Change search
Link to record
Permanent link

Direct link
BETA
Alternative names
Publications (10 of 36) Show all publications
Wang, R., Gäverth, J. & Herman, P. (2018). Changes in the neural and non-neural related properties of the spastic wrist flexors after treatment with botulinum toxin a in post-stroke subjects: An optimization study. Frontiers in Bioengineering and Biotechnology, 9(June), Article ID 73.
Open this publication in new window or tab >>Changes in the neural and non-neural related properties of the spastic wrist flexors after treatment with botulinum toxin a in post-stroke subjects: An optimization study
2018 (English)In: Frontiers in Bioengineering and Biotechnology, E-ISSN 2296-4185, Vol. 9, no June, article id 73Article in journal (Refereed) Published
Abstract [en]

Quantifying neural and non-neural contributions to the joint resistance in spasticity is essential for a better evaluation of different intervention strategies such as botulinum toxin A (BoTN-A). However, direct measurement of muscle mechanical properties and spasticity-related parameters in humans is extremely challenging. The aim of this study was to use a previously developed musculoskeletal model and optimization scheme to evaluate the changes of neural and non-neural related properties of the spastic wrist flexors during passive wrist extension after BoTN-A injection. Data of joint angle and resistant torque were collected from 21 chronic stroke patients before, and 4 and 12 weeks post BoTN-A injection using NeuroFlexor, which is a motorized force measurement device to passively stretch wrist flexors. The model was optimized by tuning the passive and stretch-related parameters to fit the measured torque in each participant. It was found that stroke survivors exhibited decreased neural components at 4 weeks post BoNT-A injection, which returned to baseline levels after 12 weeks. The decreased neural component was mainly due to the increased motoneuron pool threshold, which is interpreted as a net excitatory and inhibitory inputs to the motoneuron pool. Though the linear stiffness and viscosity properties of wrist flexors were similar before and after treatment, increased exponential stiffness was observed over time which may indicate a decreased range of motion of the wrist joint. Using a combination of modeling and experimental measurement, valuable insights into the treatment responses, i.e., transmission of motoneurons, are provided by investigating potential parameter changes along the stretch reflex pathway in persons with chronic stroke.

Place, publisher, year, edition, pages
Frontiers Media S.A., 2018
Keywords
Botulinum toxin A, Motoneuron pool, Muscle mechanical properties, Neuroflexor, Spasticity, Stretch reflex
National Category
Medical Laboratory and Measurements Technologies
Identifiers
urn:nbn:se:kth:diva-238192 (URN)10.3389/fbioe.2018.00073 (DOI)000440278100001 ()29963551 (PubMedID)2-s2.0-85048809207 (Scopus ID)
Note

QC 20181120

Available from: 2018-11-20 Created: 2018-11-20 Last updated: 2019-01-04Bibliographically approved
Lundqvist, M., Herman, P., Warden, M. R., Brincat, S. L. & Miller, E. K. (2018). Gamma and beta bursts during working memory readout suggest roles in its volitional control. Nature Communications, 9, Article ID 394.
Open this publication in new window or tab >>Gamma and beta bursts during working memory readout suggest roles in its volitional control
Show others...
2018 (English)In: Nature Communications, ISSN 2041-1723, E-ISSN 2041-1723, Vol. 9, article id 394Article in journal (Refereed) Published
Abstract [en]

Working memory (WM) activity is not as stationary or sustained as previously thought. There are brief bursts of gamma (similar to 50-120 Hz) and beta (similar to 20-35 Hz) oscillations, the former linked to stimulus information in spiking. We examined these dynamics in relation to readout and control mechanisms of WM. Monkeys held sequences of two objects in WM to match to subsequent sequences. Changes in beta and gamma bursting suggested their distinct roles. In anticipation of having to use an object for the match decision, there was an increase in gamma and spiking information about that object and reduced beta bursting. This readout signal was only seen before relevant test objects, and was related to premotor activity. When the objects were no longer needed, beta increased and gamma decreased together with object spiking information. Deviations from these dynamics predicted behavioral errors. Thus, beta could regulate gamma and the information in WM.

