Biology subjects

Sacre, P.

Publications and source records attributed to Sacre, P..

3 recordsLinked to original sources

bioRxiv2025

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

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

neuroscience↗

bioRxiv2022

Internal states as a source of subject-dependent movement variability and their representation by large-scale networks

A humans ability to adapt and learn relies on reflecting on past performance. Such reflections form latent factors called internal states that induce variability of movement and behavior to improve performance. Internal states are critical for survival, yet their temporal dynamics and neural substrates are less understood. Here, we link internal states with motor performance and neural activity using state-space models and local field potentials captured from depth electrodes in over 100 brain regions. Ten human subjects performed a goal-directed center-out reaching task with perturbations applied to random trials, causing subjects to fail goals and reflect on their performance. Using computational methods, we identified two internal states, indicating that subjects kept track of past errors and perturbations, that predicted variability in reaction times and speed errors. These states granted access to latent information indicative of how subjects strategize learning from trial history, impacting their overall performance. We further found that large-scale brain networks differentially encoded these internal states. The dorsal attention network encoded past errors in frequencies above 100 Hz, suggesting a role in modulating attention based on tracking recent performance in working memory. The default network encoded past perturbations in frequencies below 15 Hz, suggesting a role in achieving robust performance in an uncertain environment. Moreover, these networks more strongly encoded internal states and were more functionally connected in higher performing subjects, whose learning strategy was to respond by countering with behavior that opposed accumulating error. Taken together, our findings suggest large-scale brain networks as a neural basis of strategy. These networks regulate movement variability, through internal states, to improve motor performance. Key pointsO_LIMovement variability is a purposeful process conjured up by the brain to enable adaptation and learning, both of which are necessary for survival. C_LIO_LIThe culmination of recent experiences--collectively referred to as internal states--have been implicated in variability during motor and behavioral tasks. C_LIO_LITo investigate the utility and neural basis of internal states during motor control, we estimated two latent internal states using state-space representation that modeled motor behavior during a goal-directed center-out reaching task in humans with simultaneous whole-brain recordings from intracranial depth electrodes. C_LIO_LIWe show that including these states--based on error and environment uncertainty--improves the predictability of subject-specific variable motor behavior and reveals latent information related to task performance and learning strategies where top performers counter error scaled by trial history while bottom performers maintain error tendencies. C_LIO_LIWe further show that these states are encoded by the large-scale brain networks known as the dorsal attention network and default network in frequencies above 100 Hz and below 15 Hz but found neural differences between subjects where network activity closely modulates with states and exhibits stronger functional connectivity for top performers. C_LIO_LIOur findings suggest the involvement in large-scale brain networks as a neural basis of motor strategy that orchestrates movement variability to improve motor performance. C_LI

neuroscience↗

bioRxiv2022

Switches to rhythmic brain activity lead to a plasticity-induced reset in synaptic weights

1Neural circuits often alternate between tonic and burst firing - two distinct activity regimes that reflect changes in excitability and neuromodulatory state. While tonic firing produces asynchronous spiking driven by diverse external inputs, collective burst firing consists of rapid clusters of spikes followed by a period of silence, happening synchronously within the network. Synaptic plasticity has typically been studied only in either one of these regimes, leaving unclear how alternating states jointly shape long-term weight dynamics. Here, we use a conductance-based network model endowed with calcium-based or spike-timing-based plasticity rules to examine how synaptic weights evolve across state transitions. During tonic firing, synaptic weights are driven by the statistics of external inputs, producing a broad distribution across the network. In contrast, during collective burst firing, weights converge to a narrow region in weight space: a burst-induced attractor. We derive the location of this attractor analytically in terms of plasticity parameters and activity statistics, and confirm its emergence across diverse plasticity rules. The attractor reflects the synchronization of plasticity-driving signals during bursts, which homogenizes synaptic dynamics and forces convergence toward shared fixed points. We further show that neuromodulation and synaptic tagging can shift or split the burst-induced attractor, stabilizing selected synapses while weakening others. This mechanism reconciles flexibility during tonic-driven learning with stability during burst-driven consolidation. These results identify burst-induced attractors as a robust emergent property of networks combining collective bursting with soft-bound plasticity rules. By showing how they can be analytically predicted and experimentally modulated, our work provides a general computational framework linking state transitions, synaptic plasticity, and memory organization. 2 Author SummaryBrains operate in different activity states, reflecting different behaviors or neuromodulatory states. Neurons can fire isolated spikes in a tonic mode that encodes information about external inputs. They can fire rapid bursts of spikes, generating large synchronized oscillations that dominate population activity. Both tonic and burst firing are linked to learning and memory, yet their distinct contributions to shaping synaptic plasticity remain poorly understood. In this study, we use biophysical network models equipped with well-established plasticity rules to investigate how synaptic weights evolve under tonic and burst firing. We show that during tonic activity, synapses diverge toward a wide variety of values, reflecting the diversity of input statistics. In contrast, when the network enters a collective bursting state, synaptic weights collapse into a narrow region of weight space--a "burst-induced attractor." We derive the attractor mathematically and show that its position depends directly on the plasticity parameters, meaning it can be shifted or split through neuromodulatory and tag-dependent processes. Our results suggest that bursts provide a robust and controllable stage for synaptic consolidation. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=170 SRC="FIGDIR/small/500198v3_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@2ab6aeorg.highwire.dtl.DTLVardef@3f8199org.highwire.dtl.DTLVardef@155900borg.highwire.dtl.DTLVardef@63f520_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