Modeling the function of episodic memory in spatial learning

Zeng, Xiangshuai; Diekmann, Nicolas; Wiskott, Laurenz; Cheng, Sen

doi:10.3389/fpsyg.2023.1160648

Cited by 6 publications

(1 citation statement)

References 72 publications

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“…This is more realistic, in particular in dynamic environments [ 50 ] where pattern sequences are usually available only once but might still be relevant, for example to avoid negative experiences after a single exposure, like pain [ 51 ], even if the avoidance behavior is not necessarily an optimal one. The benefit of storing single experiences for learning a behavior has been demonstrated with functional models of episodic memory in reinforcement tasks [ 52 , 53 ]. These models are not neural and simply store the incoming sequences as they are.…”

Section: Discussionmentioning

confidence: 99%

A neural network model for online one-shot storage of pattern sequences

Melchior,

Altamimi,

Bayati

et al. 2024

PLoS ONE

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Based on the CRISP theory (Content Representation, Intrinsic Sequences, and Pattern completion), we present a computational model of the hippocampus that allows for online one-shot storage of pattern sequences without the need for a consolidation process. In our model, CA3 provides a pre-trained sequence that is hetero-associated with the input sequence, rather than storing a sequence in CA3. That is, plasticity on a short timescale only occurs in the incoming and outgoing connections of CA3, not in its recurrent connections. We use a single learning rule named Hebbian descent to train all plastic synapses in the network. A forgetting mechanism in the learning rule allows the network to continuously store new patterns while forgetting those stored earlier. We find that a single cue pattern can reliably trigger the retrieval of sequences, even when cues are noisy or missing information. Furthermore, pattern separation in subregion DG is necessary when sequences contain correlated patterns. Besides artificially generated input sequences, the model works with sequences of handwritten digits and natural images. Notably, our model is capable of improving itself without external input, in a process that can be referred to as ‘replay’ or ‘offline-learning’, which helps in improving the associations and consolidating the learned patterns.

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Section: Discussionmentioning

confidence: 99%

A neural network model for online one-shot storage of pattern sequences

Melchior,

Altamimi,

Bayati

et al. 2024

PLoS ONE

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Distinct mechanisms and functions of episodic memory

Cheng

2024

Phil. Trans. R. Soc. B

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The concept of episodic memory (EM) faces significant challenges by two claims: EM might not be a distinct memory system, and EM might be an epiphenomenon of a more general capacity for mental time travel (MTT). Nevertheless, the observations leading to these arguments do not preclude the existence of a mechanically and functionally distinct EM system. First, modular systems, like cognition, can have distinct subsystems that may not be distinguishable in the system’s final output. EM could be such a subsystem, even though its effects may be difficult to distinguish from those of other subsystems. Second, EM could have a distinct and consistent low-level function, which is used in diverse high-level functions such as MTT. This article introduces the scenario construction framework, proposing that EM crucially rests on memory traces containing the gist of an episodic experience. During retrieval, EM traces trigger the reconstruction of semantic representations, which were active during the remembered episode, and are further enriched with semantic information, to generate a scenario of the past experience. This conceptualization of EM is consistent with studies on the neural basis of EM and resolves the two challenges while retaining the key properties associated with EM. This article is part of the theme issue ‘Elements of episodic memory: lessons from 40 years of research’.

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Eliminating episodic memory?

Andonovski,

Sutton,

McCarroll

2024

Phil. Trans. R. Soc. B

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In Tulving’s initial characterization, episodic memory was one of multiple memory systems. It was postulated, in pursuit of explanatory depth, as displaying proprietary operations, representations and substrates such as to explain a range of cognitive, behavioural and experiential phenomena. Yet the subsequent development of this research programme has, paradoxically, introduced surprising doubts about the nature, and indeed existence, of episodic memory. On dominant versions of the ‘common system’ view, on which a single simulation system underlies both remembering and imagining, there are no processes unique to memory to support robust generalizations with inductive potential. Eliminativism about episodic memory seems to follow from the claim that it has no dedicated neurocognitive system of its own. After identifying this under-noticed threat, we push back against modern eliminativists by surveying recent evidence that still indicates specialized mechanisms, computations and representations that are distinctly mnemic in character. We argue that contemporary realists about episodic memory can retain lessons of the common system approach while resisting the further move to eliminativism. This article is part of the theme issue ‘Elements of episodic memory: lessons from 40 years of research’.

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Modeling the function of episodic memory in spatial learning

Cited by 6 publications

References 72 publications

A neural network model for online one-shot storage of pattern sequences

A neural network model for online one-shot storage of pattern sequences

Distinct mechanisms and functions of episodic memory

Eliminating episodic memory?

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