Memory storage fidelity in the hippocampal circuit: the role of subregions and input statistics

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.

Sex-specific dendritic morphology of hippocampal pyramidal neurons in the adolescent and young adult rats

CA1 and CA3 pyramidal neurons are the major sources of hippocampal efferents. The structural features of these neurons are presumed to be involved in various normal/abnormal cognitive and emotional outcomes by influencing the pattern of synaptic inputs and neuronal signal processing. Although many studies have described hippocampal structure differences between males and females, these reports mainly focused on gross anatomical features in adult or aged models, and such distinctions on neuronal morphology and dendritic spine density during adolescence, a period of high vulnerability to neurodevelopmental disorders, have received much less attention. In this work, we analyzed dendritic architecture and density of spines in CA1 and CA3 neurons of male and female rats in early adolescence (postnatal day, PND 40) and compared them with those in late adolescence/young adulthood (PND 60). On PND 40, CA1 neurons of male rats showed more Sholl intersections and spine density in apical and basal dendrites compared to those in females. The Sholl intersections in basal dendrites of CA3 neurons were also more in males, whereas the number of apical dendrite intersections was not significantly different between sexes. In male rats, there was a notable decrease in the number of branch and terminal points in the basal dendrite of CA1 neurons of young adults when compared to their sex-matched adolescent rats. On the other hand, CA1 neurons in young adult females also showed more Sholl intersections in apical and basal dendrites compared to adolescent females. Meanwhile, the total cable length, the number of branches, and terminal points of apical dendrites in CA3 neurons also exhibited a significant reduction in young adult male rats compared to their sex-matched adolescents. In young adult rats, both apical and basal dendrites of CA3 neurons in males showed fewer intersections with Sholl circles, but there were no significant differences in dendritic spine density or count estimation between males and females. On the other hand, young adult female rats had more Sholl intersections and dendritic spine count on the basal dendrites of CA3 neurons compared to adolescent females. Although no significant sex- and age-dependent difference in neuronal density was detected in CA1 and CA3 subareas, CA3 pyramidal neurons of both male and female rats showed reduced soma area compared to adolescent rats. Our findings show that the sex differences in the dendritic structure of CA1 and CA3 neurons vary by age and also by the compartments of dendritic arbors. Such variations in the morphology of hippocampal pyramidal neurons may take part as a basis for normal cognitive and affective differences between the sexes, as well as distinct sensitivity to interfering factors and the prevalence of neuropsychological diseases.

The properties of long-term potentiation at SC-CA1/ TA-CA1 hippocampal synaptic pathways depends upon their input pathway activation patterns

Long-term potentiation (LTP) has been considered as a cellular mechanism of memory. Since the Schaffer collateral (SC) and temporoammonic (TA) inputs to CA1 are distinct synaptic pathways that could mediate different cognitive functions, this study was therefore aimed to separately study and compare the properties of LTP of these two synaptic pathways. In the current study we used slice electrophysiological methods to compare various properties of these two synaptic pathways in response to single, paired pulse stimulation, and to three standard protocols for inducing LTP: the high frequency electrical stimulation (HFS), theta-burst (TBS), and primed burst (PBs) stimulation. We found that the SC-CA1 synapses could produce bigger maximum synaptic responses than TA-CA1 synapses. In addition, we showed that paired-pulse ratios of the SC-CA1 synapses were higher than TA-CA1 synapses at certain inter-pulses intervals. Finally, we showed a higher LTP% was induced by PBs or TBS at the SC-CA1 synapse than the TA-CA1 synapse. Briefly, our findings suggest the differential basal synaptic transmission, paired-pulse evoked synaptic responses, and LTP exhibition of the hippocampal SC-CA1/ TA-CA1 synaptic pathways, which may rely on spontaneous and evoked activity pattern at the local circuit level.

