ArticleProceedings of the National Academy of Sciences of the United States of America2020
Neuronal spike-rate adaptation supports working memory in language processing.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- A unified model of short- and long-term plasticity: Effects on network connectivity and information capacity.PLoS computational biology · 2026Article
- In Search of Transcriptomic Correlates of Neuronal Firing-Rate Adaptation across Subtypes, Regions and Species: A Patch-seq Analysis.bioRxiv : the preprint server for biology · 2025Article
- Neural heterogeneity as a unifying mechanism for efficient learning in spiking neural networks.Frontiers in computational neuroscience · 2025Article
- Neural networks with optimized single-neuron adaptation uncover biologically plausible regularization.PLoS computational biology · 2024Article
- Neurobiological Causal Models of Language Processing.Neurobiology of language (Cambridge, Mass.) · 2024Article
- A matter of availability: sharper tuning for memorized than for perceived stimulus features.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Neural adaptation and fractional dynamics as a window to underlying neural excitability.PLoS computational biology · 2023Article
- Toward reproducible models of sequence learning: replication and analysis of a modular spiking network with reward-based learning.Frontiers in integrative neuroscience · 2023Article
- Opening Questions in Visual Working Memory.Journal of cognitive neuroscience · 2022Article
- Robust Effects of Working Memory Demand during Naturalistic Language Comprehension in Language-Selective Cortex.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2022Article
- SAM: A Unified Self-Adaptive Multicompartmental Spiking Neuron Model for Learning With Working Memory.Frontiers in neuroscience · 2022Article
- Activation of TGR5 Ameliorates Streptozotocin-Induced Cognitive Impairment by Modulating Apoptosis, Neurogenesis, and Neuronal Firing.Oxidative medicine and cellular longevity · 2022Article
- Spike frequency adaptation supports network computations on temporally dispersed information.eLife · 2021Article
Corrections and comments
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Authors and funding
6 authors.
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Abstract
Language processing involves the ability to store and integrate pieces of information in working memory over short periods of time. According to the dominant view, information is maintained through sustained, elevated neural activity. Other work has argued that short-term synaptic facilitation can serve as a substrate of memory. Here we propose an account where memory is supported by intrinsic plasticity that downregulates neuronal firing rates. Single neuron responses are dependent on experience, and we show through simulations that these adaptive changes in excitability provide memory on timescales ranging from milliseconds to seconds. On this account, spiking activity writes information into coupled dynamic variables that control adaptation and move at slower timescales than the membrane potential. From these variables, information is continuously read back into the active membrane state for processing. This neuronal memory mechanism does not rely on persistent activity, excitatory feedback, or synaptic plasticity for storage. Instead, information is maintained in adaptive conductances that reduce firing rates and can be accessed directly without cued retrieval. Memory span is systematically related to both the time constant of adaptation and baseline levels of neuronal excitability. Interference effects within memory arise when adaptation is long lasting. We demonstrate that this mechanism is sensitive to context and serial order which makes it suitable for temporal integration in sequence processing within the language domain. We also show that it enables the binding of linguistic features over time within dynamic memory registers. This work provides a step toward a computational neurobiology of language.
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