Neuronal spike-rate adaptation supports working memory in language processing

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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Working Memory and Lexical Ambiguity Resolution as Revealed by ERPs: A Difficult Case for Activation Theories

Journal of Cognitive Neuroscience ◽

10.1162/jocn.2003.15.5.643 ◽

2003 ◽

Vol 15 (5) ◽

pp. 643-657 ◽

Cited By ~ 54

Author(s):

Thomas C. Gunter ◽

Susanne Wagner ◽

Angela D. Friederici

Keyword(s):

Working Memory ◽

Language Processing ◽

Memory Span ◽

Underlying Mechanism ◽

Event Related Potential ◽

Difficult Case ◽

Cognitive Mechanisms ◽

Subordinate Meaning ◽

Efficient Processing ◽

N400 Component

This series of three event-related potential experiments explored the issue of whether the underlying mechanism of working memory (WM) supporting language processing is inhibitory or activational in nature. These different cognitive mechanisms have been proposed to explain the more efficient processing of subjects with a high WM span compared to those with a low WM span. Participants with high and low WM span were presented with sentences containing a homonym followed three words later by a nominal disambiguation cue and a final disambiguation using a verb. At the position of the disambiguation cue, inhibitory or activational WM mechanisms predict contrasting results. When activation is the underlying mechanism for efficient processing, the prediction is that high memory span persons activate both meanings of the homonym equally in WM, whereas low memory span persons only have one meaning present. When inhibition is the underlying mechanism, the predictions are the reverse. The ERP data, in particular, the variations of the meaning related N400 component, showed clear evidence for inhibition as the underlying cognitive mechanism in high-span readers. For low-span participants the cueing towards the dominant or the subordinate meaning elicited an equivalently large N400 component suggesting that both meanings are active in WM. In highspan subjects, the dominant disambiguation cue elicited a smaller N400 than the subordinate one, indicating that for these subjects particularly the dominant meaning is active. The experiments showed that inhibitory processes are probably underlying WM used during language comprehension in high-span subjects. Moreover, they demonstrate that these subjects can use their inhibition in a more flexible manner than low-span subjects. The effects that these processing differences have on the efficiency of language parsing are discussed.

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Verbal Working Memory, Long-Term Knowledge, and Statistical Learning

Current Directions in Psychological Science ◽

10.1177/0963721420920383 ◽

2020 ◽

Vol 29 (4) ◽

pp. 340-345

Author(s):

Satoru Saito ◽

Masataka Nakayama ◽

Yuki Tanida

Keyword(s):

Working Memory ◽

Statistical Learning ◽

Language Processing ◽

Memory Performance ◽

Serial Order ◽

Verbal Working Memory ◽

Learning Mechanisms ◽

Learning Techniques ◽

Statistical Learning Techniques

Evidence supporting the idea that serial-order verbal working memory is underpinned by long-term knowledge has accumulated over more than half a century. Recent studies using natural-language statistics, artificial statistical-learning techniques, and the Hebb repetition paradigm have revealed multiple types of long-term knowledge underlying serial-order verbal working memory performance. These include (a) element-to-element association knowledge, which slowly accumulates through extensive exposure to an exemplar; (b) position–element knowledge, which is acquired through several encounters with an exemplar; and (c) whole-sequence knowledge, which is captured by the Hebb repetition paradigm and acquired rapidly with a few repetitions. Arguably, the first two are a basis for fluent and efficient language usage, and the third is a basis for vocabulary learning. Thus, statistical-learning mechanisms (and possibly episodic-learning mechanisms) may form the foundation of language acquisition and language processing, which characterize linguistic long-term knowledge for verbal working memory.

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Neuronal firing rates account for distractor effects on mnemonic accuracy in a visuo-spatial working memory task

Biological Cybernetics ◽

10.1007/s00422-006-0139-8 ◽

2007 ◽

Vol 96 (4) ◽

pp. 407-419 ◽

Cited By ~ 9

Author(s):

Julian Macoveanu ◽

Torkel Klingberg ◽

Jesper Tegnér

Keyword(s):

Working Memory ◽

Spatial Working Memory ◽

Memory Task ◽

Neuronal Firing ◽

Firing Rates

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Acetylcholine-gated current translates wake neuronal firing rate information into a spike timing-based code in Non-REM sleep, stabilizing neural network dynamics during memory consolidation

PLoS Computational Biology ◽

10.1371/journal.pcbi.1009424 ◽

2021 ◽

Vol 17 (9) ◽

pp. e1009424

Author(s):

Quinton M. Skilling ◽

Bolaji Eniwaye ◽

Brittany C. Clawson ◽

James Shaver ◽

Nicolette Ognjanovski ◽

...

Keyword(s):

Firing Rate ◽

Memory Consolidation ◽

Neuronal Excitability ◽

Network Models ◽

Nrem Sleep ◽

Spike Timing ◽

Neuronal Firing ◽

Information Encoding ◽

Firing Rates

Sleep is critical for memory consolidation, although the exact mechanisms mediating this process are unknown. Combining reduced network models and analysis of in vivo recordings, we tested the hypothesis that neuromodulatory changes in acetylcholine (ACh) levels during non-rapid eye movement (NREM) sleep mediate stabilization of network-wide firing patterns, with temporal order of neurons’ firing dependent on their mean firing rate during wake. In both reduced models and in vivo recordings from mouse hippocampus, we find that the relative order of firing among neurons during NREM sleep reflects their relative firing rates during prior wake. Our modeling results show that this remapping of wake-associated, firing frequency-based representations is based on NREM-associated changes in neuronal excitability mediated by ACh-gated potassium current. We also show that learning-dependent reordering of sequential firing during NREM sleep, together with spike timing-dependent plasticity (STDP), reconfigures neuronal firing rates across the network. This rescaling of firing rates has been reported in multiple brain circuits across periods of sleep. Our model and experimental data both suggest that this effect is amplified in neural circuits following learning. Together our data suggest that sleep may bias neural networks from firing rate-based towards phase-based information encoding to consolidate memories.

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