Neural Computation: Learning and memory computation theory - Dayan & Abbott - Trappenberg. 4 Priming...
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Transcript of Neural Computation: Learning and memory computation theory - Dayan & Abbott - Trappenberg. 4 Priming...
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Neural Computation: Learning and memory
Table of contents:
● Various types of memory● How to measure types of memory● Biophysics of LTP and LTD● Computational models
(of unsupervised learning)
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Testing animal memory
(Classical) conditioning Pavlov's dogAplysia gill reflex
Mazes and environments for rodents water mazeplace avoidancefearfood location
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More reading
Reviews of experimental LTP:- Kandel and Schwartz book- Hippocampus book
Theory of Hopfield networks and Backpropagation- Herz, Krogh and Palmer
Neural computation theory- Dayan & Abbott- Trappenberg
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Priming
Think of a zoo...
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Priming
Think of a zoo….
Now think of words starting with ‘T’
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Priming
Think of a hospital….
Now think of words starting with ‘T’
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Bell
Measuring memory: Classical conditioning
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Bell
Measuring memory: Classical conditioning
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Measuring memory: Classical conditioning
Functional perspective
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Measuring memory:Inhibitory avoidance
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Measuring memory:Inhibitory avoidance
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Measuring memory: Instrumental conditioning
Reward dependent on action.Which action?
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Human memory systems
Psychologists (e.g. Tulvin 1972) have split up memory in:
Working memory (likely activity based)
Non-declarative memory Motor skills, sensory, priming, emotional, procedural...
Declarative memory * Episodic memory
- recollection memory/familiarity* Semantic memory: General facts about the world
Are there specific brain regions for each?
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Measuring memory: perceptual learning
Humans [Dinse ‘03]
Fin
ger
sensi
tivit
y
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Episodic memory
● Episodic memory: what, where, when?
● Can link things that are not naturally linked
● Hippocampus (or Medial Temporal Lobe) based.
● Has been modeled as Hopfield network
● Patient H.M.
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Medial temporal lobe
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Semantic memory
● Statistical information about the world.
● Stored in neocortex
● Localized cortical lesions can lead to limited dysfunction(e.g. speech, faces,…)
● H.M. showed -normal priming, -skill, grammar and motor learning-liked doing cross-words(!)
● What is Hippocampus (or MTL) responsible for:explicit (vs implicit) memory?episodic (vs semantic) memory?relational memory?relational processing [Eichenbaum]?
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Semantic memory
● HM did have remote memories. How can that be?● During systems consolidation, memory is transferred/copied
from hippocampus to cortex. During sleep?● Is long-term memory only cortical, or is there still a
hippocampal component?● It is possible to store information in cortex without HPC,
but typically more slowly.
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Episodic memoryRecollection vs. Familiarity
Recollection
Example: remember where, when..
Low capacity
Hippocampus dependent
Asymmetric ROC (binary)
Long lasting
Familiarity memory
Example: faces, pictures
High capacity
Spared with HC-lesions
Symmetric ROC (confidence)
Short lasting
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Episodic memoryRecollection vs. Familiarity
Familiarity memory appearslocated in Peri-rhinal cortex
[Fortin & Eichenbaum]
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Familiarity memory
Combined model[Greve &MvR 09]
● High capacity (~N2) [Bogazc],cf Hopfield (~N)
● Use scenario 1:If something is not familiar,don’t even bother remembering.
● Use scenario 2:Search for novelty (exploitation)
● Bloom-filter in software (cache system)
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Memory systems
Declarative memory * Episodic memory
- recollection- familiarity- hippocampus (patient HM)
* Semantic memory: General facts
Non-declarative memory Motor skills, sensory processing, ...
All done withSynapticplasticity ?
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Measuring Episodic and Semantic memory: Mazes
If start point is not varied, can be learned with procedural learning (without HPC)
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Measuring memory: Mazes
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Measuring memory: Object-place tasks
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Models of memory
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Correlation-based learning
● [James 1898] Objects once experienced together tend to be-come associated in the imagination, so that when any one of them is thought of, the others are likely to be thought of also, in the same order of sequence or coexistence as before.
