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  • 1.
    Benjaminsson, Simon
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Silverstein, David
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Herman, Pawel
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Melis, Paul
    Visualization Group, SARA, Amsterdam, The Netherlands.
    Slavnić, Vladimir
    Scientific Computing Laboratory, Institute of Physics Belgrade, University of Belgrade.
    Spasojević, Marko
    Scientific Computing Laboratory, Institute of Physics Belgrade, University of Belgrade.
    Alexiev, Kiril
    Department of Mathematical Methods for Sensor Information Processing, Institute of Information and Communication Technologies, Bulgaria.
    Lansner, Anders
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Visualization of Output from Large-Scale Brain Simulations2012Rapport (Övrigt vetenskapligt)
    Abstract [en]

    This project concerned the development of tools for visualization of output from brain simulations performed on supercomputers. The project had two main parts: 1) creating visualizations using large-scale simulation output from existing neural simulation codes, and 2) making extensions to  some of the existing codes to allow interactive runtime (in-situ) visualization. In 1) simulation data was converted to HDF5 format and split over multiple files. Visualization pipelines were created for different types of visualizations, e.g. voltage and calcium. In 2) by using the VisIt visualization application and its libsim library, simulation code was instrumented so that VisIt could access simulation data directly. The simulation code was instrumented and tested on different clusters where control of simulation was demonstrated and in-situ visualization of neural unit’s and population data was achieved.

  • 2.
    Lundqvist, Mikael
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB. Stockholm University, Sweden.
    Herman, Pawel
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB. Stockholm University, Sweden.
    Palva, M.
    Palva, S.
    Silverstein, David
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB. Stockholm University, Sweden.
    Lansner, Anders
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB. Stockholm University, Sweden.
    Stimulus detection rate and latency, firing rates and 1-40Hz oscillatory power are modulated by infra-slow fluctuations in a bistable attractor network model2013Ingår i: NeuroImage, ISSN 1053-8119, E-ISSN 1095-9572, Vol. 83, s. 458-471Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Recordings of membrane and field potentials, firing rates, and oscillation amplitude dynamics show that neuronal activity levels in cortical and subcortical structures exhibit infra-slow fluctuations (ISFs) on time scales from seconds to hundreds of seconds. Similar ISFs are salient also in blood-oxygenation-level dependent (BOLD) signals as well as in psychophysical time series. Functional consequences of ISFs are not fully understood. Here, they were investigated along with dynamical implications of ISFs in large-scale simulations of cortical network activity. For this purpose, a biophysically detailed hierarchical attractor network model displaying bistability and operating in an oscillatory regime was used. ISFs were imposed as slow fluctuations in either the amplitude or frequency of fast synaptic noise. We found that both mechanisms produced an ISF component in the synthetic local field potentials (LFPs) and modulated the power of 1-40. Hz oscillations. Crucially, in a simulated threshold-stimulus detection task (TSDT), these ISFs were strongly correlated with stimulus detection probabilities and latencies. The results thus show that several phenomena observed in many empirical studies emerge concurrently in the model dynamics, which yields mechanistic insight into how infra-slow excitability fluctuations in large-scale neuronal networks may modulate fast oscillations and perceptual processing. The model also makes several novel predictions that can be experimentally tested in future studies.

  • 3.
    Silverstein, David N.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Parallelldatorcentrum, PDC. Stockholm Brain Institute, Karolinska institutet, Sweden.
    A computational investigation of feedforward and feedback processing in metacontrast backward masking2015Ingår i: Frontiers in Psychology, ISSN 1664-1078, E-ISSN 1664-1078, Vol. 6, artikel-id 6Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In human perception studies, visual backward masking has been used to understand the temporal dynamics of subliminal vs. conscious perception. When a brief target stimulus is followed by a masking stimulus after a short interval of <100 ms, performance on the target is impaired when the target and mask are in close spatial proximity. While the psychophysical properties of backward masking have been studied extensively, there is still debate on the underlying cortical dynamics. One prevailing theory suggests that the impairment of target performance due to the mask is the result of lateral inhibition between the target and mask in feedforward processing. Another prevailing theory suggests that this impairment is due to the interruption of feedback processing of the target by the mask. This computational study demonstrates that both aspects of these theories may be correct. Using a biophysical model of V1 and V2, visual processing was modeled as interacting neocortical attractors, which must propagate up the visual stream. If an activating target attractor in V1 is quiesced enough with lateral inhibition from a mask, or not reinforced by recurrent feedback, it is more likely to burn out before becoming fully active and progressing through V2 and beyond. Results are presented which simulate metacontrast backward masking with an increasing stimulus interval and with the presence and absence of feedback activity. This showed that recurrent feedback diminishes backward masking effects and can make conscious perception more likely. One model configuration presented a metacontrast noise mask in the same hypercolumns as the target, and produced type-A masking. A second model configuration presented a target line with two parallel adjacent masking lines, and produced type-B masking. Future work should examine how the model extends to more complex spatial mask configurations.

