bioRxiv Subject Collection: Neuroscience's Journal

Alzheimer's disease and its co-pathologies: implications for hippocampal degeneration, cognitive decline, and the role of APOE ϵ4
INTRODUCTIONIn neurodegenerative dementias, the co-occurrence and interaction of A{beta}, tau, and other pathological lesions confound their individual contributions to neurodegeneration and their modulation by risk factors.

METHODSWe analyzed 480 post-mortem human brains (ages 50-99) using regression and structural equation models to assess the relationships among A{beta}, tau, LATE-NC, -synuclein, other age-related lesions, and APOE {varepsilon}4, as well as their effects on CA1 neuronal density, brain weight, and cognitive status.

RESULTSA{beta}, tau, LATE-NC, and amygdala-predominant -synuclein pathology were highly interconnected. Tau was the strongest predictor of global neurodegeneration, while LATE-NC primarily, but not exclusively, affected hippocampal neuron loss. Small vessel disease correlated with both LATE-NC and -synuclein, while APOE {varepsilon}4 was mainly associated with extracellular and capillary A{beta}.

DISCUSSIONAlthough Alzheimers pathology plays a central role in brain degeneration, coexisting pathologies can both exacerbate and independently contribute to it. These factors should be considered in patient stratification.

Impairment of the blood brain barrier accelerates a negative ultraslow potential in the locust CNS
Insects provide useful models for investigating evolutionarily conserved mechanisms underlying electrical events associated with brain injury and death. Spreading depolarizations (SD) are transient events that propagate through neuropil whereas the negative ultraslow potential (NUP) is sustained and reflects accumulating damage in the tissue. We used the locust, Locusta migratoria, to investigate ion homeostasis at the blood brain barrier (BBB) during SD and NUP induced by treatment with the Na+/K+-ATPase inhibitor, ouabain. We found that sustained SD caused by the metabolic inhibitor, sodium azide, was associated with a large reduction of K+ efflux through the BBB at ganglia (= grey matter) but not at connectives (= white matter). This was accompanied by a large increase in tissue resistivity but no conductance changes of identified motoneuron dendrites in the neuropil. Males recovered more slowly from ouabain-induced SD, as previously described for anoxic SD. Impairment of barrier functions of the BBB pharmacologically with cyclosporin A or DIDS, or by cutting nerve roots, accelerated the NUP, thus promoting earlier and more frequent SD, but had no effect on the temporal parameters of SD. We conclude that the mechanisms underlying onset and recovery of SD are minimally affected by the damage associated with the NUP. We suggest that future research using tissue-specific genetic approaches in Drosophila to target identified molecular structures of the BBB are likely to be fruitful.

Weakened prefrontal activation dynamics associated with slowed information processing speed in multiple sclerosis
Information processing speed (IPS) is a core cognitive deficit in people with multiple sclerosis (PwMS). Previous efforts have associated IPS performance to frontal regions, but were constrained by limited temporal resolution. In this work, we employed a data-driven method, the time delay embedded-hidden Markov model (TDE-HMM), to identify task-specific states that are spectrally defined with distinct temporal and spatial profiles. We used magnetoencephalographic (MEG) data recorded while healthy controls and PwMS performed a cognitive task designed to capture IPS, the Symbol Digit Modalities Test (SDMT). The TDE-HMM identified five task-relevant states, supporting a tri-factor contribution to IPS: sensory speed (occipital visual detection and processing), cognitive speed (prefrontal executive and frontoparietal attention shift), and motor speed (sensorimotor). We observed reduced prefrontal and increased frontoparietal activation in PwMS, which correlated with offline SDMT performance. This work can drive future research for MS treatments targeting IPS improvements.

Intrinsic and circuit mechanisms of predictive coding in a grid cell network model
Grid cells in the medial entorhinal cortex (MEC) fire when an animal is located at the vertices of a hexagonal grid that extends across the environment. The population activity of grid cells serves as an allocentric representation of the current location of the animal. Recent studies have identified a class of grid cells that represent locations ahead of the animal. How do these predictive representations emerge from the wetware of the MEC? To address this question, we developed a detailed conductance-based model of the MEC network, constrained by existing data on the biophysical properties of stellate cells and the topology of the MEC network. Our model revealed two mechanisms underlying the emergence of a predictive code in the MEC. The first relied on a time scale associated with the HCN conductance. The other depended on the degree of asymmetry in the topology of the MEC network. The former mechanism was sufficient to explain predictive coding in layer II grid cells that represented locations shifted ahead of the current location. The shift was equivalent to ~5% of the diameter of a grid field. The latter mechanism was required to model predictive representations in layer III grid cells that were shifted forward by a distance of ~25% of the diameter of a grid field. A corollary of our model, that the extent of the predictive code changes monotonically along the dorsoventral axis of the MEC following observed changes in the properties of the HCN conductance, is borne out by recent experiments.

