Neural processes underpinning episodic memory

Episodic memory is the memory for our personal past experiences. Although numerous functional magnetic resonance imaging (fMRI) studies investigating its neural basis have revealed a consistent and distributed network of associated brain regions, surprisingly little is known about the contributions individual brain areas make to the recollective experience. In this thesis I address this fundamental issue by employing a range of different experimental techniques including neuropsychological testing, virtual reality environments, whole brain and high spatial resolution fMRI, and multivariate pattern analysis. Episodic memory recall is widely agreed to be a reconstructive process, one that is known to be critically reliant on the hippocampus. I therefore hypothesised that the same neural machinery responsible for reconstruction might also support ‘constructive’ cognitive functions such as imagination. To test this proposal, patients with focal damage to the hippocampus bilaterally were asked to imagine new experiences and were found to be impaired relative to matched control participants. Moreover, driving this deficit was a lack of spatial coherence in their imagined experiences, pointing to a role for the hippocampus in binding together the disparate elements of a scene. A subsequent fMRI study involving healthy participants compared the recall of real memories with the construction of imaginary memories. This revealed a fronto-temporo-parietal network in common to both tasks that included the hippocampus, ventromedial prefrontal, retrosplenial and parietal cortices. Based on these results I advanced the notion that this network might support the process of ‘scene construction’, defined as the generation and maintenance of a complex and coherent spatial context. Furthermore, I argued that this scene construction network might underpin other important cognitive functions besides episodic memory and imagination, such as navigation and thinking about the future. It is has been proposed that spatial context may act as the scaffold around which episodic memories are built. Given the hippocampus appears to play a critical role in imagination by supporting the creation of a rich coherent spatial scene, I sought to explore the nature of this hippocampal spatial code in a novel way. By combining high spatial resolution fMRI with multivariate pattern analysis techniques it proved possible to accurately determine where a subject was located in a virtual reality environment based solely on the pattern of activity across hippocampal voxels. For this to have been possible, the hippocampal population code must be large and non-uniform. I then extended these techniques to the domain of episodic memory by showing that individual memories could be accurately decoded from the pattern of activity across hippocampal voxels, thus identifying individual memory traces. I consider these findings together with other recent advances in the episodic memory field, and present a new perspective on the role of the hippocampus in episodic recollection. I discuss how this new (and preliminary) framework compares with current prevailing theories of hippocampal function, and suggest how it might account for some previously contradictory data

Semantic representations in the temporal pole predict false memories

Recent advances in neuroscience have given us unprecedented insight into the neural mechanisms of false memory, showing that artificial memories can be inserted into the memory cells of the hippocampus in a way that is indistinguishable from true memories. However, this alone is not enough to explain how false memories can arise naturally in the course of our daily lives. Cognitive psychology has demonstrated that many instances of false memory, both in the laboratory and the real world, can be attributed to semantic interference. Whereas previous studies have found that a diverse set of regions show some involvement in semantic false memory, none have revealed the nature of the semantic representations underpinning the phenomenon. Here we use fMRI with representational similarity analysis to search for a neural code consistent with semantic false memory. We find clear evidence that false memories emerge from a similarity-based neural code in the temporal pole, a region that has been called the "semantic hub" of the brain. We further show that each individual has a partially unique semantic code within the temporal pole, and this unique code can predict idiosyncratic patterns of memory errors. Finally, we show that the same neural code can also predict variation in true-memory performance, consistent with an adaptive perspective on false memory. Taken together, our findings reveal the underlying structure of neural representations of semantic knowledge, and how this semantic structure can both enhance and distort our memories

