Hippocampus

Background: Norepinephrine (NE) released from locus coeruleus (LC) projections regulates astrocyte structure and function through adrenergic receptor signaling. We previously showed that increasing noradrenergic tone with the NE reuptake inhibitor desipramine increases astrocyte ramification in the molecular layer of the dentate gyrus. However, whether tonic LC-derived noradrenergic tone is required to maintain astrocyte morphological complexity in vivo, and whether {beta}-adrenergic receptor activation is the effector pathway, remained unclear. Methods: Adult male C57BL/6J mice received DSP-4 (50 mg/kg X 3 days i.p.), a selective LC neurotoxin, with or without concurrent isoproterenol that continued for 21 additional days post cessation of DSP-4 treatment (ISO; 2 mg/kg/day X 24 days), or saline (n = 4 mice per group). Animals were sacrificed 22 days after the final DSP-4 injection. Noradrenergic denervation was confirmed by dopamine {beta}-hydroxylase (DBH) immunostaining. GFAP-immunostained astrocytes in the molecular layer of the dentate gyrus were morphologically characterized using Sholl analysis. Astrocyte density was quantified by SOX9 immunostaining. Results: DSP-4 produced >83% reduction in DBH fiber coverage in the molecular layer. Sholl analysis revealed significant reductions in astrocyte branching complexity in both treatment groups, with the reductions concentrated at distances of 5-15 m from the soma. The maximum number of intersections was also significantly reduced in both groups. Unexpectedly, ISO did not rescue morphological complexity. While DSP-4 alone did not alter astrocyte density, as measured by the number of SOX9-expressing astrocytes, DSP-4+ISO increased SOX9-positive cell density, dissociating the effects of adrenergic signaling on morphology from those on cell numbers. Conclusions: LC-derived noradrenergic tone is required for the maintenance of astrocyte arbour complexity in the dentate gyrus molecular layer. {beta}-adrenergic receptor activation alone is insufficient to restore structural integrity following noradrenergic denervation, yet promotes astrocyte density independently of structural remodeling. These findings have implications for understanding how LC neurodegeneration in Alzheimer's disease and depression may compromise hippocampal astrocyte structure and function.

The hippocampus is characterized by a stereotypical macroscopic structure, where the nuclei are densely and heterogeneously packed among different subregions of the hippocampus. Despite the fact that tissue-specific cellular organization has been implicated in neural function, it has been technically challenging to quantitatively analyze mesoscopic cellular organization in the hippocampus due to its high cellular density. To overcome this technical hurdle, we developed Computational Biophysical Histomorphometry Software (CBHS), an automated image-analysis pipeline, aimed at quantifying nuclear shape and the order of the cellular ensemble in high-density areas. When applied to the subfields of hippocampus, we found that denser regions, most notably the dentate gyrus, were the most positionally, but least orientationally ordered. Nuclear shape exhibited a dependence on the local environment in a packing-dependent manner. This association was cell-type specific, with neurons, but not astrocytes displaying nuclear shape that varied with neighbour proximity, although astrocytes demonstrated greater intrinsic shape variance. The results reveal the presence of reproducible mesoscale cell packing order in hippocampal tissue, and are consistent with a nucleus-driven mechanical coupling between neighbouring cells. The present study provides a quantitative framework with which to understand mesoscopic tissue organization, thus enabling the formulation of testable hypotheses for future investigation.

Sleep is thought to promote memory consolidation through the offline reactivation and reorganization of newly acquired information. However, most studies assess memory shortly after sleep, leaving unresolved whether an initial post-learning sleep episode produces enduring modifications that influence how memories respond to later reactivation. Importantly, the absence of behavioral differences after prolonged retention intervals does not necessarily imply that sleep failed to modify the original memory. Instead, sleep-dependent changes may persist in latent forms that are not readily captured by conventional memory assessments. Here, we investigated whether post-learning sleep produces lasting changes in declarative memories that influence their subsequent response to reactivation. In Study 1, participants learned a declarative memory task and were assigned to either a short nap, a wake condition, or an exploratory long-nap condition that included both NREM and REM sleep. Memory was assessed one week later. Despite substantial forgetting across the retention interval, no significant differences in memory performance were observed between groups. In Study 2, participants learned the same task and subsequently underwent either a short nap or wakefulness. Memory was reactivated six days after learning using an incomplete reminder previously shown to induce memory updating in human declarative memory, and memory was tested one day later. Under these conditions, participants who slept after learning showed better memory performance than wake controls. Moreover, sleep physiological measures predicted the magnitude of the post-reactivation memory benefit. These findings suggest that post-learning sleep induces enduring modifications in declarative memories that are not readily detectable through delayed memory testing alone. Instead, these sleep-dependent changes become evident when memories are challenged through subsequent reactivation. Our results indicate that sleep-dependent consolidation influences the future expression of memory, shaping how memories respond to later reactivation experiences and providing new insight into the relationship between consolidation and reconsolidation.