Validation and pre-analytical considerations for processing cerebrospinal fluid samples on a high-throughput proximity extension assay platform
Background: Analysis of cerebrospinal fluid (CSF) facilitates the understanding of brain-specific molecular changes that may associate with disease progression. Proximity extension assays (PEA) have been deployed in several CSF studies, however the validation of the assay and impact of freeze-thaw cycles on the protein signal has not been documented. We sought to (1) validate the assay on the PEA platform and (2) evaluate the effect of freeze-thaw cycles on the detectability of analytes on the PEA platform. Results: We have validated the PEA with Next Generation Sequencing (NGS) readout assay and report on the detectability and coefficient of variation observed in CSF samples. We have also evaluated proteomic signals with a minimum of 3 and a maximum of 9 freeze thaw cycles and detected very minimal change in signal with increasing cycle number. Conclusion: Our study is the first to validate PEA using NGS readout platform with CSF samples. We report lower protein detection rates and higher variability in the expansion panels compared to the original 4 panels, with acceptable variation above detectability threshold. In addition, our work demonstrates that the proteomic signal is robust and continues to be stable across multiple freeze thaw cycles. This is highly impactful to the processing and analysis of clinical samples and facilitates the investigation of samples with variable pre-analytical conditions.

Targeted gene transfer into developmentally defined cell populations of the primate brain
The primate brain possesses unique physiological and developmental features whose systematic investigation is hampered by a paucity of transgenic germline models and tools. Here, we present a minimally invasive method to introduce transgenes widely across the primate cerebral cortex using ultrasound-guided fetal intracerebroventricular viral injections (FIVI). This technique enables rapid-onset and long-lasting transgene expression following the delivery of recombinant adeno-associated viruses (rAAVs). By adjusting the gestational timing of injections, viral serotypes, and transcriptional regulatory elements, rAAV FIVI allows for systematic targeting of specific cell populations. We demonstrate the versatility of this method through restricted laminar expression in the cortex, Cre-dependent targeting of neurons, CRISPR-based gene editing, and labeling of peripheral somatosensory and retinal pathways. By mimicking key desirable features of germline transgenic models, this efficient and targeted method for gene transfer into the fetal primate brain opens new avenues for experimental and translational neuroscience across the lifespan.

A test of long-term and dose-dependent effects of fluoxetine exposure on the velocity of the invasive crayfish Procambarus clarkii
Fluoxetine, a selective serotonin reuptake inhibitor (SSRI), is one of the most detected antidepressants in wastewater, managing to enter watersheds where it is taken up by freshwater fauna. Here we asked if the serotonergic system affects the dispersal capabilities of Procambarus clarkii, a prolific worldwide invasive freshwater crayfish species, thanks in part due to its dispersal rates. For this, we exposed adult crayfish to fluoxetine (serotonin facilitator) and para-chlorophenylalanine (PCPA, serotonin inhibitor) at two concentrations, and measured their velocity after 8 and 16 days, on a two-meter-long metal gutter. Overall, our treatment effects revealed to be non-significant, however the lowest dosage of fluoxetine seem to decrease crayfish mean velocity between the 8th and 16th day of exposure, thus shedding some light on the putative importance of the long-term exposure to environment dosage-dependent fluoxetine in modulating the dispersal capabilities of P. clarkii. Though further research is needed, these results can help us better understand the impact of ambient fluoxetine to this invasive species.