  • 4.
    Silverstein, David N.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Beräkningsvetenskap och beräkningsteknik (CST).
    Investigations of neural attractor dynamics in human visual awareness2018Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
    Abstract [en]

    What we see, how we see it and what emotions may arise from stimuli has long been studied by philosophers, psychologists, medical doctors and neuroscientists. This thesis work investigates a particular view on the possible dynamics, utilizing computational models of spiking neural attractor networks. From neurological studies on humans and other primates, we know visual perception and recognition of objects occur partly along the visual ventral stream, from V1 to V2, V4, IT and downstream to other areas. This visual awareness can be both conscious and unconscious and may also trigger an emotional response. As seen from many psychophysical experiments in backward masking (BM) and attentional blink (AB), some spatial and temporal dynamics can determine what becomes visually conscious and what does not. To explore this computationally, biophysical models of BM and AB were implemented and simulated to mimic human experiments, with the assumption that neural assemblies as attractor networks activate and propagate along the ventral stream and beyond. It was observed that attractor interference between percepts in sensory and associative cortex can occur during this activity. During typical human AB experimental trials in which two expected target symbols amongst distractors are presented less than 500 ms apart, the second target is often not reported as seen. When simulating this paradigm as two expected target neural attractors amongst distractors, it was observed in the present work that an initial attractor in associative cortex can impede the activation and propagation of a following attractor, which mimics missing conscious perception of the second target. It was also observed that simulating the presence of benzodiazepines (GABA agonists) will slow cortical dynamics and increase the AB, as previously shown in human experiments.

    During typical human BM experimental trials in which a brief target stimulus is followed by a masking stimulus after a short interval of less than 100 ms, recognition of the target can be impaired when in close spatial proximity. When simulating this paradigm using a biophysical model of V1 and V2 with feedforward and feedback connections, attractor targets were activated in V1 before imposition of a proximal metacontrast mask. If an activating target attractor in V1 is quiesced enough with lateral inhibition from a mask, or not reinforced by recurrent feedback from feedforward activation in V2, it is more likely to burn out before becoming fully active and progressing through V2 and beyond. BM was also simulated with an increasing stimulus interval and with the presence and absence of feedback activity. This showed that recurrent feedback diminishes BM effects and can make conscious perception more likely.

    To better understand possible emotional components of visual perception and early regulation, visual signaling pathways to the amygdala were investigated and proposed for emotional salience and the possible onset of fear. While one subcortical and likely unconscious pathway (before amydala efferent signaling) was affirmed via the superior colliculus and pulvinar, four others traversed through the ventral stream. One traversed though IT on recognition, another via the OFC on conditioning, and two other possibly conscious pathways traversed though the parietal and then prefrontal cortex, one excitatory pathway via the ventral-medial area and one regulatory pathway via the ventral-lateral area. Predicted latencies were determined for these signaling pathways, which can be experimentally testable. The conscious feeling of fear itself may not occur until after interoceptive inspection.

    A pathology of attractor dynamics was also investigated, which can occur from the presence of a brain tumor in white matter. Due to degradation from tumor invasion of white matter projections between two simulated neocortical patches, information transfer between separate neural attractors degraded, leading first to recall errors and later to epileptic-like activity. Neural plasticity could partially compensate up to a point, before transmission failure. This suggests that once epileptic seizures start in glioma patients, compensatory plasticity may already be exhausted. Interestingly, the presence of additional noise could also partially compensate for white matter loss.

