The Ontogeny of Hippocampus-Dependent Memories

The formation of memories that contain information about the specific time and place of acquisition, which are commonly referred to as "autobiographical" or "episodic" memories, critically relies on the hippocampus and on a series of interconnected structures located in the medial temporal lobe of the mammalian brain. The observation that adults retain very few of these memories from the first years of their life has fueled a long-standing debate on whether infants can make the types of memories that in adults are processed by the hippocampus-dependent memory system, and whether the hippocampus is involved in learning and memory processes early in life. Recent evidence shows that, even at a time when its circuitry is not yet mature, the infant hippocampus is able to produce long-lasting memories. However, the ability to acquire and store such memories relies on molecular pathways and network-based activity dynamics different from the adult system, which mature with age. The mechanisms underlying the formation of hippocampus-dependent memories during infancy, and the role that experience exerts in promoting the maturation of the hippocampus-dependent memory system, remain to be understood. In this review, we discuss recent advances in our understanding of the ontogeny and the biological correlates of hippocampus-dependent memories

The Tolman-Eichenbaum Machine: Unifying Space and Relational Memory through Generalization in the Hippocampal Formation

The hippocampal-entorhinal system is important for spatial and relational memory tasks. We formally link these domains, provide a mechanistic understanding of the hippocampal role in generalization, and offer unifying principles underlying many entorhinal and hippocampal cell types. We propose medial entorhinal cells form a basis describing structural knowledge, and hippocampal cells link this basis with sensory representations. Adopting these principles, we introduce the Tolman-Eichenbaum machine (TEM). After learning, TEM entorhinal cells display diverse properties resembling apparently bespoke spatial responses, such as grid, band, border, and object-vector cells. TEM hippocampal cells include place and landmark cells that remap between environments. Crucially, TEM also aligns with empirically recorded representations in complex non-spatial tasks. TEM also generates predictions that hippocampal remapping is not random as previously believed; rather, structural knowledge is preserved across environments. We confirm this structural transfer over remapping in simultaneously recorded place and grid cells

Decoding cognition from spontaneous neural activity

In human neuroscience, studies of cognition are rarely grounded in non-task-evoked, ‘spontaneous’ neural activity. Indeed, studies of spontaneous activity tend to focus predominantly on intrinsic neural patterns (for example, resting-state networks). Taking a ‘representation-rich’ approach bridges the gap between cognition and resting-state communities: this approach relies on decoding task-related representations from spontaneous neural activity, allowing quantification of the representational content and rich dynamics of such activity. For example, if we know the neural representation of an episodic memory, we can decode its subsequent replay during rest. We argue that such an approach advances cognitive research beyond a focus on immediate task demand and provides insight into the functional relevance of the intrinsic neural pattern (for example, the default mode network). This in turn enables a greater integration between human and animal neuroscience, facilitating experimental testing of theoretical accounts of intrinsic activity, and opening new avenues of research in psychiatry

Neural replay in representation, learning and planning

Spontaneous neural activity is rarely the subject of investigation in cognitive neuroscience. This may be due to a dominant metaphor of cognition as the information processing unit, whereas internally generated thoughts are often considered as noise. Adopting a reinforcement learning (RL) framework, I consider cognition in terms of an agent trying to attain its internal goals. This framework motivated me to address in my thesis the role of spontaneous neural activity in human cognition. First, I developed a general method, called temporal delayed linear modelling (TDLM), to enable me to analyse this spontaneous activity. TDLM can be thought of as a domain general sequence detection method. It combines nonlinear classification and linear temporal modelling. This enables testing for statistical regularities in sequences of neural representations of a decoded state space. Although developed for use with human non- invasive neuroimaging data, the method can be extended to analyse rodent electrophysiological recordings. Next, I applied TDLM to study spontaneous neural activity during rest in humans. As in rodents, I found that spontaneously generated neural events tended to occur in structured sequences. These sequences are accelerated in time compared to those that related to actual experience (30 -50 ms state-to-state time lag). These sequences, termed replay, reverse their direction after reward receipt. Notably, this human replay is not a recapitulation of prior experience, but follows sequence implied by a learnt abstract structural knowledge, suggesting a factorized representation of structure and sensory information. Finally, I test the role of neural replay in model-based learning and planning in humans. Following reward receipt, I found significant backward replay of non-local experience with a 160 ms lag. This replay prioritises and facilitates the learning of action values. In a separate sequential planning task, I show these neural sequences go forward in direction, depicting the trajectory subjects about to take. The research presented in this thesis reveals a rich role of spontaneous neural activity in supporting internal computations that underpin planning and inference in human cognition