A Model of Semantic Completion in Generative Episodic Memory

Many studies have suggested that episodic memory is a generative process, but most computational models adopt a storage view. In this article, we present a model of the generative aspects of episodic memory. It is based on the central hypothesis that the hippocampus stores and retrieves selected aspects of an episode as a memory trace, which is necessarily incomplete. At recall, the neocortex reasonably fills in the missing parts based on general semantic information in a process we call semantic completion. The model combines two neural network architectures known from machine learning, the vector-quantized variational autoencoder (VQ-VAE) and the pixel convolutional neural network (PixelCNN). As episodes, we use images of digits and fashion items (MNIST) augmented by different backgrounds representing context. The model is able to complete missing parts of a memory trace in a semantically plausible way up to the point where it can generate plausible images from scratch, and it generalizes well to images not trained on. Compression as well as semantic completion contribute to a strong reduction in memory requirements and robustness to noise. Finally, we also model an episodic memory experiment and can reproduce that semantically congruent contexts are always recalled better than incongruent ones, high attention levels improve memory accuracy in both cases, and contexts that are not remembered correctly are more often remembered semantically congruently than completely wrong. This model contributes to a deeper understanding of the interplay between episodic memory and semantic information in the generative process of recalling the past.

The role of hippocampus in memory reactivation: an implication for a therapeutic target against opioid use disorder

Purpose of the review

The abuse of opioids induces many terrible problems in human health and social stability. For opioid-dependent individuals, withdrawal memory can be reactivated by context, which is then associated with extremely unpleasant physical and emotional feelings during opioid withdrawal. The reactivation of withdrawal memory is considered one of the most important reasons for opioid relapse, and it also allows for memory modulation based on the reconsolidation phenomenon. However, studies exploring withdrawal memory modulation during the reconsolidation window are lacking. By summarizing the previous findings about the reactivation of negative emotional memories, we are going to suggest potential neural regions and systems for modulating opioid withdrawal memory.

Recent findings

Here, we first present the role of memory reactivation in its modification, discuss how the hippocampus participates in memory reactivation, and discuss the importance of noradrenergic signaling in the hippocampus for memory reactivation. Then, we review the engagement of other limbic regions receiving noradrenergic signaling in memory reactivation. We suggest that noradrenergic signaling targeting hippocampus neurons might play a potential role in strengthening the disruptive effect of withdrawal memory extinction by facilitating the degree of memory reactivation.

Summary

This review will contribute to a better understanding of the mechanisms underlying reactivation-dependent memory malleability and will provide new therapeutic avenues for treating opioid use disorders.

Adult neurogenesis and the dentate gyrus: Predicting function from form

Hypotheses about the functional properties of the dentate gyrus and adult dentate neurogenesis have been shaped by early observations of the anatomy of this region, mostly in rodents. This has led to the development of a few core propositions that have guided research over the past several years, including the predicted role of this region in pattern separation and the local transformation of inputs from the entorhinal cortex. We now have the opportunity to review these predictions and update these anatomical observations based on recently developed techniques that reveal the complex structure, connectivity, and dynamic properties of distinct cell populations in the dentate gyrus at a higher resolution. Cumulative evidence suggests that the dentate gyrus and adult-born granule cells play a role in some forms of behavioral discriminations, but there are still many unanswered questions about how the dentate gyrus processes information to support the disambiguation of stimuli.

How Dendrites Affect Online Recognition Memory

In order to record the stream of autobiographical information that defines our unique personal history, our brains must form durable memories from single brief exposures to the patterned stimuli that impinge on them continuously throughout life. However, little is known about the computational strategies or neural mechanisms that underlie the brain's ability to perform this type of "online" learning. Based on increasing evidence that dendrites act as both signaling and learning units in the brain, we developed an analytical model that relates online recognition memory capacity to roughly a dozen dendritic, network, pattern, and task-related parameters. We used the model to determine what dendrite size maximizes storage capacity under varying assumptions about pattern density and noise level. We show that over a several-fold range of both of these parameters, and over multiple orders-of-magnitude of memory size, capacity is maximized when dendrites contain a few hundred synapses-roughly the natural number found in memory-related areas of the brain. Thus, in comparison to entire neurons, dendrites increase storage capacity by providing a larger number of better-sized learning units. Our model provides the first normative theory that explains how dendrites increase the brain's capacity for online learning; predicts which combinations of parameter settings we should expect to find in the brain under normal operating conditions; leads to novel interpretations of an array of existing experimental results; and provides a tool for understanding which changes associated with neurological disorders, aging, or stress are most likely to produce memory deficits-knowledge that could eventually help in the design of improved clinical treatments for memory loss.

