TraceLink: A model of amnesia and consolidation of memory

Murre, Jaap M. J.

doi:10.1002/(sici)1098-1063(1996)6:6<675::aid-hipo10>3.0.co;2-y

Cited by 105 publications

(97 citation statements)

References 47 publications

(25 reference statements)

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“…Ribot curves can span well over 25 years (Albert et al, 1979; Figure 2. View of how consolidation contributes to memory, as incorporated in the TraceLink model (Meeter & Murre, in press;Murre, 1996). TraceLink consists of three systems: neocortical memory repository (trace system), a temporary medial-temporal lobe memory store (link system), and a system regulating the link system's plasticity (modulatory system, indicated by ⌬W for its control of learning rates).…”

Section: Time Course Of Consolidationmentioning

confidence: 99%

Consolidation of Long-Term Memory: Evidence and Alternatives.

Meeter¹,

Murre²

2004

Psychological Bulletin

119

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Memory loss in retrograde amnesia has long been held to be larger for recent periods than for remote periods, a pattern usually referred to as the Ribot gradient. One explanation for this gradient is consolidation of long-term memories. Several computational models of such a process have shown how consolidation can explain characteristics of amnesia, but they have not elucidated how consolidation must be envisaged. Here findings are reviewed that shed light on how consolidation may be implemented in the brain. Moreover, consolidation is contrasted with alternative theories of the Ribot gradient. Consolidation theory, multiple trace theory, and semantization can all handle some findings well but not others. Conclusive evidence for or against consolidation thus remains to be found.

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Section: Time Course Of Consolidationmentioning

confidence: 99%

Consolidation of Long-Term Memory: Evidence and Alternatives.

Meeter¹,

Murre²

2004

Psychological Bulletin

119

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“…One solution is that the mossy ¢bre synapses from the dentate gyrus onto the CA3 pyramidal cells act as`detonator' synapses (being physically large and near to the cell body) that impose the new pattern of activity during encoding, while recurrent collaterals dominate retrieval (McNaughton & Morris 1987; see also Treves & Rolls 1992). An alternative proposal, and a mechanism, for the switching between encoding and retrieval modes concerns the supply of acetylcholine (ACh) to the hippocampus by the medial septum (Hasselmo et al 1996; see also Murre 1996). On this account, increased delivery of ACh suppresses the feedback excitation in CA3, allowing encoding of new information.…”

Section: A Generic Hippocampo-neocortical Model Of Long-term Memorymentioning

confidence: 99%

Memory for events and their spatial context: models and experiments

Burgess

Becker

King

et al. 2001

Phil. Trans. R. Soc. Lond. B

306

214

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The computational role of the hippocampus in memory has been characterized as: (i) an index to disparate neocortical storage sites; (ii) a time-limited store supporting neocortical long-term memory; and (iii) a content-addressable associative memory. These ideas are reviewed and related to several general aspects of episodic memory, including the di¡erences between episodic, recognition and semantic memory, and whether hippocampal lesions di¡erentially a¡ect recent or remote memories. Some outstanding questions remain, such as: what characterizes episodic retrieval as opposed to other forms of read-out from memory; what triggers the storage of an event memory; and what are the neural mechanisms involved? To address these questions a neural-level model of the medial temporal and parietal roles in retrieval of the spatial context of an event is presented. This model combines the idea that retrieval of the rich context of real-life events is a central characteristic of episodic memory, and the idea that medial temporal allocentric representations are used in long-term storage while parietal egocentric representations are used to imagine, manipulate and re-experience the products of retrieval. The model is consistent with the known neural representation of spatial information in the brain, and provides an explanation for the involvement of Papez's circuit in both the representation of heading direction and in the recollection of episodic information. Two experiments relating to the model are brie£y described. A functional neuroimaging study of memory for the spatial context of life-like events in virtual reality provides support for the model's functional localization. A neuropsychological experiment suggests that the hippocampus does store an allocentric representation of spatial locations.

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“…The existing models should be extended in several directions. Human memory involves interactions between hippocampal formation, neocortex and neuromodulatory systems, regulating plasticity of synapses depending on the emotional contents of the situation (63,76,83). Such models have been initially created only at the conceptual level, but computational simulations followed (64,65).…”

Section: Alzheimer Diseasementioning

confidence: 99%

“…Activity-dependent Hebbian models allow for studying memory acquisition, showing such effects as faster forgetting of more recent memories. The memory recency effect, known in the psychological literature on memory as the 'Ribbot gradient', has been noticed a long time ago in retrograde amnesia (63)(64)(65), and has also been observed in Alzheimer's patients. Temporal gradients of memory decline and several other experimental phenomena characterizing memory degradation in AD patients have been recreated in Hebbian models.…”

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confidence: 99%

Computational Models of Dementia and Neurological Problems

Duch

2007

Methods in Molecular Biology™

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The final goal of neuroscience is to fully understand neural processes, their relations to mental processes and to cognitive, affective, and behavioral disorders. Computational modeling, although still in its infancy, already plays a central role in this endeavor. A review of different aspects of computational models that help to explain many features of neuropsychological syndromes and psychiatric disease is presented. Recent advances in computational modeling of epilepsy, cortical reorganization after lesions, Parkinson's and Alzheimer diseases are reviewed. Some trends in computational models of brain functions are identified.Key words: neural networks; cognitive computational neuroscience; associative memory models, cortical reorganization, computational models in psychiatry, Parkinson disease, Alzheimer disease. A bit of historyNeuroinformatics has two large branches. On the one hand it provides tools for storage and analysis of information generated by neuroscience. On the other hand it provides simulations and models that capture some aspects of information processing in the brain. Complexity of the brain dynamics may be too high to understand brain's functions in details in conceptual terms. Computational models based on correct principles may capture progressively larger number of essential features of brain dynamics, eventually leading to models of the whole brain that no individual expert will ever be able to understand in details. This situation is analogous to the cell biology, where the sheer number of biomolecules and their interactions will prevent experts from understanding all genetic and metabolic mechanism of a living cell. From the engineering perspective understanding a complex system implies the ability to build a model that behaves in important aspects in the same way as the system that is being modeled. At this point in history computer simulations are the easiest way to build complex models, but progress in building neuromorphic devices that implement some neural functions in hardware may change this situation in future (1).In 1986 two volumes "Parallel Distributed Processing: Explorations in the Microstructure of Cognition" (2), written mostly by psychologist, were published. The first volume of the PDP book (as it was commonly called) focused on general properties of parallel information processing, drawing analogies with human information processing. The Parallel Distributed Processing (PDP) name did not gain popularity, replaced by "connectionism", the name that stressed the importance of connections that pass information between network of nodes representing neural cell assemblies, increasing or inhibiting their activations. One of the chapters described now famous "backpropagation of errors" algorithm that can be used to train a network of simple artificial neurons with a one-way (feedforward) signal flow desired responses to incoming signals. Such artificial neural networks became very useful for medical diagnostics support, signal and image analysis and monitoring, searc...

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TraceLink: A model of amnesia and consolidation of memory

Cited by 105 publications

References 47 publications

Consolidation of Long-Term Memory: Evidence and Alternatives.

Consolidation of Long-Term Memory: Evidence and Alternatives.

Memory for events and their spatial context: models and experiments

Computational Models of Dementia and Neurological Problems

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