Cooperation of Complementary Learning Systems in Memory

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Cooperation of Complementary Learning Systems in
Memory

• James L. McClelland
• Stanford University

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A Playwrights Take on Memory

• What interests me a great deal is the
  mistiness of the past Harold Pinter,
  Conversation prior to the opening of Old Times,
  1971

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What is a Memory?

• The trace left by an experience.
• A representation of the experience brought back
  to mind later.
• In some theories, these things are one and the
  same
• Not so in a connectionist approach to memory!

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In a connectionist approach

• An experience produces a pattern of activation
  over an ensemble of processing units.
• The memory trace is a pattern of adjustments to
  connections among simple processing units.
• The memory as recalled is a pattern of activation
  constructed with the help of the affected
  connections.
• Connections are affected by many experiences, so
  recall is always subject to influence from
  traces of other experiences.
• Remembering is thus always a process of
  reconstruction.

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Outline

• What is a memory?
• The essence of the connectionist/PDP perspective
• Contrasting systems-level approaches to the
  neural basis of memory
• The Complementary Learning Systems framework
• McClelland, McNaughton, and OReilly, 1995
• How the complementary learning systems work
  together to create episodic and semantic
  memory.

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Outline

• What is a memory?
• The essence of the connectionist/PDP perspective
• Contrasting systems-level approaches to the
  neural basis of memory
• The complementary learning systems approach
• McClelland, McNaughton, and OReilly, 1995
• How the complementary learning systems work
  together to create episodic and semantic
  memory.

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Multiple Memory Systems

• Seeks dissociations of different forms of
  learning and memory.
• Explicit vs. implicit memory
• Declarative vs. procedural memory
• Semantic vs. episodic memory
• Familiarity vs. recollection
• Although more than one system can contribute to
  performance in a given task, the contributions
  are simply alternative paths to correct
  performance.
• E.g., in a recognition memory task
• One can respond either by familiarity or
  recollection
• p(old) p(recall) (1-p(recall))
  p(familiar)

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An Alternative Approach

• Complementary and Cooperating Brain Systems
• Memory task performance depends on multiple
  contributing brain systems.
• Contributions of each system to overall task
  performance depend on their neuro-mechanistic
  properties.
• Systems work together so that overall performance
  may be better than the sum of the independent
  contributions of the parts.

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The Complementary Learning Systems
Theory(McClelland, McNaughton OReilly, 1995)

• Neuropsychological motivation
• The basic theory
• Neurophysiology consistent with the account
• Why there should be complementary systems

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Bi-lateral destruction of hippocampus and related
areas produces
- Profound deficit in forming new arbitrary
associations and new episodic memories. -
Preserved acquisition of skills and item-specific
priming. - Loss of recently learned material w/
preservation of prior knowledge, acquired skills,
and remote memory.
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Control groups
Lesioned groups
Time from experience to lesion in days
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The Neuro-Mechanistic Theory Processing and
Learning in Neocortex

• An input and a response to it result in
  activation distributed across many areas in the
  neocortex.
• Small connection weight changes occur as a
  result, producing
• Item-specific effects
• Gradual skill acquisition
• These small changes are not sufficient to support
  rapid acquisition of arbitrary new associations.

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Complementary Learning System in the Hippocampus

• Bi-directional connections produce a reduced
  description of the cortical pattern in the
  hippocampus.
• Large connection weight changes bind bits of
  reduced description together
• Cued recall depends on pattern completion within
  the hippocampal network
• Consolidation occurs through repeated
  reactivation, leading to cumulation of small
  changes in cortex.

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Supporting Neurophysiological Evidence

• The necessary pathways exist.
• Anatomy and physiology of the hippocampus support
  its role in fast learning.
• Reactivation of hippocampal representations
  during sleep.

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Different Learning and Coding Characteristics of
Hippocampus and Neocortex

• Hippocampus learns quickly to allow one-trial
  learning of particulars of individual items and
  events.
• Cortex learns slowly to allow sensitivity to
  overall statistical structure of experience.
• Hippocampus uses sparse conjunctive
  representations to maintain the distinctness of
  specific items and events.
• Cortex uses representations that start out highly
  overlapping and differentiate gradually to allow
• Generalization where warranted
• Differentiation where necessary

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Examples of neurons found in entorhinal cortex
and hippocampal area CA3, consistent with the
idea that the hippocampus but not cortex uses
sparse conjunctive coding
Recording was made while animal traversed an
eight-arm radial maze.
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Why Are There Complementary Learning Systems?