Temporal pattern separation in hippocampal neurons through multiplexed neural codes

Pattern separation is a central concept in current theories of episodic memory: this computation is thought to support our ability to avoid confusion between similar memories by transforming similar cortical input patterns of neural activity into dissimilar output patterns before their long-term storage in the hippocampus. Because there are many ways one can define patterns of neuronal activity and the similarity between them, pattern separation could in theory be achieved through multiple coding strategies. Using our recently developed assay that evaluates pattern separation in isolated tissue by controlling and recording the input and output spike trains of single hippocampal neurons, we explored neural codes through which pattern separation is performed by systematic testing of different similarity metrics and various time resolutions. We discovered that granule cells, the projection neurons of the dentate gyrus, can exhibit both pattern separation and its opposite computation, pattern convergence, depending on the neural code considered and the statistical structure of the input patterns. Pattern separation is favored when inputs are highly similar, and is achieved through spike time reorganization at short time scales (< 100 ms) as well as through variations in firing rate and burstiness at longer time scales. These multiplexed forms of pattern separation are network phenomena, notably controlled by GABAergic inhibition, that involve many celltypes with input-output transformations that participate in pattern separation to different extents and with complementary neural codes: a rate code for dentate fast-spiking interneurons, a burstiness code for hilar mossy cells and a synchrony code at long time scales for CA3 pyramidal cells. Therefore, the isolated hippocampal circuit itself is capable of performing temporal pattern separation using multiplexed coding strategies that might be essential to optimally disambiguate multimodal mnemonic representations.

Pattern separation of spiketrains in hippocampal neurons

Pattern separation is a process that minimizes overlap between patterns of neuronal activity representing similar experiences. Theoretical work suggests that the dentate gyrus (DG) performs this role for memory processing but a direct demonstration is lacking. One limitation is the difficulty to measure DG inputs and outputs simultaneously. To rigorously assess pattern separation by DG circuitry, we used mouse brain slices to stimulate DG afferents and simultaneously record DG granule cells (GCs) and interneurons. Output spiketrains of GCs are more dissimilar than their input spiketrains, demonstrating for the first time temporal pattern separation at the level of single neurons in the DG. Pattern separation is larger in GCs than in fast-spiking interneurons and hilar mossy cells, and is amplified in CA3 pyramidal cells. Analysis of the neural noise and computational modelling suggest that this form of pattern separation is not explained by simple randomness and arises from specific presynaptic dynamics. Overall, by reframing the concept of pattern separation in dynamic terms and by connecting it to the physiology of different types of neurons, our study offers a new window of understanding in how hippocampal networks might support episodic memory.

Storage fidelity for sequence memory in the hippocampal circuit

Episodic memories have been suggested to be represented by neuronal sequences, which are stored and retrieved from the hippocampal circuit. A special difficulty is that realistic neuronal sequences are strongly correlated with each other since computational memory models generally perform poorly when correlated patterns are stored. Here, we study in a computational model under which conditions the hippocampal circuit can perform this function robustly. During memory encoding, CA3 sequences in our model are driven by intrinsic dynamics, entorhinal inputs, or a combination of both. These CA3 sequences are hetero-associated with the input sequences, so that the network can retrieve entire sequences based on a single cue pattern. We find that overall memory performance depends on two factors: the robustness of sequence retrieval from CA3 and the circuit's ability to perform pattern completion through the feedforward connectivity, including CA3, CA1 and EC. The two factors, in turn, depend on the relative contribution of the external inputs and recurrent drive on CA3 activity. In conclusion, memory performance in our network model critically depends on the network architecture and dynamics in CA3.