Protective effects of central leptin on whole-body energy homeostasis upon acute olanzapine exposure
Second-generation antipsychotic use is associated with severe metabolic side effects such as obesity and type 2 diabetes. Leptin is a hormone that is secreted by adipose tissue, and it acts on the brain to decrease body weight by reducing food intake and stimulating energy expenditure. Leptin also improves glucose and lipid metabolism. We examined the short-term impact of olanzapine, a commonly used second-generation antipsychotic, on the central leptin-mediated regulation of energy balance, lipid metabolism, and hypothalamic kinase activity. Male Sprague Dawley rats were given an acute intracerebroventricular (ICV, 3rd ventricle) injection of either leptin or vehicle, combined with subcutaneous olanzapine or vehicle. As expected, ICV leptin decreased food intake and, importantly, olanzapine did not block this effect. Administration of leptin, olanzapine, or their combination reduced the average respiratory exchange ratio (RER) during the light cycle, which indicates that fat oxidation was increased. In the dark cycle, leptin decreased the average RER regardless of olanzapine administration, and in the presence of leptin, olanzapine did not affect the average RER. Leptin did not alter the olanzapine-induced increase in serum triglyceride concentrations. Olanzapine and central leptin treatment differentially activated hypothalamic kinases. In conclusion, regulation of food intake and fuel preference by central leptin is intact following acute olanzapine administration.

Initial signs of learning: Decoding newly-learned vocabulary from neural patterns in novice sign language learners
Learning a new language requires the brain to map new perceptual cues onto real-world semantic concepts. Studies of semantic processing suggest that these representations are at least partially language-independent: despite differing low-level perceptual features, homologous words can evoke overlapping neural patterns associated with a shared underlying meaning. A few studies have leveraged this relationship to examine the emergence of knowledge in the brains of novice learners, but so far all have focused on learners of spoken languages. Evidence from bimodal (sign and speech) bilinguals suggests that neural representations of signs and spoken words may be language-dependent on the individual item level but share representations of broad semantic features. However this remains to be tested in novice signers, who may rely more on their established spoken language representations. We present two experiments in which hearing English speakers underwent brief training in American Sign Language (ASL). In Study 1, participants were all trained to ceiling on a set of signs, while in Study 2 the task difficulty was increased to produce individual differences in performance. Using representational similarity analysis (RSA) with fMRI, we identify brain regions where neural patterns reflect semantic relationships between nouns in both languages. Moreover, in Study 2 participants, we show that multivariate neural measures of semantic representation of ASL in several frontal, temporal, and occipital regions reflect individual differences in comprehension. These results suggest that in contrast to prior work in bilinguals, novice signers show semantic representation at the item level in partially overlapping regions, and the degree of multivariate correlation can track individual-level shifts in understanding in the earliest stages of sign language learning.

Loss of MeCP2 leads to sleep deficits that are time-of-day dependent and worsen with sleep deprivation
Rett syndrome (RTT) is a severe, progressive neurodevelopmental disorder caused by mutations in the X-linked gene encoding methyl-CpG-binding protein 2 (MECP2). Sleep problems are frequently reported in Rett Syndrome, but the exact nature remains relatively unexplored. Currently there is limited understanding the role of MECP2 in sleep architecture and regulation. In this study, we employed longitudinal electroencephalographic (EEG) and electromyographic (EMG) recordings to investigate sleep architecture during baseline conditions as well as the homeostatic response to sleep deprivation (SD) in Mecp2-/y male mice. At baseline, Mecp2-/y mice have more non-rapid-eye-movement (NREM) sleep and less rapid-eye-movement (REM) sleep than their wildtype littermates during the light period. However, Mecp2-/y mice display altered sleep timing during the dark period, spending more time in both NREM and REM during the first half and less time during the second half. We also observe differences in REM sleep and wake quality based on spectral properties of the EEG. In response to SD, Mecp2-/y mice can accumulate and discharge sleep pressure normally and show a sleep rebound. However, baseline differences in sleep architecture are heightened after SD. Overall, our findings show that RTT mice exhibit distinct sleep patterns compared to wildtype mice, with time-of-day-dependent variations in NREM and REM sleep, as well as altered EEG spectral properties, that become more pronounced following SD. Future research should explore the molecular mechanisms through which MECP2 regulates circadian sleep architecture to develop targeted therapeutics for sleep disturbances in RTT patients.

