| 1. |
- Lindroos, Robert, 1980-, et al.
(author)
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Perceptual odor qualities predict successful odor identification in old age
- 2022
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In: Chemical Senses. - : Oxford University Press (OUP). - 0379-864X .- 1464-3553. ; 47
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Journal article (peer-reviewed)abstract
- Odor identification is a common assessment of olfaction, and it is affected in a large number of diseases. Identification abilities decline with age, but little is known about whether there are perceptual odor features that can be used to predict identification. Here, we analyzed data from a large, population-based sample of 2,479 adults, aged 60 years or above, from the Swedish National study on Aging and Care in Kungsholmen. Participants performed both free and cued odor identification tests. In a separate experiment, we assessed perceived pleasantness, familiarity, intensity, and edibility of all odors in the first sample, and examined how odor identification performance is associated with these variables. The analysis showed that high-intensity odors are easier to identify than low-intensity odors overall, but also that they are more susceptible to the negative repercussions of old age. This result indicates that sensory decline is a major aspect of age-dependent odor identification impairment, and suggests a framework where identification likelihood is proportional to the perceived intensity of the odor. Additional analyses further showed that high-performing individuals can discriminate target odors from distractors along the pleasantness and edibility dimensions and that unpleasant and inedible odors show smaller age-related differences in identification. Altogether, these results may guide further development and optimization of brief and efficient odor identification tests as well as influence the design of odorous products targeted toward older consumers.
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| 2. |
- Raj, Rohan, 1996-, et al.
(author)
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Odor identification errors reveal cognitive aspects of age-associated smell loss
- 2023
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In: Cognition. - : Elsevier. - 0010-0277 .- 1873-7838. ; 236
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Journal article (peer-reviewed)abstract
- Human olfaction can be extraordinarily sensitive, and its most common assessment method is odor identification (OID), where everyday odors are matched to word labels in a multiple-choice format. However, many older persons are unable to identify familiar odors, a deficit that is associated with the risk of future dementia and mortality. The underlying processes subserving OID in older adults are poorly understood. Here, we analyzed error patterns in OID to test whether errors could be explained by perceptual and/or semantic similarities among the response alternatives. We investigated the OID response patterns in a large, population-based sample of older adults in Sweden (n = 2479; age 60–100 years). Olfaction was assessed by a ‘Sniffin ́ TOM OID test with 16 odors; each trial involved matching a target odor to a correct label among three distractors. We analyzed the pattern of misidentifications, and the results showed that some distractors were more frequently selected than others, suggesting cognitive or perceptual factors may be present. Relatedly, we conducted a large online survey of older adults (n = 959, age 60–90 years) who were asked to imagine and rate the perceptual similarity of the target odors and the three corresponding distractors (e.g. “How similar are these smells: apple and mint?”). We then used data from the Swedish web corpus and the Word2Vec neural network algorithm to quantify the semantic association strength between the labels of each target odor and its three distractors. These data sources were used to predict odor identification errors. We found that the error patterns were partly explained by both the semantic similarity between target-distractor pairs, and the imagined perceptual similarity of the target-distractor pair. Both factors had, however, a diminished prediction in older ages, as responses became gradually less systematic. In sum, our results suggest that OID tests not only reflect olfactory perception, but also likely involve the mental processing of odor-semantic associations. This may be the reason why these tests are useful in predicting dementia onset. Our insights into olfactory-language interactions could be harnessed to develop new olfactory tests that are tailored for specific clinical purposes.
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| 3. |
- Araújo De Medeiros, Daniel
(author)
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Emerging Paradigms in the Convergence of Cloud and High-Performance Computing
- 2023
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Licentiate thesis (other academic/artistic)abstract
- Traditional HPC scientific workloads are tightly coupled, while emerging scientific workflows exhibit even more complex patterns, consisting of multiple characteristically different stages that may be IO-intensive, compute-intensive, or memory-intensive. New high-performance computer systems are evolving to adapt to these new requirements and are motivated by the need for performance and efficiency in resource usage. On the other hand, cloud workloads are loosely coupled, and their systems have matured technologies under different constraints from HPC.In this thesis, the use of cloud technologies designed for loosely coupled dynamic and elastic workloads is explored, repurposed, and examined in the landscape of HPC in three major parts. The first part deals with the deployment of HPC workloads in cloud-native environments through the use of containers and analyses the feasibility and trade-offs of elastic scaling. The second part relates to the use of workflow management systems in HPC workflows; in particular, a molecular docking workflow executed through Airflow is discussed. Finally, object storage systems, a cost-effective and scalable solution widely used in the cloud, and their usage in HPC applications through MPI I/O are discussed in the third part of this thesis.
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| 4. |
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| 5. |
- Brouwer, A.-M., et al.
(author)
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Are you really looking? : Finding the answer through fixation patterns and EEG
- 2009
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In: FOUNDATIONS OF AUGMENTED COGNITION, PROCEEDINGS. - Berlin, Heidelberg : Springer. - 9783642028113 ; , s. 329-338
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Conference paper (peer-reviewed)abstract
- Eye movement recordings do not tell us whether observers are 'really looking' or whether they are paying attention to something else than the visual environment. We want to determine whether an observer's main current occupation is visual or not by investigating fixation patterns and EEG. Subjects were presented with auditory and visual stimuli. In some conditions, they focused on the auditory information whereas in others they searched or judged the visual stimuli. Observers made more fixations that are less cluttered in the visual compared to the auditory tasks, and they were less variable in their average fixation location. Fixated features revealed which target the observers were looking for. Gaze was not attracted more by salient features when performing the auditory task. 8-12 Hz EEG oscillations recorded over the parieto-occipital regions were stronger during the auditory task than during visual search. Our results are directly relevant for monitoring surveillance workers.