High-frequency "ripple" oscillations support learning and memory across species, yet it has been argued that putative ripples in awake human recordings are false positives produced when algorithms misread aperiodic (1/f) fluctuations as ripple-band oscillations. We show that this conclusion arises from an artifact of evaluating detection algorithms on surrogate data containing only aperiodic activity. Ripple detectors are adaptive, setting their threshold from the amplitude statistics of the signal, so applying them to surrogate data that contains only aperiodic activity lowers the threshold and inflates false positives (median 62%). Adding real ripple-band events back to the surrogate corrects this threshold shift and eliminates most false detections across multiple standard algorithms. Using multivariate classifiers, we show aperiodic fluctuations can reproduce the power of ripples but not their timing or spectral content. These findings indicate care needs to be taken when using surrogates to evaluate ripple detection algorithms. Thus, under realistic signal properties, human hippocampal ripples remain distinguishable from aperiodic activity.

The hippocampus supports spatial memory by dynamically integrating external sensory inputs with intrinsic neural circuit dynamics during novel experience. In Alzheimers disease (AD), despite impaired hippocampal spatial remapping, spatial learning and memory abilities can remain partially preserved, a phenomenon consistent with cognitive resilience (Gomez-Isla and Frosch 2022, Jia, Xu et al. 2025). However, the hippocampal ensemble coding patterns associated with these preserved learning and memory abilities remain remains unclear. We hypothesize that intrinsic temporal structures of neuronal firing continue to facilitate the encoding of new spatial information. Using the AppNL-G-F rat model, we longitudinally tracked hippocampal CA1 activity during a familiar-novel context alternating task. We found a dissociation between impaired explicit spatial coding and preserved implicit temporal coding in the AD hippocampal network. Explicit spatial coding was impaired, as place cells showed weak discrimination between distinct contexts and failed to improve with learning. In contrast, implicit temporal coding exhibited learning-dependent refinement, with cofiring dynamic becoming increasingly context-specific across long-term experience. Further analysis suggested that the enhancement of implicit cofiring may be associated with the increased consistency of neural ensemble reactivation during sharp wave ripples in awake rest. Taken together, these findings reveal an explicit-implicit dissociation in the AD hippocampal network, suggesting that the learning-dependent refinement of implicit temporal coding may support preserved learning capacity despite impaired spatial remapping.

Post-encoding sleep plays an essential role in episodic memory consolidation. Much of the existing literature on sleep and memory relies on deprivation paradigms or laboratory-controlled sleep. Relatively few studies have examined how naturalistic post-encoding sleep relates to memory retrieval and its supporting neural activity, or whether age-related impairments in this sleep are linked to those in episodic memory. In the present study, we used actigraphy and electroencephalography to examine how post-encoding sleep quality relates to context memory performance and retrieval-related ERPs supporting performance in younger and older adults. Participants encoded object-scene pairs and were tested on matching and mismatching pairs after a 96-hour sleep-filled delay. We found that greater post-encoding sleep continuity predicted better delayed context memory performance for mismatching pairs across age groups. Post-encoding sleep continuity was also associated with larger ERP differences between context hits and misses for context-matching pairs, for ERP effects associated with post-retrieval monitoring operations across age groups. Together, these findings suggest that more continuous, naturalistic post-encoding sleep facilitates episodic memory performance and neural mechanisms supporting episodic memory retrieval across adult age.

