Journal Article

FZJ-2020-04257

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Neuronal spike-rate adaptation supports working memory in language processing

Fitz, H. (Corresponding author) ; Uhlmann, M. ; van den Broek, D. ; Duarte, R.FZJ* ; Hagoort, P. ; Petersson, K. M.

2020
National Acad. of Sciences Washington, DC

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Please use a persistent id in citations: http://hdl.handle.net/2128/26360  doi:10.1073/pnas.2000222117

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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Contributing Institute(s):

1. Computational and Systems Neuroscience (INM-6)
2. Theoretical Neuroscience (IAS-6)
3. Jara-Institut Brain structure-function relationships (INM-10)

Research Program(s):

1. 574 - Theory, modelling and simulation (POF3-574) (POF3-574)

Appears in the scientific report 2020

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