HighlightsO_LIMecp2-/y mice show time-of-day-dependent alterations in NREM and REM sleep.
C_LIO_LIEEG analysis revealed distinct sleep and wake quality in Mecp2-/y mice.
C_LIO_LISleep deprivation exacerbates baseline sleep architecture differences.
C_LIO_LILongitudinal EEG/EMG recordings captured comprehensive sleep patterns.
C_LI

Transcriptional and neuroprotective effects of hexokinase-2 inhibitors administered afterstroke
The inflammatory response induced by stroke can exacerbate injury to peri-infarct tissue. Microglia and other immune cells that mediate this response require increased glycolytic flux during pro-inflammatory activation. These cells, unlike neurons and most other cell types, utilize hexokinase-2 (HK2) rather than hexokinase-1 for glycolysis, such that HK2 inhibitors may selectively target them to suppress post-ischemic inflammation. Here we compared the effects of the non-selective hexokinase inhibitor 2-deoxyglucose to the HK2-selective inhibitors lonidamine and 3-bromopyruvate on secondary injury after stroke. A spatial transcriptomic assessment was performed in parallel to compare effects of the inhibitors on microglial gene expression and microglia - neuron interactions and to screen for off-target effects. Each of the inhibitors suppressed pro-inflammatory gene upregulation in peri-infarct microglia and attenuated the upregulation of cell stress functional pathways in the neighboring neurons, but had minimal effect on neuronal gene expression in uninjured cortex. The HK2-selective inhibitors were more effective than 2-deoxyglucose in suppressing morphological microglial changes, neuronal oxidative stress, and neurite loss. 3-bromopyruvate administered after stroke produced long-term improvements in functional outcome. Selective HK2 inhibitors may thus provide a clinically applicable means to suppress microglial activation and thereby improve outcomes after stroke without endangering neuronal energy metabolism.

Generating the head direction signal: Two types of head direction cells in the lateral mammillary and dorsal tegmental nuclei.
Head direction (HD) cells discharge as a function of the animals directional heading and are believed to underlie ones sense of direction. They have been identified in several brain areas, although the signal is thought to be generated across the connections between the lateral mammillary (LMN) and dorsal tegmental nuclei (DTN). Computational models have proposed that a ring-attractor network underlies the mechanisms that generate the signal. These models usually contain separate populations of neurons that encode HD and angular head velocity (AHV). Currently, both cell types have been identified in the LMN and DTN. However, HD attractor models also require cells, referred to as rotation cells, which are sensitive to both parameters conjunctively (HD+AHV). Here we sought to identify such cells in the LMN-DTN network. We identified two distinct types of HD cells. The majority of LMN HD cells (~64%) were AHV-independent, responding only to the animals directional heading. However, a second population (~36%) was sensitive to both HD and AHV. Both symmetric and asymmetric AHV cell types were found. Similar results were found in the DTN, but with a higher percentage of conjunctive HD+AHV cells (60%). Notably, many HD+AHV conjunctive cells were also sensitive to the animals linear velocity (LV). In contrast, HD cells in the anterodorsal thalamus were rarely sensitive to AHV or LV. These findings demonstrate that the requisite rotation-type HD cell is present in brain areas responsible for generating the HD signal and supports the view that an attractor style network underlies its generation in mammals.

An enteric neuron-expressed variant ionotropic receptor detects ingested salts to regulate salt stress resistance
The detection of internal chemicals by interoceptive chemosensory pathways is critical for regulating metabolism and physiology. The molecular identities of interoceptors, and the functional consequences of chemosensation by specific interoceptive neurons remain to be fully described. The C. elegans pharyngeal neuronal network is anatomically and functionally homologous to the mammalian enteric nervous system. Here, we show that the I3 pharyngeal enteric neuron responds to cations via an I3-specific variant ionotropic receptor (IR) to regulate salt stress tolerance. The GLR-9 IR, located at the gut lumen-exposed sensory end of I3, is necessary and sufficient for salt sensation, establishing a chemosensory function for IRs beyond insects. Salt detection by I3 protects specifically against high salt stress, as glr-9 mutants show reduced tolerance of hypertonic salt but not sugar solutions, with or without prior acclimation. While cholinergic signaling from I3 promotes tolerance to acute high salt stress, peptidergic signaling from I3 during acclimation is essential for resistance to a subsequent high salt challenge. Transcriptomic analyses show that I3 regulates salt tolerance in part via regulating the expression of osmotic stress response genes in distal tissues. Our results describe the mechanisms by which chemosensation mediated by a defined enteric neuron regulates physiological homeostasis in response to a specific abiotic stress.

