Cooperation of Complementary Learning Systems in Memory James L. McClelland Stanford University

A Playwright’s 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

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!

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.

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 O’Reilly, 1995 How the complementary learning systems work together to create ‘episodic’ and ‘semantic’ memory.

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 O’Reilly, 1995 How the complementary learning systems work together to create ‘episodic’ and ‘semantic’ memory.

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)

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.

The Complementary Learning Systems Theory (McClelland, McNaughton & O’Reilly, 1995) Neuropsychological motivation The basic theory Neurophysiology consistent with the account Why there should be complementary systems

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.

Control groups Lesioned groups Time from experience to lesion in days

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.

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. hippocampus

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.

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

Recording was made while animal traversed an eight-arm radial maze. 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.

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.

Keil, 1979

The Model of Rumelhart (1990)

Differentiation in Development Initially Still Young Somewhat Older

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.

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.

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.

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.

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 O’Reilly, 1995 How the complementary learning systems work together to create ‘episodic’ and ‘semantic’ memory.

Kwok & McClelland Model of Semantic 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

Effect of Prior Association on Paired-Associate Learning in Control and Amnesic Populations Base rates

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

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. Context Relation Cue Target Neo-Cortex Hippocampus

Simulation Results From KM Model

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.