Place, publisher, year, edition, pages
NATURE PUBLISHING GROUP, 2018
National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:kth:diva-222415 (URN)10.1038/s41467-017-02791-8 (DOI)000423430900014 ()29374153 (PubMedID)2-s2.0-85041207564 (Scopus ID)
Note

QC 20180228

Available from: 2018-02-28 Created: 2018-02-28 Last updated: 2018-02-28Bibliographically approved
Ravichandran, N. B., Yang, F., Peters, C., Lansner, A. & Herman, P. (2018). Pedestrian simulation as multi-objective reinforcement learning. In: Proceedings of the 18th International Conference on Intelligent Virtual Agents, IVA 2018: . Paper presented at 18th ACM International Conference on Intelligent Virtual Agents, IVA 2018; Western Sydney University's new Parramatta City Campus, Sydney; Australia; 5 November 2018 through 8 November 2018 (pp. 307-312).
Open this publication in new window or tab >>Pedestrian simulation as multi-objective reinforcement learning
Show others...
2018 (English)In: Proceedings of the 18th International Conference on Intelligent Virtual Agents, IVA 2018, 2018, p. 307-312Conference paper, Published paper (Refereed)
Abstract [en]

Modelling and simulation of pedestrian crowds require agents to reach pre-determined goals and avoid collisions with static obstacles and dynamic pedestrians, while maintaining natural gait behaviour. We model pedestrians as autonomous, learning, and reactive agents employing Reinforcement Learning (RL). Typical RL-based agent simulations suffer poor generalization due to handcrafted reward function to ensure realistic behaviour. In this work, we model pedestrians in a modular framework integrating navigation and collision-avoidance tasks as separate modules. Each such module consists of independent state-spaces and rewards, but with shared action-spaces. Empirical results suggest that such modular framework learning models can show satisfactory performance without tuning parameters, and we compare it with the state-of-art crowd simulation methods.

Keywords
Agent-based simulation, Multi-objective learning, Parallel learning, Reinforcement learning
National Category
Other Engineering and Technologies
Identifiers
urn:nbn:se:kth:diva-241487 (URN)10.1145/3267851.3267914 (DOI)2-s2.0-85058477147 (Scopus ID)9781450360135 (ISBN)
Conference
18th ACM International Conference on Intelligent Virtual Agents, IVA 2018; Western Sydney University's new Parramatta City Campus, Sydney; Australia; 5 November 2018 through 8 November 2018
Note

QC 20190123

Available from: 2019-01-23 Created: 2019-01-23 Last updated: 2019-06-03Bibliographically approved
Iatropoulos, G., Herman, P., Lansner, A., Karlgren, J., Larsson, M. & Olofsson, J. K. (2018). The language of smell: Connecting linguistic and psychophysical properties of odor descriptors. Cognition, 178, 37-49
Open this publication in new window or tab >>The language of smell: Connecting linguistic and psychophysical properties of odor descriptors
Show others...
2018 (English)In: Cognition, ISSN 0010-0277, E-ISSN 1873-7838, Vol. 178, p. 37-49Article in journal (Refereed) Published
Abstract [en]

The olfactory sense is a particularly challenging domain for cognitive science investigations of perception, memory, and language. Although many studies show that odors often are difficult to describe verbally, little is known about the associations between olfactory percepts and the words that describe them. Quantitative models of how odor experiences are described in natural language are therefore needed to understand how odors are perceived and communicated. In this study, we develop a computational method to characterize the olfaction-related semantic content of words in a large text corpus of internet sites in English. We introduce two new metrics: olfactory association index (OAI, how strongly a word is associated with olfaction) and olfactory specificity index (OSI, how specific a word is in its description of odors). We validate the OAI and OSI metrics using psychophysical datasets by showing that terms with high OAI have high ratings of perceived olfactory association and are used to describe highly familiar odors. In contrast, terms with high OSI have high inter-individual consistency in how they are applied to odors. Finally, we analyze Dravnieks's (1985) dataset of odor ratings in terms of OAI and OSI. This analysis reveals that terms that are used broadly (applied often but with moderate ratings) tend to be olfaction-unrelated and abstract (e.g., “heavy” or “light”; low OAI and low OSI) while descriptors that are used selectively (applied seldom but with high ratings) tend to be olfaction-related (e.g., “vanilla” or “licorice”; high OAI). Thus, OAI and OSI provide behaviorally meaningful information about olfactory language. These statistical tools are useful for future studies of olfactory perception and cognition, and might help integrate research on odor perception, neuroimaging, and corpus-based linguistic models of semantic organization.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
Computational linguistics, Distributional semantics, Odour identification, Odour naming, Sensory lexicon, Sensory-semantic integration
National Category
Language Technology (Computational Linguistics)
Identifiers
urn:nbn:se:kth:diva-228700 (URN)10.1016/j.cognition.2018.05.007 (DOI)000439402400004 ()2-s2.0-85047188460 (Scopus ID)
Note