● [Hebb 1949] Let us assume that the persistence or repetition of a reverberatory activity (or ‘trace’) tends to induce lasting cellu-lar changes that add to its stability … When an axon of cell A is near enough to excite A cell B and repeatedly or persistently takes part in fring it, some growth Process or metabolic change takes place in one or both cells such that A’s efcacy, as one of the cells fring B is increased.
● [Schatz] What fres together, wires together.
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Phenomenology of synaptic plasticity
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Diagram: Kit Longden
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Long term synaptic plasticity
What is (activity dependent, long term) synaptic plasticity?
Long term, semi-permanent changes in the synapticefficacy, induced by neural activity.
In contrast to:- development- short term changes - excitability changes
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Biophysics of LTP
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LTP stages
Induction:- Requires pre- and post synaptic activity.- Mechanism: NMDA and Ca influx
Expression / maintenance phases:- Early LTP- Late LTP
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AP5 blocks learning
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Ca hypothesis
LTP LTD
Pairing high pre- and post synaptic activity => LTPPairing with low activity => Long term depression
[O'Connor & Wang '05][Bliss & Lomo '73]
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Spike Timing Dependent Plasticity: Experimental data
[Bi & Poo 1998]
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LTP stages
Induction:- Requires pre- and postsynaptic activity.- Mechanism: NMDA and Ca influx
Expression / maintenance phases:- Early LTP (1 hr):
- partly pre-synaptic changes- AMPAR phosphorylation- AMPAR insertion
-Late LTP -? (requires protein synthesis)
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Changes in AMPA receptor phosphorilation
[Whitlock,.. and Bear '06]
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Early phase LTP
Rapid and localchange
Stim.:1 s @ 100Hz
CaMKII
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Associativity
Too weak
LTPinducing
Weak stim.no LTP
Weak stim.paired with strong
Associative LTP
- Can be explained with voltage dependence of NMDA
- Associative learning such as Classical conditioning (Pavlov)
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Early phase LTP
But goneafter few hours
Rapid and localchange
Stim.:1 s @ 100Hz
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Late LTP requires protein synthesis
[Fonseca et al 06]
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Late phase LTP
PRPs
)
Dopamine
Stim:3x 1s @100Hz
Start proteinsynthesis
LTP lastsShip PRPs to tagged synapses
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LTP stages
Induction:- Requires pre- and post synaptic activity.- Mechanism: NMDA and Ca influx
Expression and Maintenance phases:- Early LTP (1 hr):
- partly pre-synaptic changes- AMPAR phosphorylation- AMPAR insertion
-Late phase LTP -requires protein synthesis
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Longevity: In vivo physiology
● Strong extracellular stimulation, leads to long lasting strengthening of synapse [Bliss and Lomo '73]
[Abraham '00]
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What determines if LTP lasts?
[Abraham '02, Li & Rowan '00](Dopamine mediated)Does a novel environment 'reset' hippocampal learning?
[Abraham '00]
Stimulus protocol Environment
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What determines if LTP lasts?
[Seidenbecher '95]
Reward and punishment
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Hypotheses for long term stability
[Yao & Sacktor '08]
GluR2 traffickingSlots for AMPA receptors
[Turrigiano '02]
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Late LTP maintenance as an active process
ZIP disrupts one month old memory [Pastalkova et al '06]
[movie demo]
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Hypotheses for maintaince /long term stability
[Yao & Sacktor '08]
GluR2 traffickingSlots for AMPA receptors
[Turrigiano '02]
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Stable memory despite changes
[Ziv et al 2013]
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Synaptic plasticity = memory?
●Detectabilitychanges in behaviour and synaptic efcacy should be correlatedYes
●Mimicrychange synaptic efcacies → new ‘apparent’ memoryNot quite yet...
●Anterograde alteration prevent synaptic plasticity → anterograde amnesiaYes (e.g. NMDA block)
●Retrograde alteration alter synaptic efcacies → retrograde amnesiaYes, but...
[Martin, Greenwood, Morris]
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Synaptic plasticity = memory?
