certain temporal and perhaps frontal areas
system 3 modulatory system
System 3: Modulatory system
  • Function: Control of plasticity
  • Involves at least parts of the hippocampus, amygdala, fornix, and certain nuclei in the basal forebrain and in the brain stem
dreaming and consolidation of memory
Dreaming and consolidation of memory

“We dream in order to forget”

  • Theory by Francis Crick and Graeme Mitchison (1983)
  • Main problem: Overloading of memory
  • Solution: Reverse learning leads to removal of ‘obsessions’
dreaming and memory consolidation
Dreaming and memory consolidation
  • When should this reverse learning take place?
  • During REM sleep
    • Normal input is deactivated
    • Semi-random activations from the brain stem
    • REM sleep may have lively hallucinations
consolidation may also strengthen memory
Consolidation may also strengthen memory
  • This may occur during deep sleep (as opposed to REM sleep)
  • Both hypothetical processes may work together to achieve an increase in the definition of representations in the cortex
recent data by matt wilson and bruce mcnaughton 1994
Recent data by Matt Wilson and Bruce McNaughton (1994)
  • 120 neurons in rat hippocampus
  • PRE: Slow-wave sleep before being in the experimental environment (cage)
  • RUN: During experimental environment
  • POST: Slow-wave sleep after having been in the experimental environment
wilson en mcnaughton data
Wilson en McNaughton Data
  • PRE: Slow-wave sleep before being in the experimental environment (cage)
  • RUN: During experimental environment
  • POST: Slow-wave sleep after having been in the experimental environment
some important characteristics of amnesia
Some important characteristics of amnesia
  • Anterograde amnesia (AA)
    • Implicit memory preserved
  • Retrograde amnesia (RA)
    • Ribot gradients
  • Pattern of correlations between AA and RA
    • No perfect correlation between AA and RA

Normal forgetting









an example of retrograde amnesia patient data
An example of retrograde amnesia patient data

Kopelman (1989) News events test

retrograde amnesia
Retrograde amnesia
  • Primary cause: loss of links
  • Ribot gradients
  • Shrinkage
anterograde amnesia
Anterograde amnesia
  • Primary cause: loss of modulatory system
  • Secondary cause: loss of links
  • Preserved implicit memory
connectionist implementation of the tracelink model

Connectionist implementationof the TraceLink model

With Martijn Meeter from the University of Amsterdam

some details of the model
Some details of the model
  • 42 link nodes, 200 trace nodes
  • for each pattern
    • 7 nodes are active in the link system
    • 10 nodes in the trace system
  • Trace system has lower learning rate that the link system
how the simulations work one simulated day
How the simulations work: One simulated ‘day’
  • A new pattern is activated
  • The pattern is learned
  • Because of low learning rate, the pattern is not well encoded at first in the trace system
  • A period of ‘simulated dreaming’ follows
    • Nodes are activated randomly by the model
    • This random activity causes recall of a pattern
    • A recalled pattern is than learned extra
patient data
(Patient data)

Kopelman (1989) News events test

strongly and weakly encoded patterns
Strongly and weakly encoded patterns
  • Mixture of weak, middle and strong patterns
  • Strong patterns had a higher learning parameter (cf. longer learning time)
other simulations
Other simulations
  • Focal retrograde amnesia
  • Levels of processing
  • Transient Global Amnesia (TGA)
  • Semantic dementia
  • Implicit memory
  • More subtle lesions (e.g., only within-link connections, cf. CA1 lesions)
the memory chain model a very abstract neural network