QC 20180530

Available from: 2018-05-30 Created: 2018-05-30 Last updated: 2018-08-06Bibliographically approved
Lundqvist, M., Herman, P. & Miller, E. K. (2018). Working Memory: Delay Activity, Yes! Persistent Activity? Maybe Not. Journal of Neuroscience, 38(32), 7013-7019
Open this publication in new window or tab >>Working Memory: Delay Activity, Yes! Persistent Activity? Maybe Not
2018 (English)In: Journal of Neuroscience, ISSN 0270-6474, E-ISSN 1529-2401, Vol. 38, no 32, p. 7013-7019Article in journal (Refereed) Published
Abstract [en]

Persistent spiking has been thought to underlie working memory (WM). However, virtually all of the evidence for this comes from studies that averaged spiking across time and across trials, which masks the details. On single trials, activity often occurs in sparse transient bursts. This has important computational and functional advantages. In addition, examination of more complex tasks reveals neural coding in WM is dynamic over the course of a trial. All this suggests that spiking is important for WM, but that its role is more complex than simply persistent spiking.

Place, publisher, year, edition, pages
Society for Neuroscience, 2018
Keywords
working memory, transient dynamics, persistent activity, computational models
National Category
Neurology
Identifiers
urn:nbn:se:kth:diva-233413 (URN)10.1523/JNEUROSCI.2485-17.2018 (DOI)000441039800001 ()30089640 (PubMedID)
Note

QC 20180821

Available from: 2018-08-21 Created: 2018-08-21 Last updated: 2018-08-21Bibliographically approved
Mbuvha, R., Jonsson, M., Ehn, N. & Herman, P. (2017). Bayesian neural networks for one-hour ahead wind power forecasting. In: 2017 6th International Conference on Renewable Energy Research and Applications, ICRERA 2017: . Paper presented at 6th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2017, 5 November 2017 through 8 November 2017, San Diego, United States (pp. 591-596). Institute of Electrical and Electronics Engineers (IEEE), 2017
Open this publication in new window or tab >>Bayesian neural networks for one-hour ahead wind power forecasting
2017 (English)In: 2017 6th International Conference on Renewable Energy Research and Applications, ICRERA 2017, Institute of Electrical and Electronics Engineers (IEEE), 2017, Vol. 2017, p. 591-596Conference paper, Published paper (Refereed)
Abstract [en]

The greatest concern facing renewable energy sources like wind is the uncertainty in production volumes as their generation ability is inherently dependent on weather conditions. When providing forecasts for newly commissioned wind farms there is a limited amount of historical power production data, while the number of potential features from different weather forecast providers is vast. Bayesian regularization is therefore seen as a possible technique for reducing model overfitting problems that may arise. This work investigates Bayesian Neural Networks for one-hour ahead forecasting of wind power generation. Initial results show that Bayesian Neural Networks display equivalent predictive performance to Neural Networks trained by Maximum Likelihood. Further results show that Bayesian Neural Networks become superior after removing irrelevant features using Automatic Relevance Determination(ARD).

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017
Series
International Conference on Renewable Energy Research and Applications
Keywords
Ahead, Automatic relevance determination, Bayesian, Forecasting, Neural networks, One-hour, Wind power
National Category
Communication Systems
Identifiers
urn:nbn:se:kth:diva-224241 (URN)10.1109/DISTRA.2017.8191129 (DOI)000426708600096 ()2-s2.0-85042722249 (Scopus ID)9781538620953 (ISBN)
Conference
6th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2017, 5 November 2017 through 8 November 2017, San Diego, United States
Note

