Dick van den Broek


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  • 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.
  • Duarte, R., Uhlmann, M., Van den Broek, D., Fitz, H., Petersson, K. M., & Morrison, A. (2018). Encoding symbolic sequences with spiking neural reservoirs. In Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN). doi:10.1109/IJCNN.2018.8489114.


    Biologically inspired spiking networks are an important tool to study the nature of computation and cognition in neural systems. In this work, we investigate the representational capacity of spiking networks engaged in an identity mapping task. We compare two schemes for encoding symbolic input, one in which input is injected as a direct current and one where input is delivered as a spatio-temporal spike pattern. We test the ability of networks to discriminate their input as a function of the number of distinct input symbols. We also compare performance using either membrane potentials or filtered spike trains as state variable. Furthermore, we investigate how the circuit behavior depends on the balance between excitation and inhibition, and the degree of synchrony and regularity in its internal dynamics. Finally, we compare different linear methods of decoding population activity onto desired target labels. Overall, our results suggest that even this simple mapping task is strongly influenced by design choices on input encoding, state-variables, circuit characteristics and decoding methods, and these factors can interact in complex ways. This work highlights the importance of constraining computational network models of behavior by available neurobiological evidence.

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