The reduction of adult neurogenesis in depression impairs the retrieval of new as well as remote episodic memory

Major depressive disorder (MDD) is associated with an impairment of episodic memory, but the mechanisms underlying this deficit remain unclear. Animal models of MDD find impaired adult neurogenesis (AN) in the dentate gyrus (DG), and AN in DG has been suggested to play a critical role in reducing the interference between overlapping memories through pattern separation. Here, we study the effect of reduced AN in MDD on the accuracy of episodic memory using computational modeling. We focus on how memory is affected when periods with a normal rate of AN (asymptomatic states) alternate with periods with a low rate (depressive episodes), which has never been studied before. Also, unlike previous models of adult neurogenesis, which consider memories as static patterns, we model episodic memory as sequences of neural activity patterns. In our model, AN adds additional random components to the memory patterns, which results in the decorrelation of similar patterns. Consistent with previous studies, higher rates of AN lead to higher memory accuracy in our model, which implies that memories stored in the depressive state are impaired. Intriguingly, our model makes the novel prediction that memories stored in an earlier asymptomatic state are also impaired by a later depressive episode. This retrograde effect exacerbates with increased duration of the depressive episode. Finally, pattern separation at the sensory processing stage does not improve, but rather worsens, the accuracy of episodic memory retrieval, suggesting an explanation for why AN is found in brain areas serving memory rather than sensory function. In conclusion, while cognitive retrieval biases might contribute to episodic memory deficits in MDD, our model suggests a mechanistic explanation that affects all episodic memories, regardless of emotional relevance.

The Interaction between Semantic Representation and Episodic Memory

The experimental evidence on the interrelation between episodic memory and semantic memory is inconclusive. Are they independent systems, different aspects of a single system, or separate but strongly interacting systems? Here, we propose a computational role for the interaction between the semantic and episodic systems that might help resolve this debate. We hypothesize that episodic memories are represented as sequences of activation patterns. These patterns are the output of a semantic representational network that compresses the high-dimensional sensory input. We show quantitatively that the accuracy of episodic memory crucially depends on the quality of the semantic representation. We compare two types of semantic representations: appropriate representations, which means that the representation is used to store input sequences that are of the same type as those that it was trained on, and inappropriate representations, which means that stored inputs differ from the training data. Retrieval accuracy is higher for appropriate representations because the encoded sequences are less divergent than those encoded with inappropriate representations. Consistent with our model prediction, we found that human subjects remember some aspects of episodes significantly more accurately if they had previously been familiarized with the objects occurring in the episode, as compared to episodes involving unfamiliar objects. We thus conclude that the interaction with the semantic system plays an important role for episodic memory.

From grid cells to place cells with realistic field sizes

While grid cells in the medial entorhinal cortex (MEC) of rodents have multiple, regularly arranged firing fields, place cells in the cornu ammonis (CA) regions of the hippocampus mostly have single spatial firing fields. Since there are extensive projections from MEC to the CA regions, many models have suggested that a feedforward network can transform grid cell firing into robust place cell firing. However, these models generate place fields that are consistently too small compared to those recorded in experiments. Here, we argue that it is implausible that grid cell activity alone can be transformed into place cells with robust place fields of realistic size in a feedforward network. We propose two solutions to this problem. Firstly, weakly spatially modulated cells, which are abundant throughout EC, provide input to downstream place cells along with grid cells. This simple model reproduces many place cell characteristics as well as results from lesion studies. Secondly, the recurrent connections between place cells in the CA3 network generate robust and realistic place fields. Both mechanisms could work in parallel in the hippocampal formation and this redundancy might account for the robustness of place cell responses to a range of disruptions of the hippocampal circuitry.

Pattern separation in the hippocampus through the eyes of computational modeling

Pattern separation is a mnemonic process that has been extensively studied over the years. It entails the ability -of primarily hippocampal circuits- to distinguish between highly similar inputs, via generating different neuronal activity (output) patterns. The dentate gyrus (DG) in particular has long been hypothesized to implement pattern separation by detecting and storing similar inputs as distinct representations. The ways in which these distinct representations can be generated have been explored in a number of theoretical and computational modeling studies. Here, we review two categories of pattern separation models: those that address the phenomenon in an abstract mathematical fashion and those that delve into the underlying biological mechanisms by taking into account the anatomy and/or physiology of hippocampal circuits. We summarize the strategies, findings and limitations of these modeling approaches in the light of new experimental findings and propose a unifying framework whereby different network, cellular and sub-cellular mechanisms converge to a common goal: controlling sparsity, the key determinant of pattern separation in the DG.