QC 20180315

Available from: 2018-03-15 Created: 2018-03-15 Last updated: 2018-03-23Bibliographically approved
Herman, P., Prasad, G. & McGinnity, T. M. (2017). Designing an Interval Type-2 Fuzzy Logic System for Handling Uncertainty Effects in Brain-Computer Interface Classification of Motor Imagery Induced EEG Patterns. IEEE transactions on fuzzy systems, 25(1), 29-42
Open this publication in new window or tab >>Designing an Interval Type-2 Fuzzy Logic System for Handling Uncertainty Effects in Brain-Computer Interface Classification of Motor Imagery Induced EEG Patterns
2017 (English)In: IEEE transactions on fuzzy systems, ISSN 1063-6706, E-ISSN 1941-0034, Vol. 25, no 1, p. 29-42Article in journal (Refereed) Published
Abstract [en]

One of the urgent challenges in the automated analysis and interpretation of electrical brain activity is the effective handling of uncertainties associated with the complexity and variability of brain dynamics, reflected in the nonstationary nature of brain signals such as electroencephalogram (EEG). This poses a severe problem for existing approaches to the classification task within brain-computer interface (BCI) systems. Recently emerged type-2 fuzzy logic (T2FL) methodology has shown a remarkable potential in dealing with uncertain information given limited insight into the nature of the data-generating mechanism. The objective of this work is, thus, to examine the applicability of the T2FL approach to the problem of EEG pattern recognition. In particular, the focus is two-fold: 1) the design methodology for the interval T2FL system (IT2FLS) that can robustly deal with inter-session as well as within-session manifestations of nonstationary spectral EEG correlates of motor imagery, and 2) the comprehensive examination of the proposed fuzzy classifier in both off-line and on-line EEG classification case studies. The on-line evaluation of the IT2FLS-controlled real-time neurofeedback over multiple recording sessions holds special importance for EEG-based BCI technology. In addition, a retrospective comparative analysis accounting for other popular BCI classifiers such as linear discriminant analysis, kernel Fisher discriminant, and support vector machines as well as a conventional type-1 FLS, simulated off-line on the recorded EEGs, has demonstrated the enhanced potential of the proposed IT2FLS approach to robustly handle uncertainty effects in BCI classification.

Place, publisher, year, edition, pages
IEEE Press, 2017
Keywords
Brain-computer interface (BCI), electroencephalogram (EEG), interval type-2 fuzzy systems, pattern recognition, real-time systems, uncertainty
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:kth:diva-204097 (URN)10.1109/TFUZZ.2016.2637934 (DOI)000396393100004 ()2-s2.0-85014919560 (Scopus ID)
Note

QC 20170329

Available from: 2017-03-29 Created: 2017-03-29 Last updated: 2018-01-13Bibliographically approved
Herman, P., Benjaminsson, S. & Lansner, A. (2017). Odor recognition in an attractor network model of the mammalian olfactory cortex. In: 2017 International Joint Conference on Neural Networks (IJCNN): . Paper presented at 2017 International Joint Conference on Neural Networks, IJCNN 2017, Anchorage, United States, 14 May 2017 through 19 May 2017 (pp. 3561-3568). Institute of Electrical and Electronics Engineers (IEEE), Article ID 7966304.
Open this publication in new window or tab >>Odor recognition in an attractor network model of the mammalian olfactory cortex
2017 (English)In: 2017 International Joint Conference on Neural Networks (IJCNN), Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 3561-3568, article id 7966304Conference paper, Published paper (Refereed)
Abstract [en]

Odor recognition constitutes a key functional aspect of olfaction and in real-world scenarios it requires that odorants occurring in complex chemical mixtures are identified irrespective of their concentrations. We investigate this challenging pattern recognition problem in the framework of a three-stage computational model of the mammalian olfactory system. To this end, we first synthesize odor stimuli with the primary representations in the olfactory receptor neuron (ORN) layer and the secondary representations in the output of the olfactory bulb (OB) in the model. Next, sparse olfactory codes are extracted and fed into the recurrent network model, where as a result of Hebbian-like associative learning an attractor memory storage is produced. We demonstrate the capability of the resultant olfactory cortex (OC) model to perform robust odor recognition tasks and offer computational insights into the underlying network mechanisms.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017
Keywords
Attractor network, Computational model, Learning, Olfaction, Pattern recognition
National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:kth:diva-218557 (URN)10.1109/IJCNN.2017.7966304 (DOI)000426968703110 ()2-s2.0-85031048062 (Scopus ID)9781509061815 (ISBN)
Conference
2017 International Joint Conference on Neural Networks, IJCNN 2017, Anchorage, United States, 14 May 2017 through 19 May 2017
Note