Spatiotemporal precision of neuroimaging in psychiatry

Aberrant patterns of cognition, perception, and behaviour seen in psychiatric disorders are thought to be driven by a complex interplay of neural processes that evolve at a rapid temporal scale. Understanding these dynamic processes in vivo in humans has been hampered by a trade-off between the spatial and temporal resolution inherent to current neuroimaging technology. A recent trend in psychiatric research has been the use of high temporal resolution imaging, particularly magnetoencephalography (MEG), often in conjunction with sophisticated machine learning decoding techniques. Developments here promise novel insights into the spatiotemporal dynamics of cognitive phenomena, including domains relevant to psychiatric illness such as reward and avoidance learning, memory, and planning. This review considers recent advances afforded by exploiting this increased spatiotemporal precision, with specific reference to applications the seek to drive a mechanistic understanding of psychopathology and the realisation of preclinical translation

Bridging Cognitive Maps: a Hierarchical Active Inference Model of Spatial Alternation Tasks and the Hippocampal-Prefrontal Circuit

Cognitive problem-solving benefits from cognitive maps aiding navigation and planning. Previous studies revealed that cognitive maps for physical space navigation involve hippocampal (HC) allocentric codes, while cognitive maps for abstract task space engage medial prefrontal cortex (mPFC) task-specific codes. Solving challenging cognitive tasks requires integrating these two types of maps. This is exemplified by spatial alternation tasks in multi-corridor settings, where animals like rodents are rewarded upon executing an alternation pattern in maze corridors. Existing studies demonstrated the HC - mPFC circuit's engagement in spatial alternation tasks and that its disruption impairs task performance. Yet, a comprehensive theory explaining how this circuit integrates task-related and spatial information is lacking. We advance a novel hierarchical active inference model clarifying how the HC - mPFC circuit enables the resolution of spatial alternation tasks, by merging physical and task-space cognitive maps. Through a series of simulations, we demonstrate that the model's dual layers acquire effective cognitive maps for navigation within physical (HC map) and task (mPFC map) spaces, using a biologically-inspired approach: a clone-structured cognitive graph. The model solves spatial alternation tasks through reciprocal interactions between the two layers. Importantly, disrupting inter-layer communication impairs difficult decisions, consistent with empirical findings. The same model showcases the ability to switch between multiple alternation rules. However, inhibiting message transmission between the two layers results in perseverative behavior, consistent with empirical findings. In summary, our model provides a mechanistic account of how the HC - mPFC circuit supports spatial alternation tasks and how its disruption impairs task performance

Cognitive and Neural Map Representations in Schizophrenia

An ability to build structured cognitive maps of the world may lie at the heart of understanding cognitive features of schizophrenia. In rodents, cognitive map representations are supported by sequential hippocampal place cell reactivations during rest (offline), known as replay. These events occur in the context of local high frequency ripple oscillations, and whole-brain default mode network (DMN) activation. Genetic mouse models of schizophrenia also report replay and ripple abnormalities. Here, I investigate the behavioural and neural signatures of structured internal representations in people with a diagnosis of schizophrenia (PScz, n = 29) and matched control participants (n = 28) using magnetoencephalography (MEG). Participants were asked to infer correct sequential relationships between task pictures by applying a pre-learned task template to visual experiences containing these pictures. In Chapter 3 I show that, during a post-task rest session, controls exhibited fast spontaneous neural reactivation of task state representations that replayed inferred relationships. Replay was coincident with increased ripple power in hippocampus, which may be related to NMDAR availability (Chapter 4). PScz showed both reduced replay and augmented ripple power, convergent with genetic mouse models. These abnormalities were linked to impairments in behavioural acquisition of task structure, and to its subsequent representation in visually evoked neural responses. In Chapter 5 I explore the temporal coupling between replay onsets and DMN activation. I show an impairment in this association in PScz, which related to subsequent mnemonic maintenance of learned task structure, complementing previous reports of DMN abnormalities in the condition. Finally, in Chapter 6, using a separate verbal fluency task, I show that PScz exhibit evidence of reduced use of (semantic) associative information when sampling concepts from memory. Together, my results provide support for a hypothesis that schizophrenia is associated with abnormalities in neural and behavioural correlates of cognitive map representation

Human Replay Spontaneously Reorganizes Experience

Knowledge abstracted from previous experiences can be transferred to aid new learning. Here, we asked whether such abstract knowledge immediately guides the replay of new experiences. We first trained participants on a rule defining an ordering of objects and then presented a novel set of objects in a scrambled order. Across two studies, we observed that representations of these novel objects were reactivated during a subsequent rest. As in rodents, human "replay" events occurred in sequences accelerated in time, compared to actual experience, and reversed their direction after a reward. Notably, replay did not simply recapitulate visual experience, but followed instead a sequence implied by learned abstract knowledge. Furthermore, each replay contained more than sensory representations of the relevant objects. A sensory code of object representations was preceded 50 ms by a code factorized into sequence position and sequence identity. We argue that this factorized representation facilitates the generalization of a previously learned structure to new objects