[Martin, Greenwood, Morris]
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Synaptic plasticity=memory?
[Whitlock,.. and Bear '06]
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False memories
[Tonegawa review 2010]
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Spine plasticity
Yu Zuo Curr Opin Neurobiol (2010)
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[Hayashi-Takagi et al., 2015]
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Learningmodels
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Why modelling plasticity
Why modeling plasticity: 2 cross-fertilizing approaches
1) Artificial neural networks, engineering approach
- make a network do something
- now somewhat superseded by more formal
machine learning
2) Insight in biology
- extrapolate single neuron plasticity to network level
- how can organisms adapt?
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Models of plasticity and memory
Supervised learning- tell network exactly what desired output is- train network by changing the weights
Reinforcement learning- Only give reward/punishment
Unsupervised learning- Let the network discover things (statistics) about the input, e.g. Create representations that are useful for further processing (V1)
Animals can do all three presumably
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Bell
‘Saliva’-neuron
Food
NB: just a cartoon!
Modeling classical conditioning
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Modeling classical conditioning
Rescorla-Wagner (delta-rule)
Reward prediction model:
Learn until r=y.
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Modeling classical conditioning
Rescorla-Wagner (delta-rule)
Reward prediction model:
For instance describes blocking:
Lacks temporal effects[Dayan and Abbott book]
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Supervised: Perceptron
Categorize inputs into two classes
Perceptron learning rule [Rosenblatt 1952]- If it can be learned, the rule converges- Not all classification problems can be learned
y= ∑wixi
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Linear separability
SeparablePerceptron can classify
Non-separablePerceptron can't classify Need multiple layers
Extrachair
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Multi-layer perceptron
Network to approximate any function with arbitrary number of inputs and outputs
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Back propagationE in ,out∣w
1,w
2, ...
wi=−
∂ E∂w
i
E=∑patternout
actual−out
desired2
E in ,out∣w1,w
2, ...
wi=−
∂ E∂w
i
E=∑patternout
actual−out
desired2
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Back propagation
General approach:
- Come up with cost function, (objective function) Examples: #errors, sparseness, invariances
- Take the derivative wrt synaptic weights.
- You have created a learning rule
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Hopfield network
- Model for CA3- Recurrent network- Auto-associator (i.e. Pattern completion)
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Hopfield network
One shot learning: wij=∑ patternsxi x
j
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Phenomenological models of plasticity (unsupervised)
wi= x
iyVanilla model:
Covariance rule:
Assumptions made:- w can change sign- w is unbounded- dw independent of w- linear- dw independent of other synapses- changes are gradual and small
w i= x i−⟨x i⟩ .y−⟨y ⟩
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Unsupervised learning
wi=⟨ x
iy⟩
wi=⟨ x
i∑ j w j x j ⟩slow , linear
wi=∑ j
⟨ xixj⟩wj
wi=Q
ijwj
∂ w t ∂ t
=Q. w t
DivergesOOPS...
w t =∑i ci wi eit
PCA
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Constraints and competition
Normalization
Make sure that is constant
This leads to competition
- Divisive normalization (weak competition)
- Subtractive normalization (strong competition)
∑iwi
ConstraintsKeep each weight within bounds
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Constraints and competition
The outcome of the learning is strongly determined by the constraints [Miller & Mackay](Alternatives: BCM, Oja's rule)
Practical tip:
Use subtractive normalization
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Formation of V1 receptive fields
● A wide class of learning rules lead to V1 like receptive fields [Britto & Gerstner '16]● Lateral inhibition ensures complimentary RFs [Dayan and Abbott book]● Unless lateral interaction, there will be no map.
Sparse coding [Olshausen &Field]ICA [Bell & Sejnowski]
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Map formation
Rate-based Hebbian learning, subtractive normalizationSimple learning rules, can lead to realistic maps.
[Bednar, 2012]
Data
Model
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Higher visual areas
V4 and IT “match” machine learning [Yamins 2014]
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Unsupervised learning
Development of realistic receptive fields using generative models.
V1 (1997)
IT [Le … Ng, 2012]
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