  • 5.
    Silverstein, David N.
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Parallelldatorcentrum, PDC. KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB. Stockholm Brain Institute, Karolinska Institutet, Sweden.
    Ingvar, Martin
    Karolinska Institutet, Sweden.
    A multi-pathway hypothesis for human visual fear signaling2015Ingår i: Frontiers in Systems Neuroscience, ISSN 1662-5137, E-ISSN 1662-5137, Vol. 9, artikel-id 101Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    A hypothesis is proposed for five visual fear signaling pathways in humans, based on an analysis of anatomical connectivity from primate studies and human functional connectvity and tractography from brain imaging studies. Earlier work has identified possible subcortical and cortical fear pathways known as the “low road” and “high road,” which arrive at the amygdala independently. In addition to a subcortical pathway, we propose four cortical signaling pathways in humans along the visual ventral stream. All four of these traverse through the LGN to the visual cortex (VC) and branching off at the inferior temporal area, with one projection directly to the amygdala; another traversing the orbitofrontal cortex; and two others passing through the parietal and then prefrontal cortex, one excitatory pathway via the ventral-medial area and one regulatory pathway via the ventral-lateral area. These pathways have progressively longer propagation latencies and may have progressively evolved with brain development to take advantage of higher-level processing. Using the anatomical path lengths and latency estimates for each of these five pathways, predictions are made for the relative processing times at selective ROIs and arrival at the amygdala, based on the presentation of a fear-relevant visual stimulus. Partial verification of the temporal dynamics of this hypothesis might be accomplished using experimental MEG analysis. Possible experimental protocols are suggested.

  • 6.
    Silverstein, David N.
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB. Karolinska Institute, Sweden.
    Lansner, Anders
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB. Karolinska Institute, Sweden.
    Is attentional blink a byproduct of neocortical attractors?2011Ingår i: Frontiers in Computational Neuroscience, ISSN 1662-5188, E-ISSN 1662-5188, Vol. 5, artikel-id 13Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This study proposes a computational model for attentional blink or "blink of the mind," a phenomenon where a human subject misses perception of a later expected visual pattern as two expected visual patterns are presented less than 500 ms apart. A neocortical patch modeled as an attractor network is stimulated with a sequence of 14 patterns 100 ms apart, two of which are expected targets. Patterns that become active attractors are considered recognized. A neocortical patch is represented as a square matrix of hypercolumns, each containing a set of minicolumns with synaptic connections within and across both minicolumns and hypercolumns. Each minicolumn consists of locally connected layer 2/3 pyramidal cells with interacting basket cells and layer 4 pyramidal cells for input stimulation. All neurons are implemented using the Hodgkin-Huxley multi-compartmental cell formalism and include calcium dynamics, and they interact via saturating and depressing AMPA/NMDA and GABA(A) synapses. Stored patterns are encoded with global connectivity of minicolumns across hypercolumns and active patterns compete as the result of lateral inhibition in the network. Stored patterns were stimulated over time intervals to create attractor interference measurable with synthetic spike traces. This setup corresponds with item presentations in human visual attentional blink studies. Stored target patterns were depolarized while distractor patterns where hyperpolarized to represent expectation of items in working memory. Simulations replicated the basic attentional blink phenomena and showed a reduced blink when targets were more salient. Studies on the inhibitory effect of benzodiazepines on attentional blink in human subjects were compared with neocortical simulations where the GABA(A) receptor conductance and decay time were increased. Simulations showed increases in the attentional blink duration, agreeing with observations in human studies. In addition, sensitivity analysis was performed on key parameters of the model, including Ca2+-gated K+ channel conductance, synaptic depression, GABA(A) channel conductance and the NMDA/AMPA ratio of charge entry.

  • 7. Szalisznyo, Krisztina
    et al.
    Silverstein, David N.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Parallelldatorcentrum, PDC.
    Duffau, Hugues
    Smits, Anja
    Pathological Neural Attractor Dynamics in Slowly Growing Gliomas Supports an Optimal Time Frame for White Matter Plasticity2013Ingår i: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 8, nr 7, s. e69798-Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Neurological function in patients with slowly growing brain tumors can be preserved even after extensive tumor resection. However, the global process of cortical reshaping and cerebral redistribution cannot be understood without taking into account the white matter tracts. The aim of this study was to predict the functional consequences of tumor-induced white matter damage by computer simulation. A computational model was proposed, incorporating two cortical patches and the white matter connections of the uncinate fasciculus. Tumor-induced structural changes were modeled such that different aspects of the connectivity were altered, mimicking the biological heterogeneity of gliomas. The network performance was quantified by comparing memory pattern recall and the plastic compensatory capacity of the network was analyzed. The model predicts an optimal level of synaptic conductance boost that compensates for tumor-induced connectivity loss. Tumor density appears to change the optimal plasticity regime, but tumor size does not. Compensatory conductance values that are too high lead to performance loss in the network and eventually to epileptic activity. Tumors of different configurations show differences in memory recall performance with slightly lower plasticity values for dense tumors compared to more diffuse tumors. Simulation results also suggest an optimal noise level that is capable of increasing the recall performance in tumor-induced white matter damage. In conclusion, the model presented here is able to capture the influence of different tumor-related parameters on memory pattern recall decline and provides a new way to study the functional consequences of white matter invasion by slowly growing brain tumors.