The hippocampal CA3 subfield is central to associative learning and memory consolidation. The principal cells of the CA3, pyramidal neurons, execute these functions by generating hypersynchronous bursts that feed forward to the CA1. Extensive recurrent collateral connections within the CA3 neuron population are crucial for the generation of this burst activity. Double C2 domain-containing protein (Doc2) and synaptotagmin 7 (Syt7) are high-affinity calcium sensors implicated in asynchronous synaptic vesicle (SV) release and in the exocytosis of dense-core vesicles (DCVs). Additionally, Doc2 is a sensor for miniature neurotransmission, whereas Syt7 is involved in synaptic facilitation and SV replenishment. Both Doc2 and Syt7 are expressed in the hippocampus, but their potential roles in spontaneous excitatory network activity remain unanswered. Using whole-cell recordings in disinhibited acute hippocampal slices obtained from juvenile Doc2- and Syt7- knockout (KO) mice (P15-21), we report increased CA3 burst generation without changes in spontaneous excitatory postsynaptic current (sEPSC) frequency or amplitude. Moreover, the intrinsic properties of CA3 pyramidal neurons, such as the resting membrane potential, firing rate and input resistance, are unchanged. We propose that this novel burst phenotype in Doc2- and Syt7- KO mice is unrelated to changes in SV release but might be mediated by changes in neuropeptide release from DCVs. Regardless of the underlying mechanisms, this work reveals that both proteins act to regulate network activity. The hippocampal CA3 subfield is central to associative learning and memory consolidation. The principal cells of the CA3, pyramidal neurons, execute these functions by generating hypersynchronous bursts that feed forward to the CA1. Extensive recurrent collateral connections within the CA3 neuron population are crucial for the generation of this burst activity. Double C2 domain-containing protein (Doc2) and synaptotagmin 7 (Syt7) are high-affinity calcium sensors implicated in asynchronous synaptic vesicle (SV) release and in the exocytosis of dense-core vesicles (DCVs). Additionally, Doc2 is a sensor for miniature neurotransmission, whereas Syt7 is involved in synaptic facilitation and SV replenishment. Both Doc2 and Syt7 are expressed in the hippocampus, but their potential roles in spontaneous excitatory network activity remain unanswered. Using whole-cell recordings in disinhibited acute hippocampal slices obtained from juvenile Doc2- and Syt7- knockout (KO) mice (P15-21), we report increased CA3 burst generation without changes in spontaneous excitatory postsynaptic current (sEPSC) frequency or amplitude. Moreover, the intrinsic properties of CA3 pyramidal neurons, such as the resting membrane potential, firing rate and input resistance, are unchanged. We propose that this novel burst phenotype in Doc2- and Syt7- KO mice is unrelated to changes in SV release but might be mediated by changes in neuropeptide release from DCVs. Regardless of the underlying mechanisms, this work reveals that both proteins act to regulate network activity.

Tauopathy is characterized by progressive synaptic failure and neuroinflammation, yet the laminar-specific nature of these disruptions remains poorly understood. We investigated hippocampal functional integrity and glial reactivity in 8-10-month-old PS19 (P301S) mice. Electrophysiological recordings in the CA1 stratum radiatum revealed an unexpected increase in basal synaptic transmission despite profound deficits in both maintenance and early induction of the LTP phase. Conversely, the dentate gyrus exhibited reduced basal transmission and impaired LTP maintenance, alongside significant paired-pulse plasticity changes not observed in CA1. Furthermore, we demonstrate that transregional metaplasticity, as driven by prior activity in the stratum oriens (SO) in a way that inhibits subsequent LTP in wild-type mice, is occluded in PS19 mice. These data suggest that the tauopathic hippocampus exists in a "metaplastic" state, which inhibits future LTP. Immunofluorescence studies revealed that while astrogliosis and microglial activation were pan-hippocampal, specific neuroinflammatory markers exhibited striking laminar specificity. Mean fluorescence intensity for the neuroinflammatory astrocyte marker C3 was significantly upregulated only in the SO, and the lysosomal marker CD68 showed heightened occupancy specifically in the SO and stratum lacunosum-moleculare. Our findings indicate that tau pathology does not affect the hippocampus uniformly. Instead, it induces region-specific shifts in synaptic efficacy and a breakdown of metaplastic control that coincides with anatomically localized neuroinflammatory signaling.