QC 20171130

Available from: 2017-11-30 Created: 2017-11-30 Last updated: 2018-04-11Bibliographically approved
Ekeberg, Ö., Fransén, E., Hellgren Kotaleski, J., Herman, P., Kumar, A., Lansner, A. & Lindeberg, T. (2016). Computational Brain Science at CST, CSC, KTH. KTH Royal Institute of Technology
Open this publication in new window or tab >>Computational Brain Science at CST, CSC, KTH
Show others...
2016 (English)Other, Policy document (Other academic)
Abstract [en]

Mission and Vision - Computational Brain Science Lab at CST, CSC, KTH

The scientific mission of the Computational Brain Science Lab at CSC is to be at the forefront of mathematical modelling, quantitative analysis and mechanistic understanding of brain function. We perform research on (i) computational modelling of biological brain function and on (ii) developing theory, algorithms and software for building computer systems that can perform brain-like functions. Our research answers scientific questions and develops methods in these fields. We integrate results from our science-driven brain research into our work on brain-like algorithms and likewise use theoretical results about artificial brain-like functions as hypotheses for biological brain research.

Our research on biological brain function includes sensory perception (vision, hearing, olfaction, pain), cognition (action selection, memory, learning) and motor control at different levels of biological detail (molecular, cellular, network) and mathematical/functional description. Methods development for investigating biological brain function and its dynamics as well as dysfunction comprises biomechanical simulation engines for locomotion and voice, machine learning methods for analysing functional brain images, craniofacial morphology and neuronal multi-scale simulations. Projects are conducted in close collaborations with Karolinska Institutet and Karolinska Hospital in Sweden as well as other laboratories in Europe, U.S., Japan and India.

Our research on brain-like computing concerns methods development for perceptual systems that extract information from sensory signals (images, video and audio), analysis of functional brain images and EEG data, learning for autonomous agents as well as development of computational architectures (both software and hardware) for neural information processing. Our brain-inspired approach to computing also applies more generically to other computer science problems such as pattern recognition, data analysis and intelligent systems. Recent industrial collaborations include analysis of patient brain data with MentisCura and the startup company 13 Lab bought by Facebook.

Our long term vision is to contribute to (i) deeper understanding of the computational mechanisms underlying biological brain function and (ii) better theories, methods and algorithms for perceptual and intelligent systems that perform artificial brain-like functions by (iii) performing interdisciplinary and cross-fertilizing research on both biological and artificial brain-like functions. 

On one hand, biological brains provide existence proofs for guiding our research on artificial perceptual and intelligent systems. On the other hand, applying Richard Feynman’s famous statement ”What I cannot create I do not understand” to brain science implies that we can only claim to fully understand the computational mechanisms underlying biological brain function if we can build and implement corresponding computational mechanisms on a computerized system that performs similar brain-like functions.

Place, publisher, year, pages
KTH Royal Institute of Technology, 2016. p. 1
National Category
Computer and Information Sciences Neurosciences
Identifiers
urn:nbn:se:kth:diva-180669 (URN)
Note

QC 20160121

Available from: 2016-01-19 Created: 2016-01-19 Last updated: 2018-01-10Bibliographically approved
Benjaminsson, S., Herman, P. & Lansner, A. (2016). Performance of a computational model of the mammalian olfactory system. In: Neuromorphic Olfaction: (pp. 173-211). CRC Press
Open this publication in new window or tab >>Performance of a computational model of the mammalian olfactory system
2016 (English)In: Neuromorphic Olfaction, CRC Press , 2016, p. 173-211Chapter in book (Other academic)
Place, publisher, year, edition, pages
CRC Press, 2016
National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:kth:diva-236878 (URN)2-s2.0-85052766502 (Scopus ID)9781439871720 (ISBN)9781439871713 (ISBN)
Note

QC 20181213

Available from: 2018-12-13 Created: 2018-12-13 Last updated: 2018-12-13Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-6553-823X

Search in DiVA

Show all publications