The Memory Chain Model: a very abstract neural network

With Antonio Chessa from the University of Amsterdam

abstracting tracelink level 1
Abstracting TraceLink (level 1)
  • Model formulated within the mathematical framework of point processes
  • Generalizes TraceLink’s two-store approach to multiple neural ‘stores’
    • trace system
    • link system
    • working memory, short-term memory, etc.
  • A store corresponds to a neural process or structure
learning and forgetting as a stochastic process 1 store example
Learning and forgetting as a stochastic process: 1-store example
  • A recall cue (e.g., a face) may access different aspects of a stored memory
  • If a point is found in the neural cue area, the correct response (e.g., the name) can be given







single store point process



Link system


Survival probability

Single-store point process
  • The expected number of points in the cue area after learning is called 
  • This  is directly increased by learning and also by more effective cueing
  • At each time step, points die
  • The probability of survival of a point is denoted by a
some aspects of the point process model
Some aspects of the point process model
  • Model of simultaneous learning and forgetting
  • Clear relationship between signal detection theory (d'), recall (p), savings (Ebbinghaus’ Q), and Crovitz-type distribution functions
  • Multi-trial learning and multi-trial savings
  • Currently applied to over 250 experiments in learning and forgetting, since 1885
forgetting curve
Forgetting curve

If we need to find at least one point we obtain the following curve (one-store case):

m is the intensity of the process (expected number

of points) and a is the decay parameter

We predict a flex point when the initial recall is

at least

multi store generalization
Multi-store generalization
  • Information about the current event passes through many neural ‘stores’
  • The retina, for example, holds a lot of information very briefly
  • The cerebral cortex holds very little information (of the current event) for a very long time
general principles of the ppm multi store model
General principles of the PPM multi-store model
  • A small part of the information is passed to the next store before it decays completely
  • Subsequent stores hold information for longer time periods: slower decay rates in ‘higher’ stores
two store model
Two-store model
  • While neural store 1 is decaying (with rate a1) it induces new points (representations) in store 2
  • Induction rate is linear with the intensity in store 1 and has induction rate m2
  • The points in store immediately start to decay as well (at a lower rate a2)
example of two neural stores
Example of two neural stores
  • Store 1: firing neural groups
  • Store 2: synaptic connections between the neural groups
  • Other interpretation are possible as well, e.g.:
    • Store 1: hippocampus
    • Store 2: cerebral cortex


recall probability p t as a function of different learning times l
Recall probability p(t) as a function of different learning times l

n is the learning rate

l is the learning time

r(t) is the decline function

t time since learning

hellyer 1962 recall as a function of 1 2 4 and 8 presentations
Hellyer (1962). Recall as a function of 1, 2, 4 and 8 presentations


Two-store model with saturation. Parameters are

m1= 7.4, a1= 0.53, m2= 0.26, a2= 0.31, rmax= 85; R2=.986

amnesia animal data

Amnesia: animal data

Retrograde amnesia

frankland et al 2001 study
Frankland et al. (2001) study
  • a-CaMKB-dependent plasticity (in neocortex) switched off in knock-out mice
  • No LTP measurable in neocortex but LTP in hippocampus was largely normal
  • Forgetting curves with different levels of initial learning were measured
  • A learning curve was measured
  • Assumption: use r1[2](t) for knock-out mice
application to advertising data
Application to advertising data
  • Advertisements as learning trials
  • Zielske (1958): recall of printed advertisements
  • SPOT study: TV commercials of over 40 brands
zielske 1958
Zielske 1958
  • Printed advertisements were mailed repeatedly to randomly selected house wives
  • After a week they were called
  • The memory for the advertisements was checked
  • Mailings were carried out weekly (massed) or every four weeks (distributed)
spot data
SPOT data
  • Research carried out by SPOT
  • 43 brands were followed for half a year
  • 50 phone calls per week per brand
  • Gross Rating Points (GRPs) were matched
  • A GRP is a measure for how many people will probably view the commercial
memory impact as a function of advertising
Memory (impact) as a function of advertising



Memory (impact)



spot brand with 1 store model
SPOT brand with 1-store model

Base rate



  • Advertisements are learning trials
  • Their learning and forgetting can be described by our model
  • If a good fit can be achieved, more optimal advertising schedules can be derived
concluding remarks
Concluding remarks
  • Given that the brain is exceedingly complex, we need models at various levels of abstraction to aid our understanding
  • This is especially true when trying to unravel the link between the brain and human behavior, which is extremely complex itself
  • Hence, models are of particular use in the new, interdisciplinary field of cognitive neuroscience