Sleep supports episodic memory consolidation, yet it remains unclear how naturalistic post-encoding sleep quality relates to the neural reinstatement of episodic representations across adulthood. The present study examined whether sleep discontinuity during the retention interval predicted delayed context memory and encoding-retrieval similarity (ERS) of EEG in younger and older adults. Participants completed an object-scene context memory task with immediate and delayed retrieval, while EEG was recorded during encoding and retrieval. Actigraphy was used to measure sleep across the post-encoding retention period, and principal component analysis identified sleep discontinuity and sleep time components. Behavioral results showed that greater post-encoding sleep discontinuity, but not sleep time, was associated with poorer delayed memory accuracy for mismatching object-context pairs across age. ERS analyses further showed that greater sleep discontinuity was associated with reduced ERS for correctly rejected mismatching pairs across frontal and posterior spatiotemporal clusters. Age moderated sleep-ERS associations: greater sleep discontinuity was generally related to lower ERS in younger adults, whereas some spatiotemporal clusters showed positive associations in older adults, potentially reflecting compensatory or effortful retrieval-related processing in poorer sleepers. Together, these findings suggest that sleep continuity during the post-encoding retention interval is important for preserving high-fidelity episodic representations needed for later context discrimination. More broadly, the results demonstrate that naturalistic sleep fragmentation is linked to both behavioral memory outcomes and neural reinstatement across adults.

Deep brain stimulation has demonstrated its therapeutic potential in modulating pathological oscillations associated with Parkinsons disease and epilepsy. However, its efficacy in treating disrupted theta-gamma phase-amplitude coupling seen in memory-related disorders, such as Alzheimers disease, remains poorly understood. While recent studies have targeted the entorhinal-hippocampal circuit, results remain inconsistent. This discrepancy stems from a lack of mechanistic understanding regarding how stimulation protocols affect this circuit. In this work, we present a reduced multicompartment model of the hippocampal CA1 area that reproduces theta-nested gamma oscillations characteristic of healthy neural activity during memory performance. The model comprises pyramidal, basket and OLM cells with simplified morphologies. We also incorporated CA3-to-CA1 axonal projections, providing a foundational framework for studying how stimulation-induced recruitment of afferent pathways modulates CA1 dynamics. By balancing computational efficiency with anatomical accuracy, our model enables systematic investigation of the effects of electrode placement and orientation, as well as stimulation amplitude and frequency on CA1 neural activity. We demonstrate that the excitatory response in CA1 is primarily driven by the recruitment of Schaffer collateral projections. Overall, this work provides a computationally efficient template for exploring diverse stimulation configurations and could be expanded for developing neuromodulatory strategies to restore physiological network dynamics. Author summaryDeep brain stimulation has shown success in treating Parkinsons disease by suppressing abnormal neural activity responsible for movement disorders. However, when applied to memory-related pathologies, such as Alzheimers disease, the therapeutic outcomes remain unpredictable, ranging from cognitive improvement to impairment. This discrepancy highlights a critical gap in our understanding of how stimulation protocols interact with neural dynamics of the targeted circuits. To address this, we developed a computationally efficient model of the hippocampus, which is involved in memory processes, in order to understand how deep brain stimulation might influence its activity. Our model maintains enough biological accuracy to capture essential memory-related neural activity while remaining lightweight enough for rapid execution and systematic exploration of different protocols. This computational efficiency allowed us to conduct systematic investigations of several stimulation configurations to study their effects on hippocampal dynamics. Overall, this model could provide a useful and computationally cost-efficient tool for exploring the mechanisms of deep brain stimulation and help optimize stimulation protocols aimed at alleviating memory disorders.

Existing memories can be updated by the presentation of new information during memory retrieval. Memory updating is impaired with age, and recent reports indicate this process is more susceptible to age-related impairments than the formation of new memories. However, the neuronal mechanisms underlying age-related updating impairments are unknown. Here, we investigated how memory engrams within the dorsal hippocampus encode a memory update in the young and old brain. We found that old mice tended to re-engage a smaller proportion of the original memory engram during the update session and chemogenetically increasing the activation of this engram alleviated age-related updating deficits. A range of therapeutically relevant behavioral and pharmacological approaches promoting re-engagement of the training engram also improved memory updating in old mice. Together, these results identify a novel mechanism by which memory updating is impaired with age and expand our understanding of how the brain organizes related information.