• Discovery of structure requires gradual
  interleaved learning with dense (overlapping)
  patterns of activation. (Many aspects of
  semantic cognition and conceptual development are
  explained by this approach).
• Rapid learning of new information in such systems
  leads to catastrophic interference.
• The hippocampus (working with the cortex) can
  solve this problem.

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Keil, 1979
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The Model of Rumelhart (1990)
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Differentiation in Development
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Catastrophic Interference

• First observed by McClosky and Cohen (1989) when
  they tried to teach first one, then another list
  to a neural network.
• All items on the first list were forgotten before
  even one item from the second list was learned.
• Catastrophic interference also occurs if one
  tries to teach the trained Rumelhart network some
  partially inconsistent new information.

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How can we solve the problem?

• Hippocampus provides a separate system that can
  learn the new information rapidly.
• Once in the hippocampus, the information can be
  reinstated, allowing cortical learning.
• If these hippocampal reinstatements are
  interleaved with ongoing exposure to other items,
  the new information will be integrated into the
  cortical system without interfering with what is
  already known.

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How can we solve the problem?

• Hippocampus provides a separate system that can
  learn the new information rapidly.
• Once in the hippocampus, the information can be
  reinstated, allowing cortical learning.
• If these hippocampal reinstatements are
  interleaved with ongoing exposure to other items,
  the new information will be integrated into the
  cortical system without interfering with what is
  already known.

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How can we solve the problem?

• Hippocampus provides a separate system that can
  learn the new information rapidly.
• Once in the hippocampus, the information can be
  reinstated, allowing cortical learning.
• If these hippocampal reinstatements are
  interleaved with ongoing exposure to other items,
  the new information will be integrated into the
  cortical system without interfering with what is
  already known.

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Overview

• What is a memory?
• The essence of the connectionist/PDP perspective
• Contrasting systems-level approaches to the
  neural basis of memory
• The complementary learning systems approach
• McClelland, McNaughton, and OReilly, 1995
• How the complementary learning systems work
  together to create episodic and semantic
  memory.

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Kwok McClelland Model ofSemantic and Episodic
Memory

• A slow learning cortical system and a
  fast-learning hippocampal system.
• Cortex contains units representing both content
  and context of an experience.
• Semantic memory is gradually built up through
  repeated presentations of the same content in
  different contexts.
• Memory for a specific episode depends on
  hippocampus and the relevant cortical areas,
  including context.
• Episodic memories benefit from relevant semantic
  learning.
• Virtually all memories are partly semantic and
  partly episodic.

Hippocampus
Relation
Cue
Context
Target
Neo-Cortex
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Effect of Prior Association on Paired-Associate
Learning in Control and Amnesic Populations
Base rates
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Kwok McClelland Simulation Cortical
Pre-Training

• Cortical network is pre-trained using CHL with 4
  cue-relation-target triples for each of 20
  different cues.
• Dog chews bone
• Dog chases cat
• Context varies randomly throughout cortical
  pretraining.
• Words and context are patterns of activation over
  units in the appropriate pool.
• Training frequency was varied to create strong
  and weak associates for each cue.

Hippocampus
Relation
Cue
Context
Target
Neo-Cortex
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Kwok McClelland Simulation Experiment

• Study phase is simulated by presenting a set of
  cue-target pairs in a fixed context.
• Cortex fills in relation as mediator.
• Hippocampal network assigns sparse conjunctive
  representation to the combined cue and context.
• Hebbian learning is used to associate this
  representation with the corresponding target
  pattern.
• At test, context and cue are presented, cortex
  and hippocampus collaborate to fill in target.
• Amnesia is simulated by removing some (or all) of
  the hippocampal units.

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Simulation Results From KM Model
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Summary

• Memory traces are in your connections memories
  are constructed using these traces (and those of
  other experiences) to constrain the construction
  process.
• Memory task performance involves cooperation
  among brain regions
• Cortical regions that gradually learn to
  represent content and context
• Medial temporal regions that can learn
  conjunctive associations of cortical patterns
  rapidly
• There are no separate systems dedicated to
  different kinds of memory. These functions
  depend on cooperating brain systems.