A goal direction signal in the human entorhinal/subicular region

Being able to navigate to a safe place, such as a home or nest, is a fundamental behaviour for all complex animals. Determining the direction to such goals is a crucial first step in navigation. Surprisingly, little is known about how, or where in the brain, this 'goal direction signal' is represented. In mammals 'head-direction cells' are thought to support this process, but despite 30 years of research no evidence for a goal direction representation has been reported [1, 2]. Here we used functional magnetic resonance imaging to record neural activity while participants made goal directions judgments based on a previously learned virtual environment. We applied multivoxel pattern analysis [3-5] to this data, and found that the human entorhinal/subicular region contains a neural representation of intended goal direction. Furthermore, the neural pattern expressed for a given goal direction matched the pattern expressed when simply facing that same direction. This suggests the existence of a shared neural representation of both goal and facing direction. We argue that this reflects a mechanism based on head-direction populations that simulate future goal directions during route planning [6]. Our data further revealed that the strength of direction information predicts performance. Finally, we found a dissociation between this geocentric information in the entorhinal/subicular region and egocentric direction information in the precuneus

Decoding neuronal ensembles in the human hippocampus

BACKGROUND: The hippocampus underpins our ability to navigate, to form and recollect memories, and to imagine future experiences. How activity across millions of hippocampal neurons supports these functions is a fundamental question in neuroscience, wherein the size, sparseness, and organization of the hippocampal neural code are debated. RESULTS: Here, by using multivariate pattern classification and high spatial resolution functional MRI, we decoded activity across the population of neurons in the human medial temporal lobe while participants navigated in a virtual reality environment. Remarkably, we could accurately predict the position of an individual within this environment solely from the pattern of activity in his hippocampus even when visual input and task were held constant. Moreover, we observed a dissociation between responses in the hippocampus and parahippocampal gyrus, suggesting that they play differing roles in navigation. CONCLUSIONS: These results show that highly abstracted representations of space are expressed in the human hippocampus. Furthermore, our findings have implications for understanding the hippocampal population code and suggest that, contrary to current consensus, neuronal ensembles representing place memories must be large and have an anisotropic structure

Big-Loop Recurrence within the Hippocampal System Supports Integration of Information across Episodes

Recent evidence challenges the widely held view that the hippocampus is specialized for episodic memory, by demonstrating that it also underpins the integration of information across experiences. Contemporary computational theories propose that these two contrasting functions can be accomplished by big-loop recurrence, whereby the output of the system is recirculated back into the hippocampus. We use ultra-high-resolution fMRI to provide support for this hypothesis, by showing that retrieved information is presented as a new input on the superficial entorhinal cortex—driven by functional connectivity between the deep and superficial entorhinal layers. Further, the magnitude of this laminar connectivity correlated with inferential performance, demonstrating its importance for behavior. Our findings offer a novel perspective on information processing within the hippocampus and support a unifying framework in which the hippocampus captures higher-order structure across experiences, by creating a dynamic memory space from separate episodic codes for individual experiences

Hippocampal place cells construct reward related sequences through unexplored space

Dominant theories of hippocampal function propose that place cell representations are formed during an animal's first encounter with a novel environment and are subsequently replayed during off-line states to support consolidation and future behaviour. Here we report that viewing the delivery of food to an unvisited portion of an environment leads to off-line pre-activation of place cells sequences corresponding to that space. Such 'preplay' was not observed for an unrewarded but otherwise similar portion of the environment. These results suggest that a hippocampal representation of a visible, yet unexplored environment can be formed if the environment is of motivational relevance to the animal. We hypothesise such goal-biased preplay may support preparation for future experiences in novel environments