Humans exhibit structured patterns of memory recall, including a tendency to recall more recent information and to recall events in the same order they were experienced. Classic computational models explain these patterns by positing that memories incorporate the ongoing ''temporal context'', formed by smoothly integrating the stimulus history. However, it is unclear whether a single mechanism can account for the full repertoire of human memory strategies, as the optimal approach may be task-dependent. For example, human memory experts widely apply the ''memory palace'' strategy, which is empirically better but not captured by temporal context models. Here we show that neural networks optimized for free recall develop diverse retrieval strategies, with only some of them resembling temporal context models.The best-performing models discovered a stimulus-invariant index code that emphasizes the studied position of each list item, instead of its temporal context. This creates a stable scaffold for forward recall akin to the memory palace technique. This index code was more likely to emerge when networks were i) encouraged to recall all studied items rather than prioritizing a few items, and ii) prevented from relying on recency, resonating with human data. Our findings demonstrate that human-like recall patterns can arise from multiple distinct computational mechanisms, and that sequential retrieval using item index is an optimal strategy that explains expert-level recall performance.

Older adults are among the fastest growing groups of traumatic brain injury (TBI) patients and sustain disproportionately poor chronic outcomes. Despite this, the preclinical aging-TBI literature is limited. Beyond the limited presence of aging TBI studies, most studies published in this domain use moderate-to-severe, open head models of TBI, rather than closed head models of mild TBI (mTBI) and repetitive mTBI (rmTBI), the most clinically prevalent presentation. Whether age modulates the chronic behavioral consequences of rmTBI is unknown. In the current study, young (3-4 months) and aged (18-19 months) male C57BL/6 mice received either five mTBIs on alternating days to model rmTBI or sham procedures and underwent behavioral testing in the chronic phase for spatial memory and anxiety-related behavior. Because cross-age behavioral comparisons are confounded by age-related declines in activity and by large sample sizes necessary to detection interaction effects, we applied a three-tier analytical framework combining within-age comparisons, sham-normalized inter-age comparisons, and factorial two-way ANOVA. Contrary to our hypothesis that aging would worsen rmTBI behavioral deficits, age produced domain-divergent effects. Spatial memory deficits were directionally consistent in both young and aged mice but was attenuated in the aged group. Conversely, anxiety-related behavior emerged selectively in the aged mice showing increased thigmotaxis. Locomotion was driven by age alone, with no injury effect, confirming that the aged anxiety signal was not a locomotor artifact. A post-hoc sensitivity analysis indicated that resolving the Age x Injury interaction effect would require at least 44 animals per group. These findings show that age shapes the affective, but not the cognitive, consequences of chronic rmTBI, and underscoring that statistical strategy is inseparable from design in factorial injury studies.

Hippocampal ripples are transient, high-frequency oscillations linked to memory replay and consolidation. Ripples are well-characterized in rodents to occur during periods of behavioral inactivity (i.e., sleep, rest), viewed as "offline" states where replay can emerge with limited sensory interference. However, human studies have increasingly observed ripples during active tasks, raising the questions of whether ripple genesis and function have been misunderstood or whether there are fundamental species differences. We propose that low arousal states--predominant during offline sleep and transient during wake--may constitute a common mechanism of ripple genesis that reconciles these observations. We recorded directly from human hippocampus during sleep and wake, measuring arousal via sleep staging, pupillometry, and heart rate. Ripple occurrence consistently tracked low arousal: rates were maximal in NREM sleep, small-pupil wake states, and slow heart rate periods across sleep and wake. This modulation was stronger in anterior than posterior hippocampus and was hippocampus specific: ripple-like activity outside the hippocampus showed an opposite modulation, increasing with high arousal. These results resolve apparent species differences and provide a unifying view of offline periods as arousal dips that can emerge across behavioral states, including transiently during active wake, suggesting hippocampal ripples, and memory consolidation, occur continuously intermixed with cognition.

Hippocampal place cell activity represents an animals location in space; yet, how hippocampal neuronal population dynamics change with spatial learning and the mechanisms underlying these activity changes, which drive allocentric navigation to a learned goal, are poorly understood. To address these questions, we performed calcium imaging with a novel wire-free waterproof miniaturized microscope to image the activity of large populations of hippocampal CA1 neurons during spatial learning of a two-dimensional navigational task, the Morris water maze. We followed the same cells during learning and were able to directly examine how each neuron in the ensemble, and the ensemble as a whole, changes its response properties. We found that neuronal spatial selectivity increased and population decoding of spatial location improved as mice learned to navigate to the goal. Viral CRISPR knock out of Grin1 (encoding the essential GluN1 NMDA receptor subunit) in dorsal hippocampal neurons, dramatically reduced long-term potentiation in CA1. This manipulation also prevented the increase in spatial selectivity and improvement of population decoding with spatial learning and resulted in learning deficits in the Morris water maze. Together, our results show that dorsal hippocampus NMDAR-dependent synaptic plasticity is essential for the learning-dependent refinement of CA1 place selectivity and improvement in population decoding of space.

