Neuronal spike-rate adaptation supports working memory in language processing
Number of pages
SourceProceedings of the National Academy of Sciences USA, 117, 34, (2020), pp. 20881-20889
Article / Letter to editor
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SW OZ DCC PL
PI Group Neurobiology of Language
Proceedings of the National Academy of Sciences USA
Subject110 000 Neurocognition of Language; Psycholinguistics
To understand an utterance, words have to be remembered and rapidly combined into an interpretation. How neurobiology supports this feat is currently unknown. One proposal that we investigate here is that information is stored and manipulated within single neurons. Depending on input history, neurons show different spike responses, and this adaptation constitutes a form of processing memory on short timescales. We implemented this approach as spike-rate adaptation that decreases neuronal excitability. Through computer simulations we show that this mechanism is suitable to establish meaning relations in sequential language processing. This account of working memory complements the more traditional views that information is stored in persistent spiking activity or short-lived synaptic changes.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. Simulation data and model code data have been deposited in the Max Planck Institute for Psycholinguistics Archive (https://hdl.handle.net/1839/8a42116e-77a7-46e1-b838-beee60e4e11a).
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