Loading in 5 sec....

Connectionism and models of memory and amnesiaPowerPoint Presentation

Connectionism and models of memory and amnesia

- By
**plato** - Follow User

- 127 Views
- Uploaded on

Download Presentation
## PowerPoint Slideshow about ' Connectionism and models of memory and amnesia' - plato

**An Image/Link below is provided (as is) to download presentation**

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript

### Connectionism and models of memory and amnesia

### Connectionist implementationof the TraceLink model

### The Memory Chain Model: a very abstract neural network

### Amnesia: animal data presentations

### Amnesia: human data for all curves (

Jaap Murre

University of Amsterdam

http://www.memory.uva.nl

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

Overview

- 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

- Concluding remarks

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)

- 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)

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

- Reason 1: Strongly overlapping hidden-layer representations
- Remedy 1: reduce the hidden-layer representations
- French, Murre (semi-distributed representations)

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

- 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

- TraceLink
- Point-process model
- Chain-development model

Neuroanatomy of amnesia

- Hippocampus
- Adjacent areas such as entorhinal cortex and parahippocampal cortex
- Basal forebrain nuclei
- Diencephalon

System 1: Trace system

- Function: Substrate for bulk storage of memories, ‘association machine’
- Corresponds roughly to neocortex

System 2: Link system

- Function: Initial ‘scaffold’ for episodes
- Corresponds roughly to hippocampus and certain temporal and perhaps frontal areas

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

“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

- 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

- 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)

- 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

- 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

- 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

An example of retrograde amnesia patient data

Kopelman (1989) News events test

Retrograde amnesia

- Primary cause: loss of links
- Ribot gradients
- Shrinkage

Anterograde amnesia

- Primary cause: loss of modulatory system
- Secondary cause: loss of links
- Preserved implicit memory

Semantic dementia

- The term was adopted recently to describe a new form of dementia, notably by Julie Snowden et al. (1989, 1994) and by John Hodges et al. (1992, 1994)
- Semantic dementia is almost a mirror-image of amnesia

Neuropsychology of semantic dementia

- Progressive loss of semantic knowledge
- Word-finding problems
- Comprehension difficulties
- No problems with new learning
- Lesions mainly located in the infero-lateral temporal cortex but (early in the disease) with sparing of the hippocampus

No consolidation in semantic dementia

Severe loss of trace

connections

Stage-2 learning proceeds

as normal

Stage 3 learning strongly

impaired

Non-rehearsed memories

will be lost

Semantic dementia in TraceLink

- Primary cause: loss of trace-trace connections
- Stage-3 (and 4) memories cannot be formed: no consolidation
- The preservation of new memories will be dependent on constant rehearsal

With Martijn Meeter from the University of Amsterdam

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’

- 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)

Kopelman (1989) News events test

Strongly and weakly encoded patterns

- Mixture of weak, middle and strong patterns
- Strong patterns had a higher learning parameter (cf. longer learning time)

Transient Global Amnesia (TGA)

- (Witnessed onset) of severe anterograde and retrograde amnesia
- Resolves within 24 hours
- Retrograde amnesia may have Ribot gradients
- Hippocampal area is most probably implicated

Other simulations

- Focal retrograde amnesia
- Levels of processing
- Semantic dementia
- Implicit memory
- More subtle lesions (e.g., only within-link connections, cf. CA1 lesions)

With Antonio Chessa from the University of Amsterdam

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

- 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

Forgetting

Successful

Recall

Unsuccessful

Recall

Learning

Jo Brand example

Neural network interpretationm example

a

Link system

Retrieval

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 example

- 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 example

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

Example: Single-store model fitted to short-term forgetting data

R2 = 0,985

Multi-store generalization data

- 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 data

- 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 data

- 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 data

- 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

Skip

The contributions of dataS individual neural stores can simply be added

Two-store model retention function: datar12(t)= r1(t)+ r2(t)

Recall probability model 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

Saturation assumption model

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

Skip

Two-store model with saturation. Parameters are

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

Retrograde amnesia

Cho & Kesner (1996). (mice) presentationsR2=0.96

Summary of animal data presentations

Frankland et al. (2001) study presentations

- 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

Forgetting after 3 shocks, using three parameters presentations

Controls receive 1 shock, experimental animals 3 shocks (no new free parameters).

Summary of ‘cortical amnesia’. Using only 4 parameters for all curves (R2 = 0.976).

Application to retrograde amnesia for all curves (

- Data on clinical tests cannot be used for direct modeling
- The reason is that remote time periods in these tests are typically made easier
- Data for the different time periods are therefore not equivalent
- Our model may offer a solution here: the relative retrograde gradient or rr-gradient

Sometimes this problems occurs with animal data as well for all curves (

- Wiig, Cooper, and Bear (1996)
- Used non-counterbalanced stimuli

Wiig, Cooper & Bear (1996). (rats) for all curves (R2=0.28

Wiig, Cooper & Bear (1996). (rats) with rr-gradient: for all curves (R2=0.84

Define the relative retrograde gradient or for all curves (rr-gradient

rr for all curves (-gradient (continued)

The for all curves (rr-gradient does not have parameters for learning strength m1 or cue strength q

Recall probability for all curves (p(t) must transformed to retention r(t)

Albert et al. (1979), naming of famous faces for all curves (

Squire, Haist, and Shimamura (1989), recall of public events for all curves (

Concluding remarks for all curves (

- In this presentation, we have shown models at two levels of abstraction:
- Mathematical, based on point processes
- Computational, based on simplified neural networks

Concluding remarks for all curves (

- These models incorporate data from:
- Neuroanatomy and neurophysiology
- Neurology and neuropsychology
- Experimental psychology

- The aim is to integrate these various sources of data into a single theory that is implemented in a series of coordinated models

Concluding remarks for all curves (

- 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

Download Presentation

Connecting to Server..