Publications

Displaying 1 - 15 of 15
  • 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.

    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 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.
  • Fransson, P., Merboldt, K.-D., Petersson, K. M., Ingvar, M., & Frahm, J. (2002). On the effects of spatial filtering — A comparative fMRI study of episodic memory encoding at high and low resolution. NeuroImage, 16(4), 977-984. doi:10.1006/nimg.2002.1079.

    Abstract

    Theeffects of spatial filtering in functional magnetic resonance imaging were investigated by reevaluating the data of a previous study of episodic memory encoding at 2 × 2 × 4-mm3 resolution with use of a SPM99 analysis involving a Gaussian kernel of 8-mm full width at half maximum. In addition, a multisubject analysis of activated regions was performed by normalizing the functional images to an approximate Talairach brain atlas. In individual subjects, spatial filtering merged activations in anatomically separated brain regions. Moreover, small foci of activated pixels which originated from veins became blurred and hence indistinguishable from parenchymal responses. The multisubject analysis resulted in activation of the hippocampus proper, a finding which could not be confirmed by the activation maps obtained at high resolution. It is concluded that the validity of multisubject fMRI analyses can be considerably improved by first analyzing individual data sets at optimum resolution to assess the effects of spatial filtering and minimize the risk of signal contamination by macroscopically visible vessels.
  • Nyberg, L., Forkstam, C., Petersson, K. M., Cabeza, R., & Ingvar, M. (2002). Brain imaging of human memory systems: Between-systems similarities and within-system differences. Cognitive Brain Research, 13(2), 281-292. doi:10.1016/S0926-6410(02)00052-6.

    Abstract

    There is much evidence for the existence of multiple memory systems. However, it has been argued that tasks assumed to reflect different memory systems share basic processing components and are mediated by overlapping neural systems. Here we used multivariate analysis of PET-data to analyze similarities and differences in brain activity for multiple tests of working memory, semantic memory, and episodic memory. The results from two experiments revealed between-systems differences, but also between-systems similarities and within-system differences. Specifically, support was obtained for a task-general working-memory network that may underlie active maintenance. Premotor and parietal regions were salient components of this network. A common network was also identified for two episodic tasks, cued recall and recognition, but not for a test of autobiographical memory. This network involved regions in right inferior and polar frontal cortex, and lateral and medial parietal cortex. Several of these regions were also engaged during the working-memory tasks, indicating shared processing for episodic and working memory. Fact retrieval and synonym generation were associated with increased activity in left inferior frontal and middle temporal regions and right cerebellum. This network was also associated with the autobiographical task, but not with living/non-living classification, and may reflect elaborate retrieval of semantic information. Implications of the present results for the classification of memory tasks with respect to systems and/or processes are discussed.
  • Petersson, K. M. (2002). Brain physiology. In R. Behn, & C. Veranda (Eds.), Proceedings of The 4th Southern European School of the European Physical Society - Physics in Medicine (pp. 37-38). Montreux: ESF.
  • Petrovic, P., Kalso, E., Petersson, K. M., & Ingvar, M. (2002). Placebo and opioid analgesia - Imaging a shared neuronal network. Science, 295(5560), 1737-1740. doi:10.1126/science.1067176.

    Abstract

    It has been suggested that placebo analgesia involves both higher order cognitive networks and endogenous opioid systems. The rostral anterior cingulate cortex (rACC) and the brainstem are implicated in opioid analgesia, suggesting a similar role for these structures in placebo analgesia. Using positron emission tomography, we confirmed that both opioid and placebo analgesia are associated with increased activity in the rACC. We also observed a covariation between the activity in the rACC and the brainstem during both opioid and placebo analgesia, but not during the pain-only condition. These findings indicate a related neural mechanism in placebo and opioid analgesia.
  • Petrovic, P., Kalso, E., Petersson, K. M., & Ingvar, M. (2002). Placebo and opioid analgesia - Imaging a shared neuronal network. Science, 295(5560), 1737-1740. doi:10.1126/science.1067176.

    Abstract

    It has been suggested that placebo analgesia involves both higher order cognitive networks and endogenous opioid systems. The rostral anterior cingulate cortex (rACC) and the brainstem are implicated in opioid analgesia, suggesting a similar role for these structures in placebo analgesia. Using positron emission tomography, we confirmed that both opioid and placebo analgesia are associated with increased activity in the rACC. We also observed a covariation between the activity in the rACC and the brainstem during both opioid and placebo analgesia, but not during the pain-only condition. These findings indicate a related neural mechanism in placebo and opioid analgesia.
  • Petrovic, P., Petersson, K. M., Hansson, P., & Ingvar, M. (2002). A regression analysis study of the primary somatosensory cortex during pain. NeuroImage, 16(4), 1142-1150. doi:10.1006/nimg.2002.1069.