Spontaneous and deliberate future thinking: A dual process account

© 2019 Springer Nature.This is the final published version of an article published in Psychological Research, licensed under a Creative Commons Attri-bution 4.0 International License. Available online at: https://doi.org/10.1007/s00426-019-01262-7.In this article, we address an apparent paradox in the literature on mental time travel and mind-wandering: How is it possible that future thinking is both constructive, yet often experienced as occurring spontaneously? We identify and describe two ‘routes’ whereby episodic future thoughts are brought to consciousness, with each of the ‘routes’ being associated with separable cognitive processes and functions. Voluntary future thinking relies on controlled, deliberate and slow cognitive processing. The other, termed involuntary or spontaneous future thinking, relies on automatic processes that allows ‘fully-fledged’ episodic future thoughts to freely come to mind, often triggered by internal or external cues. To unravel the paradox, we propose that the majority of spontaneous future thoughts are ‘pre-made’ (i.e., each spontaneous future thought is a re-iteration of a previously constructed future event), and therefore based on simple, well-understood, memory processes. We also propose that the pre-made hypothesis explains why spontaneous future thoughts occur rapidly, are similar to involuntary memories, and predominantly about upcoming tasks and goals. We also raise the possibility that spontaneous future thinking is the default mode of imagining the future. This dual process approach complements and extends standard theoretical approaches that emphasise constructive simulation, and outlines novel opportunities for researchers examining voluntary and spontaneous forms of future thinking.Peer reviewe

True and intentionally fabricated memories

The aim of the experiment reported here was to investigate the processes underlying the construction of truthful and deliberately fabricated memories. Properties of memories created to be intentionally false - fabricated memories - were compared to properties of memories believed to be true - true memories. Participants recalled and then wrote or spoke true memories and fabricated memories of everyday events. It was found that true memories were reliably more vivid than fabricated memories and were nearly always recalled from a first person perspective. In contrast, fabricated differed from true memories in that they were judged to be reliably older, were more frequently recalled from a third person perspective, and linguistic analysis revealed that they required more cognitive effort to generate. No notable differences were found across modality of reporting. Finally, it was found that, intentionally fabricated memories were created by recalling and then ‘editing’ true memories. Overall, these findings show that true and fabricated memories systematically differ, despite the fact that both are based on true memories

Improved protein structure prediction using potentials from deep learning

Protein structure prediction can be used to determine the three-dimensional shape of a protein from its amino acid sequence1. This problem is of fundamental importance as the structure of a protein largely determines its function2; however, protein structures can be difficult to determine experimentally. Considerable progress has recently been made by leveraging genetic information. It is possible to infer which amino acid residues are in contact by analysing covariation in homologous sequences, which aids in the prediction of protein structures3. Here we show that we can train a neural network to make accurate predictions of the distances between pairs of residues, which convey more information about the structure than contact predictions. Using this information, we construct a potential of mean force4 that can accurately describe the shape of a protein. We find that the resulting potential can be optimized by a simple gradient descent algorithm to generate structures without complex sampling procedures. The resulting system, named AlphaFold, achieves high accuracy, even for sequences with fewer homologous sequences. In the recent Critical Assessment of Protein Structure Prediction5 (CASP13)—a blind assessment of the state of the field—AlphaFold created high-accuracy structures (with template modelling (TM) scores6 of 0.7 or higher) for 24 out of 43 free modelling domains, whereas the next best method, which used sampling and contact information, achieved such accuracy for only 14 out of 43 domains. AlphaFold represents a considerable advance in protein-structure prediction. We expect this increased accuracy to enable insights into the function and malfunction of proteins, especially in cases for which no structures for homologous proteins have been experimentally determined7

Mastering the game of Go without human knowledge

A long-standing goal of artificial intelligence is an algorithm that learns, tabula rasa, superhuman proficiency in challenging domains. Recently, AlphaGo became the first program to defeat a world champion in the game of Go. The tree search in AlphaGo evaluated positions and selected moves using deep neural networks. These neural networks were trained by supervised learning from human expert moves, and by reinforcement learning from self-play. Here we introduce an algorithm based solely on reinforcement learning, without human data, guidance or domain knowledge beyond game rules. AlphaGo becomes its own teacher: a neural network is trained to predict AlphaGo’s own move selections and also the winner of AlphaGo’s games. This neural network improves the strength of the tree search, resulting in higher quality move selection and stronger self-play in the next iteration. Starting tabula rasa, our new program AlphaGo Zero achieved superhuman performance, winning 100–0 against the previously published, champion-defeating AlphaGo