Dissociating physiological ripples and epileptiform discharges with vision transformers
Two frequently studied bursts of neural activity in the hippocampus are normal physiological ripples and abnormal interictal epileptiform discharges (IEDs). While they are different waveforms, IEDs are notoriously picked up as false positives when using typical automated ripples detectors which are prone to sharp edge artifacts. This has created challenges for studying ripples and IEDs independently. We leveraged recent advances in computer vision on time-frequency feature representations to enable more comprehensive and objective dissociation of these phenomena. We retrospectively evaluated human intracranial recordings from 46 hippocampal depth electrode sites among 17 patients with focal epilepsy, the majority of whom had a seizure-onset zone/network involving the hippocampus. We implemented a common human ripple detection algorithm and broadband spectrograms of all detected ripple candidates were projected into low-dimensional space. We segmented them using k-means to infer pseudo-labels for probable ripples and probable IEDs. Independently, human expert IED labels were manually annotated for comparison. State-of-the-art vision transformer models were implemented on individual spectrograms to approach ripple vs. IED dissociation as an image classification problem. We detected 31,847 ripple/IED candidates, and a median 3.9% per patient (range: 0-47.2%) were IEDs based on expert label overlap. Low-dimensional projection of spectrograms separated canonical IEDs vs. ripples better than raw or ripple-filtered waveforms. Canonical ripple and IED candidates emerged at opposite poles with a continuous landscape of intermediates in between. A binary vision transformer model trained on expert-labeled IED vs. non-IED candidate spectrograms with 5-fold cross-validation showed a mean area under the curve (AUC) of 0.970 and mean precision-recall curve of 0.694, both significantly above chance. To evaluate generalizability, we implemented a leave-one-patient-out cross-validation approach, in which training on pseudo-labels and testing on expert-labeled data demonstrated near-expert performance (mean AUC 0.966 across patients, range 0.892-0.997). Transformer-derived attention maps revealed that models were tuned to triangle-like edge artifact spatial features in the spectrograms. Model-derived probabilities (i.e. of being an IED) for all candidates demonstrated continuous transitions between ripples vs. IEDs, as opposed to binary clustering. The delineation between ripples and IEDs appears best represented as a gradient (i.e. not binary) due to physiological ripple features overlapping with sharpened and/or high frequency pathophysiological IED features. ViTs nevertheless perform virtually at human expert levels in dissociating these phenomena by leveraging time-frequency spatial features enabled by neural data spectrograms. Such tools applied to spectrotemporal representations may augment comprehensive investigations in cognitive neurophysiology and epileptiform signal biomarker optimization for closed-loop applications.

The Ventral Attention Network Mediates Attentional Reorienting to Cross-Modal Expectancy Violations: Evidence from EEG and fMRI
Our daily interactions with the world are shaped by sensory expectations informed by context and prior experiences, which in turn influence how we allocate our attention. Prominent predictive coding models suggest that sensory expectancy and attention interact but disagree on the precise mechanisms. One possibility is that the Ventral Attention Network (VAN), may play a role by facilitating attentional reorienting when expectancy is violated. To test this, we employed an auditory-visual trial-by-trial cueing paradigm in three experiments integrating EEG and fMRI to investigate the role of VAN in violations of cross-modal expectancy. Behavioral results showed faster responses to expected targets, confirming the efficacy of cue-induced expectations in orienting attention to the expected target modality. EEG analyses revealed differences in early (~100 ms latency) event-related potentials (ERPs) to both auditory and visual stimuli when expectations were violated. Unexpected stimuli elicited significantly larger early-latency negative ERPs, across both modalities. Source localization of these ERPs and subsequent fMRI evidence revealed activation in the right VAN. Functional connectivity analyses further showed greater coupling between VAN regions and sensory cortices, with modality-specific pathways involving superior temporal gyrus (STG) for auditory and fusiform gyrus (FG) for visual targets. These findings demonstrate that expectancy violations recruit the VAN to reorient attention and resolve sensory conflict. By coordinating top-down control and bottom-up sensory input, the VAN supports adaptive responses to unexpected stimuli. This work advances our understanding of predictive processing in multisensory perception and highlights the role of VAN in flexible cognitive control.

Deep learning using structural MRI massively improves prediction accuracy of body mass index
Obesity is a major public health problem globally and there is considerable interest in the neural mechanisms in food overconsumption. Artificial intelligence (AI), particularly machine learning, has shown promise in characterizing links between brain morphometry and obesity. In 1106 adults, compared to other forms of machine learning, deep learning using 3D convolutional neural networks (3D-CNN) dramatically improves prediction of body mass index (BMI). The 3D-CNN model robustly predicted BMI (R2 =.325), outperforming random forest, elastic net, and tabnet models (R2s<.07) in a 'lockbox' sample. Explainable AI analyses revealed the specific brain regions implicated and these regions were moderately associated with delay discounting, fluid cognition, gait speed, dexterity, and alcohol use. Collectively, these findings reveal the value of deep learning for understanding of the neural basis and motivational processes in the neurobiology of obesity.

