Distinct mechanisms and functions of episodic memory

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'.

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

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.

Recognition Receiver Operating Characteristic Curves: The Complex Influence of Input Statistics, Memory, and Decision-making

Receiver operating characteristic (ROC) analysis is the standard tool for studying recognition memory. In particular, the curvilinearity and the y-offset of recognition ROC curves have been interpreted as indicative of either memory strength (single-process models) or different memory processes (dual-process model). The distinction between familiarity and recollection has been widely studied in cognitive neuroscience in a variety of conditions, including lesions of different brain regions. We develop a computational model that explicitly shows how performance in recognition memory is affected by a complex and, as yet, underappreciated interplay of various factors, such as stimulus statistics, memory processing, and decision-making. We demonstrate that (1) the factors in the model affect recognition ROC curves in unexpected ways, (2) fitting R and F parameters according to the dual-process model is not particularly useful for understanding the underlying processes, and (3) the variability of recognition ROC curves and the controversies they have caused might be due to the uncontrolled variability in the contributing factors. Although our model is abstract, its functional components can be mapped onto brain regions, which are involved in corresponding functions. This enables us to reproduce and interpret in a coherent framework the diverse effects on recognition memory that have been reported in patients with frontal and hippocampal lesions. To conclude, our work highlights the importance of the rich interplay of a variety of factors in driving recognition memory performance, which has to be taken into account when interpreting recognition ROC curves.