Neuronal spike-rate adaptation supports working memory in language processing

Fitz, H., Uhlmann, M., Van den Broek, D., Duarte, R., Hagoort, P., & Petersson, K. M. (2020). Neuronal spike-rate adaptation supports working memory in language processing. Proceedings of the National Academy of Sciences of the United States of America, 117(34), 20881-20889. doi:10.1073/pnas.2000222117.
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 pro- vide 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 mech- anism does not rely on persistent activity, excitatory feedback, or synap- tic plasticity for storage. Instead, information is maintained in adaptive conductances that reduce firing rates and can be accessed directly with- out cued retrieval. Memory span is systematically related to both the time constant of adaptation and baseline levels of neuronal excitability. Inter- ference 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 towards a computational neurobiology of language.
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