Task-induced 1/f slope modulation as a paradigm-independent marker of cognitive control in multiple sclerosis
Multiple sclerosis (MS) is a chronic neuro-degenerative and inflammatory disease causing motor, sensory, and cognitive deficits, including impairments in working memory and attention. These cognitive deficits may arise from an imbalance between excitatory and inhibitory neural activity due to synaptic loss. Recent studies suggest that the aperiodic 1/f slope, a neural marker reflecting excitation/inhibition (E/I) balance, could serve as a biomarker for cognitive control. This study examines 1/f slope modulation during cognitive tasks in people with MS and healthy controls to investigate its potential as a paradigm-independent marker of cognitive control. We analyzed the Magnetoencephalography (MEG) data collected from 126 participants: 44 healthy controls (HCs), 61 people with MS not treated with benzodiazepines (pwMS(BZDn)), and 21 pwMS treated with benzodiazepines (pwMS(BZDp)). Participants performed an auditory oddball task and a visual-verbal n-back working memory task. After preprocessing MEG data, we used the FOOOF algorithm to extract the aperiodic 1/f slope from power spectral densities across 42 cortical parcels. Through this analysis, we observed significant increases in the 1/f slope following stimulus onset for all stimulus types, with more pronounced modulation for non-standard stimuli (targets and distractors), especially within the temporal cortex. Group comparisons revealed less slope modulation in pwMS(BZDp) compared to HCs during distractor stimuli, indicating impaired inhibitory control linked to benzodiazepine treatment. Positive correlations of 1/f slope modulation across auditory oddball and n-back tasks were observed in HCs and pwMS(BZDn), highlighting a consistent, paradigm-independent mechanism. Taken together, these findings demonstrate that the aperiodic 1/f slope is a sensitive, paradigm-independent marker of cognitive control and E/I balance. Reduced modulation in response to distractors among pwMS(BZDp) highlights benzodiazepine-related disruptions in inhibitory neural processes underlying cognitive deficits. These findings underscore the value of aperiodic spectral measures to deepen understanding and potentially guide therapeutic interventions targeting cognitive impairments in MS.

Brainstem pathology in human narcolepsy: Neurodegeneration in the locus coeruleus in narcoleptic humans, but not in genetically narcoleptic mice or dogs
Our earlier study led to the conclusion that human narcolepsy with cataplexy was caused by the loss of forebrain hypocretin (orexin) neurons in the hypothalamus. We now report that humans having narcolepsy with cataplexy also have a 46% decrease in the number and an 18% increase in the size of brainstem neuromelanin-pigmented locus coeruleus (LC) neurons and increased microglial activity in LC. However, no such changes are observed in the LC of hypocretin peptide depleted narcoleptic mice, hypocretin neuron depleted orexin-tTA/TetO-DTA (orexin-DTA) narcoleptic mice or in narcoleptic dogs. Sodium oxybate, an effective treatment for narcolepsy, decreased the size of LC norepinephrine neurons and increased the number and size of LC microglial cells in mice. Our results indicate that the autoimmune process believed to cause human narcolepsy affects both forebrain Hcrt neurons and brainstem LC norepinephrine cells. Addressing both forebrain and brainstem pathologies may improve understanding of, and treatments for, human narcolepsy.

Switches from tonic to burst firing enable memory consolidation through late-phase synaptic plasticity
This study explores how alternating periods of tonic and burst firing phases in neural networks contribute to memory consolidation. We develop a biophysical neural network model that leverages these brain state fluctuations to investigate the interplay between neuronal firing shifts and synaptic plasticity. The model introduces a two-stage plasticity rule, combining conventional early-phase plasticity with a novel burst-driven late-phase plasticity. Using a learning protocol where tonic firing is associated with active learning periods and burst firing is associated with rest periods, we demonstrate that burst firing resets early-phase plasticity, enabling new memory formation during subsequent tonic firing learning periods and memory consolidation through late-phase plasticity. We further confirm through a pattern recognition task that replacing burst firing with additional tonic firing or quiescent periods hinders memory consolidation. These findings suggest a mechanistic role for burst firing in memory consolidation and its importance for both computational models and experimental studies of learning.