  • 8. Szalisznyó, K.
    et al.
    Silverstein, David N.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Beräkningsvetenskap och beräkningsteknik (CST).
    Tóth, J.
    Neural dynamics in co-morbid schizophrenia and OCD: A computational approach2019Ingår i: Journal of Theoretical Biology, ISSN 0022-5193, E-ISSN 1095-8541, Vol. 473, s. 80-94Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The co-morbidity of obsessive-compulsive disorder (OCD) and schizophrenia is higher than what would be expected by chance and the common underlying neuropathophysiology is not well understood. Repetitive stereotypes and routines can be caused by perseverative thoughts and motor sequences in both of these disorders. We extended a previously published computational model to investigate cortico-striatal network dynamics. Given the considerable overlap in symptom phenomenology and the high degree of co-morbidity between OCD and schizophrenia, we examined the dynamical consequences of functional connectivity variations in the overlapping network. This was achieved by focusing on the emergence of network oscillatory activity and examining parameter sensitivity. Opposing activity levels are present in orbitofrontal cortex (OFC) and anterior cingulate cortex (ACC) in schizophrenia and OCD. We found that with over-compensation of the primary pathology, emergence of the other disorder can occur. The oscillatory behavior is delicately modulated by connections between the OFC/ACC to the ventral and dorsal striatum and by the coupling between the ACC and dorsolateral prefrontal cortex (DLPFC). Modulation on cortical self-inhibition (e.g. serotonin reuptake inhibitor treatment) together with dopaminergic input to the striatum (e.g. anti-dopaminergic medication) has non-trivial complex effects on the network oscillatory behavior, with an optimal modulatory window. Additionally, there are several disruption mechanisms and compensatory processes in the cortico-striato-thalamic network which may contribute to the underlying neuropathophysiology and clinical heterogeneity in schizo-obsessive spectrum disorders. Our mechanistic model predicts that dynamic over-compensation of the primarily occuring neuropathophysiology can lead to the secondary co-morbid disease.

  • 9. Szalisznyó, K.
    et al.
    Silverstein, David
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsvetenskap och beräkningsteknik (CST). Karolinska Institutet, Sweden.
    Teichmann, M.
    Duffau, H.
    Smits, A.
    Cortico-striatal language pathways dynamically adjust for syntactic complexity: A computational study2017Ingår i: Brain and Language, ISSN 0093-934X, E-ISSN 1090-2155, Vol. 164, s. 53-62Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    A growing body of literature supports a key role of fronto-striatal circuits in language perception. It is now known that the striatum plays a role in engaging attentional resources and linguistic rule computation while also serving phonological short-term memory capabilities. The ventral semantic and the dorsal phonological stream dichotomy assumed for spoken language processing also seems to play a role in cortico-striatal perception. Based on recent studies that correlate deep Broca-striatal pathways with complex syntax performance, we used a previously developed computational model of frontal-striatal syntax circuits and hypothesized that different parallel language pathways may contribute to canonical and non-canonical sentence comprehension separately. We modified and further analyzed a thematic role assignment task and corresponding reservoir computing model of language circuits, as previously developed by Dominey and coworkers. We examined the models performance under various parameter regimes, by influencing how fast the presented language input decays and altering the temporal dynamics of activated word representations. This enabled us to quantify canonical and non-canonical sentence comprehension abilities. The modeling results suggest that separate cortico-cortical and cortico-striatal circuits may be recruited differently for processing syntactically more difficult and less complicated sentences. Alternatively, a single circuit would need to dynamically and adaptively adjust to syntactic complexity.

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