bioRxiv Science⌕ Search

bioRxiv · 10.1101/2025.04.12.648539

Switches from tonic to burst firing enable memory consolidation through late-phase synaptic plasticity

Abstract

Neural circuits routinely alternate between input-driven tonic activity and collective burst firing. In the presence of Hebbian plasticity, bursts generate a robust attractor in weight space, creating a built-in drift that can be repurposed into a stabilizing trace of prior learning. We show that this phenomenon can be harnessed for memory consolidation through the introduction of a two-stage synaptic rule. The effective synaptic weight is defined as the product of a primary weight--updated by a Hebbian rule during both tonic and burst periods--and a secondary weight that updates in proportion with a coupling gain to the negative time-derivative of the primary weight. In a MNIST-like task, alternating tonic and burst epochs preserves earlier patterns, improves generalization to unseen inputs, and resists interference and noise, whereas replacing burst by quiescence or additional tonic epochs does not. Parameter sweeps reveal that coupling gain and the initial synaptic weights control whether bursts consolidate ("up-selection") or prune ("down-selection") synapses. Pairing the rule with alternative primary plasticity models yields distinct treatments of overlapping inputs, enabling either integration or separation. Studying switches in firing activity with a two-stage synaptic plasticity provides a plausible route to consolidation in biological and neuromorphic networks. Significance StatementNeural circuits alternate between tonic spiking and burst firing, yet most models of synaptic plasticity are limited to a single firing regime. We introduce a two--stage synaptic rule in which a primary weight encodes activity during both states, while a secondary weight--engaged only during bursts-- stabilizes learning from tonic periods. In conductance-based networks and a pattern recognition task, this rule preserves memories, improves generalization, and resists interference, whereas quiescence or extended tonic activity do not. The model further shows that bursts can consolidate or prune synapses depending on coupling gain and initial conditions. These findings identify a plausible, biologically motivated mechanism for how activity state transitions shape memory consolidation. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=160 HEIGHT=200 SRC="FIGDIR/small/648539v2_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@7a8922org.highwire.dtl.DTLVardef@c13425org.highwire.dtl.DTLVardef@46a505org.highwire.dtl.DTLVardef@1fc0820_HPS_FORMAT_FIGEXP M_FIG C_FIG

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Jacquerie, K., Tyulmankov, D., Sacre, P., Drion, G.. 2025-04-18. Switches from tonic to burst firing enable memory consolidation through late-phase synaptic plasticity. https://doi.org/10.1101/2025.04.12.648539

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

CoReg, tracking-based motion compensation for in vivo calcium imaging

In vivo calcium imaging enables the observation of neural activity with high spatial and temporal resolution. However, recordings are frequently affected by motion artifacts caused by animal movement, physiological processes, tissue drift, or instability of the imaging setup. These artifacts make signal extraction harder and reduce the consistency of downstream analyses. Current registration methods can correct rigid and non-rigid motion, but often estimate motion from individual frames or image patches and make limited use of temporal information. In addition, activity-driven fluorescence changes can make it difficult to distinguish changes in image appearance from actual motion. Here, we treat motion correction in calcium imaging as a point tracking problem and introduce CoReg, an open-source Python- based software tool with a graphical user interface. We benchmark CoReg against established registration methods using fluorescence imaging datasets from multiple species. CoReg performs competitively with or better than existing approaches across several settings. We further show that its performance is robust to the choice of tracked points, making the method straightforward to apply. CoReg includes implementations of both our method and the evaluated established methods. Our results show that tracking-based registration provides a simple and reliable approach for motion correction in vivo calcium imaging.

neuroscience↗

A shared slowing axis and phenotype-specific rhythms link clinical EEG to FDG-PET dementia phenotypes

Early and accurate identification of neurodegenerative disorders has notable implications for treatment and quality of life. Whereas neuroimaging-based biomarkers can accurately identify neurodegenerative subtypes, EEG represents a low-cost alternative that could expand access to dementia care. Here, we investigated whether six data-driven EEG latent factors, derived by nonnegative tensor decomposition of clinical EEGs, differ systematically across FDG-PET-defined neurodegenerative phenotypes. In 455 patients with EEG and FDG-PET acquired within one year of each other, factor expression was compared across 14 diagnostic classes assigned by StateViewer, a validated artificial intelligence framework for FDG-PET interpretation. To separate disease-general severity from phenotype-specific topography, each factor was also regressed on a subject-level slowing index. Finally, we related the factors to regional FDG uptake and to neuropsychological performance (n = 98) and assessed their potential utility for single-subject classification. Four factors reflecting neurophysiological oscillations (posterior alpha, Rolandic beta, frontotemporal delta-theta and posterior theta-slow alpha) varied significantly across phenotypes, with attenuation of faster and augmentation of slower rhythms most pronounced in dementia with Lewy bodies (DLB), dysexecutive Alzheimer disease (AD) and posterior cortical atrophy, whereas factors dominated by eye-movement and muscle artifact showed no robust phenotypic association. Alpha and beta effects were largely attributable to overall slowing, whereas posterior theta-slow alpha became more phenotype-specific after adjustment (eta-squared 0.082 to 0.147), dissociating the Alzheimer disease spectrum from frontotemporal syndromes. The slowing index tracked with an FDG-PET pattern highly associated with DLB across the cohort, irrespective of assigned phenotype (r = 0.51, P < 0.001). Consistent with a model in which dementia phenotypes reflect altered hippocampal-neocortical oscillatory coupling (stochastic latent oscillatory diffusion), frontotemporal delta-theta tracked a limbocortical axis of metabolic pattern expression, rising with neocortical and falling with limbic-predominant involvement, independent of overall slowing and phenotype. Each factor mapped onto a distinct metabolic topography, with frontotemporal delta-theta resembling Alzheimer-type hypometabolism and posterior theta-slow alpha the medial occipital hypometabolism of DLB. In terms of neuropsychological performance, Rolandic beta was associated with executive functions, and frontotemporal delta-theta with memory, visuospatial and global performance. Using these four factors alone, an exploratory logistic regression classifier separated cognitively unimpaired participants from DLB, posterior cortical atrophy, typical AD, and dysexecutive AD (area under the curve > 0.8) but poorly discriminated frontotemporal dementia phenotypes canonically associated with 4-repeat tau. Thus, data-driven decomposition of clinical EEG separates oscillatory activity from artifact into factors that track metabolic phenotype, regional metabolism and cognition, capturing both overall slowing and phenotype-specific patterns. This work informs the further development of scalable, biology-informed EEG-based phenotyping of neurodegenerative disease.

neuroscience↗

An application of fNIRS for the prediction of hearing thresholds in sleeping infants: a proof of principle

This study applied a two-component-response modelling approach to fNIRS recordings obtained from naturally sleeping infants presented with auditory stimuli at different sound intensities. We investigated whether the relationship between fNIRS response measures and stimulus intensity could be modelled to estimate individual auditory hearing thresholds. In the normal-hearing group, 75% of infants had threshold estimates for the sound /ba/ within the expected normal-hearing range, with an estimated mean equivalent normal loudness (ENL) of 10.72 dB HL (SD 8.64 dB HL). A case study further explored the potential application of this approach in an infant with hearing impairment, highlighting the importance of reliable responses at lower stimulus intensities and appropriate regression model selection. These findings provide preliminary evidence that cortical haemodynamic responses can be quantitatively related to sound intensity for estimating auditory sensitivity.

neuroscience↗