Background: The ability to remember object locations in real life is a fundamental cognitive process that supports goal-directed behavior and is particularly vulnerable to aging and neurodegenerative disease. Despite a growing body of functional magnetic resonance imaging (fMRI) research on object-location memory (OLM), the neural substrates of establishing and retrieving location information are largely unknown. Objective: This systematic review and coordinate-based meta-analysis aimed to identify brain regions consistently activated during OLM in healthy adults, primarily for encoding and - on an exploratory basis - for retrieval, and to characterize age-related differences in OLM-related neural activity. Methods: A systematic search was conducted across three databases (PubMed, PsycInfo, Cochrane Library) up to February 2026. Studies employing task-based fMRI during the encoding and retrieval of object-location associations in healthy adults were eligible. Age-related differences in OLM-related brain activity were examined via narrative synthesis. An activation likelihood estimation (ALE) meta-analysis was performed on studies reporting stereotactic peak coordinates. The review was pre-registered on PROSPERO (CRD420251023695). Results: Twenty-one studies comprising 637 participants were included in the systematic review, with 12 studies being eligible for the encoding ALE meta-analysis. The retrieval ALE meta-analysis was not possible due to the limited number of included studies and reported foci. The systematic review indicated that OLM encoding consistently recruited bilateral fusiform gyri and parahippocampal cortices, with additional engagement of parietal and prefrontal regions across individual studies, whereas OLM retrieval recruited mainly the hippocampus and precuneus. The coordinate-based ALE meta-analysis revealed two significant clusters of activation during OLM encoding: a left-lateralized cluster encompassing the fusiform gyrus, parahippocampal gyrus, and inferior temporal gyrus (peak MNI: -28, -38, -16), and a right-hemisphere cluster spanning the parahippocampal gyrus and fusiform gyrus (peak MNI: 30, -46, -16). Age-related differences, based on a small number of studies with direct age comparison, pointed toward reduced activity in posterior cortical regions coupled with increased activity in prefrontal and midline regions. Additionally, younger adults showed greater hippocampal activation for successful than unsuccessful spatial retrieval, whereas older adults showed the opposite pattern. Conclusion: The systematic review and meta-analysis identify the fusiform gyri and parahippocampal cortices as the most reliably activated regions during OLM encoding, locating OLM formation primarily within the ventral visual-to-medial-temporal processing stream. Retrieval additionally engaged the hippocampus and precuneus, consistent with their established roles in episodic memory. Age-related differences included reduced posterior cortical encoding activity in older adults, a reversal of the hippocampal activation pattern during retrieval, and weaker suppression of midline regions during task performance. The identified encoding pathway may inform targeted network-level interventions such as non-invasive brain stimulation to counteract cognitive decline in aging and neurodegenerative disease.

Background / Introduction: Biologically inspired spiking neural networks can model adaptive behavior, but learning multiple goals is difficult because synaptic updates for different targets can interfere. We tested whether multi-timescale plasticity and context-specific credit assignment could improve continual multi-goal learning in a spiking navigation system inspired by entorhinal-hippocampal circuitry. Methods: We developed a closed-loop spiking model containing grid-like, place-like, target-related, association, and motor-output populations. An agent navigated in a two-dimensional environment with randomized starting locations and learned through reward-modulated spike-timing dependent plasticity (STDP/RL) and a novel evidence-gated plasticity (EGP) framework. EGP accumulates candidate synaptic modifications, evaluates them using reward evidence, and consolidates only changes that improve performance. A target-context variant maintained separate proposal stores and reward evaluation for each target. Results: STDP/RL learned and retained a single-target navigation policy, but multi-target training produced substantial interference, including attraction to incorrect targets after learning. Across 10 connectivity seeds, target-context EGP achieved higher late-stage reward than global EGP, improved weakest-target performance, and increased the fraction of targets achieving positive reward. In a longer continual-learning simulation, reward increased for all targets, TEST-phase performance increasingly exceeded TRAIN-phase performance, and proposal magnitudes grew over learning. Dwell-time confusion analyses showed that target-context EGP reduced wrong-target attraction and improved target selectivity relative to multi-target STDP/RL. Conclusions: These results demonstrate that spiking navigation circuits can learn goal-directed behavior using local plasticity, but robust multi-goal learning benefits from context-specific evidence-based consolidation. Target-context EGP provides a biologically motivated mechanism for reducing interference during continual reinforcement learning in spiking neural networks.