    Abstract

    Several functional imaging studies of pain, using a number of different experimental paradigms and a variety of reference states, have failed to detect activations in the somatosensory cortices, while other imaging studies of pain have reported significant activations in these regions. The role of the somatosensory areas in pain processing has therefore been debated. In the present study the left hand was immersed in painfully cold water (standard cold pressor test) and in nonpainfully cold water during 2 min, and PET-scans were obtained either during the first or the second minute of stimulation. We observed no significant increase of activity in the somatosensory regions when the painful conditions were directly compared with the control conditions. In order to better understand the role of the primary somatosensory cortex (S1) in pain processing we used a regression analysis to study the relation between a ROI (region of interest) in the somatotopic S1-area for the stimulated hand and other regions known to be involved in pain processing. We hypothesized that although no increased activity was observed in the S1 during pain, this region would change its covariation pattern during noxious input as compared to the control stimulation if it is involved in or affected by the processing of pain. In the nonpainful cold conditions widespread regions of the ipsilateral and contralateral somatosensory cortex showed a positive covariation with the activity in the S1-ROI. However, during the first and second minute of pain this regression was significantly attenuated. During the second minute of painful stimulation there was a significant positive covariation between the activity in the S1-ROI and the other regions that are known to be involved in pain processing. Importantly, this relation was significantly stronger for the insula and the orbitofrontal cortex bilaterally when compared to the nonpainful state. The results indicate that the S1-cortex may be engaged in or affected by the processing of pain although no differential activity is observed when pain is compared with the reference condition.
  • Sandberg, A., Lansner, A., Petersson, K. M., & Ekeberg, Ö. (2002). A Bayesian attractor network with incremental learning. Network: Computation in Neural Systems, 13(2), 179-194. doi:10.1088/0954-898X/13/2/302.

    Abstract

    A realtime online learning system with capacity limits needs to gradually forget old information in order to avoid catastrophic forgetting. This can be achieved by allowing new information to overwrite old, as in a so-called palimpsest memory. This paper describes an incremental learning rule based on the Bayesian confidence propagation neural network that has palimpsest properties when employed in an attractor neural network. The network does not suffer from catastrophic forgetting, has a capacity dependent on the learning time constant and exhibits faster convergence for newer patterns.
  • Carlsson, K., Petrovic, P., Skare, S., Petersson, K. M., & Ingvar, M. (2000). Tickling expectations: Neural processing in anticipation of a sensory stimulus. Journal of Cognitive Neuroscience, 12(4), 691-703. doi:10.1162/089892900562318.
  • Ingvar, M., & Petersson, K. M. (2000). Functional maps and brain networks. In A. W. Toga (Ed.), Brain mapping: The systems (pp. 111-140). San Diego: Academic Press.
  • Lansner, A., Sandberg, A., Petersson, K. M., & Ingvar, M. (2000). On forgetful attractor network memories. In H. Malmgren, M. Borga, & L. Niklasson (Eds.), Artificial neural networks in medicine and biology: Proceedings of the ANNIMAB-1 Conference, Göteborg, Sweden, 13-16 May 2000 (pp. 54-62). Heidelberg: Springer Verlag.

    Abstract

    A recurrently connected attractor neural network with a Hebbian learning rule is currently our best ANN analogy for a piece cortex. Functionally biological memory operates on a spectrum of time scales with regard to induction and retention, and it is modulated in complex ways by sub-cortical neuromodulatory systems. Moreover, biological memory networks are commonly believed to be highly distributed and engage many co-operating cortical areas. Here we focus on the temporal aspects of induction and retention of memory in a connectionist type attractor memory model of a piece of cortex. A continuous time, forgetful Bayesian-Hebbian learning rule is described and compared to the characteristics of LTP and LTD seen experimentally. More generally, an attractor network implementing this learning rule can operate as a long-term, intermediate-term, or short-term memory. Modulation of the print-now signal of the learning rule replicates some experimental memory phenomena, like e.g. the von Restorff effect.
  • Petersson, K. M., Reis, A., Askelöf, S., Castro-Caldas, A., & Ingvar, M. (2000). Language processing modulated by literacy: A network analysis of verbal repetition in literate and illiterate subjects. Journal of Cognitive Neuroscience, 12(3), 364-382. doi:10.1162/089892900562147.
  • Petrovic, P., Petersson, K. M., Ghatan, P., Stone-Elander, S., & Ingvar, M. (2000). Pain related cerebral activation is altered by a distracting cognitive task. Pain, 85, 19-30.

    Abstract

    It has previously been suggested that the activity in sensory regions of the brain can be modulated by attentional mechanisms during parallel cognitive processing. To investigate whether such attention-related modulations are present in the processing of pain, the regional cerebral blood ¯ow was measured using [15O]butanol and positron emission tomography in conditions involving both pain and parallel cognitive demands. The painful stimulus consisted of the standard cold pressor test and the cognitive task was a computerised perceptual maze test. The activations during the maze test reproduced findings in previous studies of the same cognitive task. The cold pressor test evoked signi®cant activity in the contralateral S1, and bilaterally in the somatosensory association areas (including S2), the ACC and the mid-insula. The activity in the somatosensory association areas and periaqueductal gray/midbrain were significantly modified, i.e. relatively decreased, when the subjects also were performing the maze task. The altered activity was accompanied with significantly lower ratings of pain during the cognitive task. In contrast, lateral orbitofrontal regions showed a relative increase of activity during pain combined with the maze task as compared to only pain, which suggests the possibility of the involvement of frontal cortex in modulation of regions processing pain
  • Sandberg, A., Lansner, A., Petersson, K. M., & Ekeberg, Ö. (2000). A palimpsest memory based on an incremental Bayesian learning rule. Neurocomputing, 32(33), 987-994. doi:10.1016/S0925-2312(00)00270-8.

    Abstract

    Capacity limited memory systems need to gradually forget old information in order to avoid catastrophic forgetting where all stored information is lost. This can be achieved by allowing new information to overwrite old, as in the so-called palimpsest memory. This paper describes a new such learning rule employed in an attractor neural network. The network does not exhibit catastrophic forgetting, has a capacity dependent on the learning time constant and exhibits recency e!ects in retrieval
  • Sandberg, A., Lansner, A., Petersson, K. M., & Ekeberg, Ö. (2000). A palimpsest memory based on an incremental Bayesian learning rule. In J. M. Bower (Ed.), Computational Neuroscience: Trends in Research 2000 (pp. 987-994). Amsterdam: Elsevier.

Share this page