Sustained upregulation of widespread hippocampal-neocortical coupling following memory encoding

Systems consolidation of new experiences into lasting episodic memories involves interactions between hippocampus and the neocortex. Evidence of this process is seen already during early awake post-encoding rest periods. Functional MRI (fMRI) studies have demonstrated increased hippocampal coupling with task-relevant perceptual regions and reactivation of stimulus-specific encoding patterns following intensive encoding tasks. Here we investigate the spatial and temporal characteristics of these hippocampally anchored post-encoding neocortical modulations. Eighty-nine adults participated in an experiment consisting of interleaved memory task- and resting-state periods. As expected, we observed increased post-encoding functional connectivity between hippocampus and individually localized neocortical regions responsive to stimulus categories encountered during memory encoding. Post-encoding modulations were however not restricted to stimulus-selective cortex, but manifested as a nearly system-wide upregulation in hippocampal coupling with all major functional networks. The spatial configuration of these extensive modulations resembled hippocampal-neocortical interaction patterns estimated from active encoding operations, suggesting hippocampal post-encoding involvement by far exceeds reactivation of perceptual aspects. This reinstatement of encoding patterns during immediate post-encoding rest was not observed in resting-state scans collected 12 hours later, nor in control analyses estimating post-encoding neocortical modulations in functional connectivity using other candidate seed regions. The broad similarity in hippocampal functional coupling between online memory encoding and offline post-encoding rest suggests reactivation in humans may involve a spectrum of cognitive processes engaged during experience of an event.

Targeted Memory Reactivation during Mind-Wandering in Younger and Older Adults

Mind-wandering (MW) is a universal cognitive process that is estimated to comprise ~30% of our everyday thoughts. Despite its prevalence, the functional utility of MW remains a scientific blind spot. The present study sought to investigate whether MW serves a functional role in cognition. Specifically, we investigated whether MW contributes to memory consolidation processes, and if age differences in the ability to reactivate episodic memories during MW may contribute to age-related declines in episodic memory. Younger and older adults encoded paired associates, received targeted reactivation cues during an interval filled with a task which promotes MW, and were tested on their memory for the cued and uncued stimuli from the initial encoding task. Thought probes were presented during the retention (MW) interval to assess participants’ thought contents. Across three experiments, we compared the effect of different cue modalities (i.e., auditory, visual) on cued recall performance, and examined both correct retrieval response times as well as accuracy. Across experiments, there was evidence that stimuli that were cued during the MW task were correctly retrieved more quickly than uncued stimuli and that this effect was more robust for younger adults than older adults. Additionally, the more MW a participant reported during the retention interval, the stronger the cueing effect they produced during retrieval. The results from these experiments are interpreted within a retrieval facilitation framework wherein cues serve to reactivate the earlier traces during MW, and this reactivation benefits retrieval speed for cued items as compared to uncued items.

Relevance of a Novel Circuit-Level Model of Episodic Memories to Alzheimer’s Disease

The medial temporal lobe memory system has long been identified as the brain region showing the first histopathological changes in early Alzheimer’s disease (AD), and the functional decline observed in patients also points to a loss of function in this brain area. Nonetheless, the exact identity of the neurons and networks that undergo deterioration has not been determined so far. A recent study has identified the entorhinal and hippocampal neural circuits responsible for encoding new episodic memories. Using this novel model we describe the elements of the episodic memory network that are especially vulnerable in early AD. We provide a hypothesis of how reduced reelin signaling within such a network can promote AD-related changes. Establishing novel associations and creating a temporal structure for new episodic memories are both affected in AD. Here, we furnish a reasonable explanation for both of these previous observations.

Conscious Machines for Autonomous Agents and Cybersecurity

<div>Although consciousness has been difficult to define, most researchers in artificial intelligence would agree that AI systems to date have not exhibited anything resembling consciousness. But is a conscious machine possible in the near future? I suggest that a new definition of consciousness may provide a basis for developing a conscious machine. The key is pattern recognition of correlated events in time, leading to the identification of a unified self-agent. Such a conscious system can create a simplified virtual environment, revise it to reflect updated sensor inputs, and partition the environment into self, other agents, and relevant objects. It can track recent time sequences of events, predict future events based on models and patterns in memory, and attribute causality to events and agents. It can make rapid decisions based on incomplete data, and can dynamically learn new responses based on appropriate measures of success and failure. The central aspect of consciousness is the generation of a dynamic narrative, a real-time model of a self-agent pursuing goals in a virtual reality. A conscious machine of this type may be implemented using an appropriate neural network linked to episodic memories. Near-term applications may include autonomous vehicles and online agents for cybersecurity.</div><div>Paper presented at virtual IEEE International Conference on Rebooting Computing (ICRC), Nov. 2021. To be published in conference proceedings 2022.</div>