Wakeful rest after learning has been proposed to facilitate memory consolidation compared to engaging in a distraction task, with prior EEG studies linking slow oscillation power during rest to better memory performance. However, replication attempts have yielded mixed results. We investigated whether 10 minutes of wakeful rest would enhance associative memory performance relative to 10 minutes of a hippocampus-dependent auditory short-term memory distraction task, using a within-participant design with continuous EEG recording. We employed both a replication-inspired analytical approach, closely modeled on prior work, and a data-specific approach adapted to our dataset. Contrary to our hypotheses, we found no advantage of rest over distraction on associative memory performance. We did, however, observe an order effect: performance was better for the second learning than the first, and this improvement was more pronounced when rest was performed second compared to first. At the neurophysiological level, neither slow oscillation nor alpha power during the post-learning period correlated with memory performance, regardless of analytical pipeline, although cross-over analyses revealed that the choice of EEG reference influenced the direction of some correlations. At the phenomenological level, self-reported mental activity during rest and distraction, as well as trait daydreaming frequency, were not related to memory outcomes, despite the two conditions inducing distinct subjective cognitive states. Together, these findings do not support a robust benefit of post-learning wakeful rest over a hippocampus-dependent distraction task for associative memory, nor do they replicate prior EEG correlates of consolidation. We discuss methodological factors, including task-learning effects in within-participant designs, the coarseness of averaged spectral power measures, and variability in EEG preprocessing pipelines, that may contribute to inconsistencies across the literature, and we call for greater standardization and transparency in future studies.

Medical imaging pipelines routinely copy single-channel grayscale data into three identical RGB channels before classification, usually without justification. This study tests whether that step affects model predictions. Four coordinated experiments on bit-identical RGB inputs sorted eleven classical machine learning models into three groups: five that were invariant to the copy, two that were nearly invariant, and four whose predictions changed. On the Kaggle Alzheimer MRI Dataset (6,400 images, four classes, five seeds), five models (AdaBoost, HistGradientBoosting, KNN, SVM_Polynomial, and SVM_RBF) produced identical predictions in both conditions for every seed, where KNN is k-nearest neighbors and SVM a support vector machine, with polynomial and radial basis function (RBF) kernels. Two models (GaussianNB and SVM_Linear) differed by at most one of 1,280 samples, a dataset-dependent gap rather than exact invariance. The remaining four (DecisionTree, ExtraTrees, RandomForest, and LogisticRegression) differed substantively. A regularization sweep on Logistic Regression traced its gap to a single cause. As L2 regularization weakened, the color-minus-grayscale macro F1 gap shrank steadily, from +12.07 percentage points at C=0.001 to near zero at C=100 (paired Wilcoxon p=0.0020 under strong regularization), showing the effect scales with feature count rather than image content. A replication on the OASIS dataset, matched in size and class balance, reproduced every grouping, and the Logistic Regression gap reappeared in the same direction at smaller magnitude (+5.30 points macro F1). Two deep networks, ResNet18 and DenseNet121, gave identical predictions across all twenty paired conditions. Channel triplication left most models unchanged while multiplying classical training time 2.3 to 4.0 times without benefit.

The hippocampus and its main input, the entorhinal cortex, are essential for memory formation, yet how they interact to do so remains unresolved. Hippocampal CA1 pyramidal neurons exhibit spatial receptive fields which reorganize unpredictably upon exposure to a novel environment, a process called remapping, considered a model of memory formation. CA1 neurons integrate both hippocampal and direct entorhinal inputs, but their relative contributions to spatial coding are unclear. Here we combined population recordings of CA1 neurons with optogenetic silencing of their direct entorhinal input. While this manipulation did not impact spatial firing in a familiar environment, it impaired remapping in a novel environment, resulting in the emergence of a stable hybrid map combining features of both environments. This shows the direct entorhinal pathway plays a specific role in CA1 novelty detection, enabling the plasticity necessary to drive the reorganisation of spatial firing patterns, preventing interference between memories in different contexts.