Conscious Machines for Autonomous Agents and Cybersecurity

<div>Although consciousness has been difficult to define, most researchers in artificial intelligence would agree that AI systems to date have not exhibited anything resembling consciousness. But is a conscious machine possible in the near future? I suggest that a new definition of consciousness may provide a basis for developing a conscious machine. The key is pattern recognition of correlated events in time, leading to the identification of a unified self-agent. Such a conscious system can create a simplified virtual environment, revise it to reflect updated sensor inputs, and partition the environment into self, other agents, and relevant objects. It can track recent time sequences of events, predict future events based on models and patterns in memory, and attribute causality to events and agents. It can make rapid decisions based on incomplete data, and can dynamically learn new responses based on appropriate measures of success and failure. The central aspect of consciousness is the generation of a dynamic narrative, a real-time model of a self-agent pursuing goals in a virtual reality. A conscious machine of this type may be implemented using an appropriate neural network linked to episodic memories. Near-term applications may include autonomous vehicles and online agents for cybersecurity.</div><div>Paper presented at virtual IEEE International Conference on Rebooting Computing (ICRC), Nov. 2021. To be published in conference proceedings 2022.</div>

Prediction errors disrupt hippocampal representations and update episodic memories

The brain supports adaptive behavior by generating predictions, learning from errors, and updating memories to incorporate new information. Prediction error, or surprise, triggers learning when reality contradicts expectations. Prior studies have shown that the hippocampus signals prediction errors, but the hypothesized link to memory updating has not been demonstrated. In a human functional MRI study, we elicited mnemonic prediction errors by interrupting familiar narrative videos immediately before the expected endings. We found that prediction errors reversed the relationship between univariate hippocampal activation and memory: greater hippocampal activation predicted memory preservation after expected endings, but memory updating after surprising endings. In contrast to previous studies, we show that univariate activation was insufficient for understanding hippocampal prediction error signals. We explain this surprising finding by tracking both the evolution of hippocampal activation patterns and the connectivity between the hippocampus and neuromodulatory regions. We found that hippocampal activation patterns stabilized as each narrative episode unfolded, suggesting sustained episodic representations. Prediction errors disrupted these sustained representations and the degree of disruption predicted memory updating. The relationship between hippocampal activation and subsequent memory depended on concurrent basal forebrain activation, supporting the idea that cholinergic modulation regulates attention and memory. We conclude that prediction errors create conditions that favor memory updating, prompting the hippocampus to abandon ongoing predictions and make memories malleable.

Conditioning and Associative Learning

Associative learning is the process whereby humans and other animals learn the predictive relationship between cues in their environment. This process underlies simple forms of learning from rewards, such as classical and operant conditioning. In this chapter, we introduce the basics of associative learning and discuss the role that memory processes play in the establishment and maintenance of this learning. We then discuss the role that associative learning plays in human memory, including through paired associate learning, the enhancement of memory by reward, and the formation of episodic memories. Finally, we illustrate how the memory process influences choice in decision-making, where associative learning allows people to learn the values of different options. We conclude with some suggestions about how models of associative learning, memory, and choice can be integrated into a single theoretical framework.

Neuronal activity–driven oligodendrogenesis in selected brain regions is required for episodic memories

The formation of long-term episodic memories requires the activation of molecular mechanisms in several regions of the medial temporal lobe, including the hippocampus and anterior cingulate cortex (ACC). The extent to which these regions engage distinct mechanisms and cell types to support memory formation is not well understood. Recent studies reported that oligodendrogenesis is essential for learning and long-term memory; however, whether these mechanisms are required only in selected brain regions is still unclear. Also still unknown are the temporal kinetics of engagement of learning-induced oligodendrogenesis and whether this oligodendrogenesis occurs in response to neuronal activity. Here we show that in rats and mice, episodic learning rapidly increases the oligodendrogenesis and myelin biogenesis transcripts olig2, myrf, mbp, and plp1, as well as oligodendrogenesis, in the ACC but not in the dorsal hippocampus (dHC). Region-specific knockdown and knockout of Myrf, a master regulator of oligodendrocyte maturation, revealed that oligodendrogenesis is required for memory formation in the ACC but not the dHC. Chemogenetic neuronal silencing in the ACC showed that neuronal activity is critical for learning-induced oligodendrogenesis. Hence, an activity-dependent increase in oligodendrogenesis in selected brain regions, specifically in the ACC but not dHC, is critical for the formation of episodic memories.

Under what circumstances does autobiographical memory enhance self-control?

<p>Self-control is an important skill because it helps us regulate many of our behaviours, such as how much we eat and drink. Limiting our intake of food and drink is sometimes difficult to do, however. One explanation for why self-control can be difficult is because the value for good health is discounted because it’s delayed, whereas the reward of food and drink are immediate. This is known as delay discounting: larger, future rewards (e.g. saving for a future holiday) decrease in value with the increase in delay and thus people sometimes pick a smaller, sooner reward instead (e.g. needless shopping now). Using a delay discounting paradigm, this study examined whether autobiographical memories can enhance self-control. Study 1 was a replication study and found that cuing participants to retrieve positive, episodic memories enhanced self-control. This effect was only evident in one out of two delay discounting measures used, however. Building on these findings, Study 2 and 3 investigated whether the amount of episodic detail in specific autobiographical memories and a positive self-concept contribute to the effect of autobiographical memory enhancing self-control. The amount of episodic detail recalled was not related to self-control and results about a positive self-concept were inconclusive. Unexpectedly Study 3 also yielded a non-significant result for positive, episodic memory enhancing self-control. Participants in Study 3 were, however, significantly more tired than participants in Study 1, raising the possibility that they were less engaged in the task. This pattern of findings suggests that the effect of autobiographical memory on self-control is fragile, and is possibly influenced by factors such as participant fatigue. Potential reasons for the fragile effect and inconclusive results, and a potential way forward are also discussed.</p>