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| 6. |
- Chien, Wei Der, et al.
(author)
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NoaSci : A Numerical Object Array Library for I/O of Scientific Applications on Object Storage
- 2022
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In: <em>2022</em> 30th Euromicro International Conference on Parallel, Distributed and Network-Based Processing. - : Institute of Electrical and Electronics Engineers (IEEE).
-
Conference paper (peer-reviewed)abstract
- The strong consistency and stateful workflow are seen as the major factors for limiting parallel I/O performance because of the need for locking and state management. While the POSIX-based I/O model dominates modern HPC storage infrastructure, emerging object storage technology can potentially improve I/O performance by eliminating these bottlenecks.Despite a wide deployment on the cloud, its adoption in HPCremains low. We argue one reason is the lack of a suitable programming interface for parallel I/O in scientific applications. In this work, we introduce NoaSci, a Numerical Object Arraylibrary for scientific applications. NoaSci supports different data formats (e.g. HDF5, binary), and focuses on supporting node-local burst buffers and object stores. We demonstrate for the first time how scientific applications can perform parallel I/Oon Seagate’s Motr object store through NoaSci. We evaluate NoaSci’s preliminary performance using the iPIC3D spaceweather application and position against existing I/O methods.
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| 7. |
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| 8. |
- Chrysanthidis, Nikolaos, et al.
(author)
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Traces of Semantization, from Episodic to Semantic Memory in a Spiking Cortical Network Model
- 2022
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In: eNeuro. - : Society for Neuroscience. - 2373-2822. ; 9:4
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Journal article (peer-reviewed)abstract
- Episodic memory is a recollection of past personal experiences associated with particular times and places. This kind of memory is commonly subject to loss of contextual information or “semantization,” which gradually decouples the encoded memory items from their associated contexts while transforming them into semantic or gist-like representations. Novel extensions to the classical Remember/Know (R/K) behavioral paradigm attribute the loss of episodicity to multiple exposures of an item in different contexts. Despite recent advancements explaining semantization at a behavioral level, the underlying neural mechanisms remain poorly understood. In this study, we suggest and evaluate a novel hypothesis proposing that Bayesian–Hebbian synaptic plasticity mechanisms might cause semantization of episodic memory. We implement a cortical spiking neural network model with a Bayesian–Hebbian learning rule called Bayesian Confidence Propagation Neural Network (BCPNN), which captures the semantization phenomenon and offers a mechanistic explanation for it. Encoding items across multiple contexts leads to item-context decoupling akin to semantization. We compare BCPNN plasticity with the more commonly used spike-timing-dependent plasticity (STDP) learning rule in the same episodic memory task. Unlike BCPNN, STDP does not explain the decontextualization process. We further examine how selective plasticity modulation of isolated salient events may enhance preferential retention and resistance to semantization. Our model reproduces important features of episodicity on behavioral timescales under various biological constraints while also offering a novel neural and synaptic explanation for semantization, thereby casting new light on the interplay between episodic and semantic memory processes.
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| 9. |
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| 10. |
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| 11. |
- Elowsson, Anders
(author)
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Modeling Music : Studies of Music Transcription, Music Perception and Music Production
- 2018
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Doctoral thesis (other academic/artistic)abstract
- This dissertation presents ten studies focusing on three important subfields of music information retrieval (MIR): music transcription (Part A), music perception (Part B), and music production (Part C).In Part A, systems capable of transcribing rhythm and polyphonic pitch are described. The first two publications present methods for tempo estimation and beat tracking. A method is developed for computing the most salient periodicity (the “cepstroid”), and the computed cepstroid is used to guide the machine learning processing. The polyphonic pitch tracking system uses novel pitch-invariant and tone-shift-invariant processing techniques. Furthermore, the neural flux is introduced – a latent feature for onset and offset detection. The transcription systems use a layered learning technique with separate intermediate networks of varying depth. Important music concepts are used as intermediate targets to create a processing chain with high generalization. State-of-the-art performance is reported for all tasks.Part B is devoted to perceptual features of music, which can be used as intermediate targets or as parameters for exploring fundamental music perception mechanisms. Systems are proposed that can predict the perceived speed and performed dynamics of an audio file with high accuracy, using the average ratings from around 20 listeners as ground truths. In Part C, aspects related to music production are explored. The first paper analyzes long-term average spectrum (LTAS) in popular music. A compact equation is derived to describe the mean LTAS of a large dataset, and the variation is visualized. Further analysis shows that the level of the percussion is an important factor for LTAS. The second paper examines songwriting and composition through the development of an algorithmic composer of popular music. Various factors relevant for writing good compositions are encoded, and a listening test employed that shows the validity of the proposed methods.The dissertation is concluded by Part D - Looking Back and Ahead, which acts as a discussion and provides a road-map for future work. The first paper discusses the deep layered learning (DLL) technique, outlining concepts and pointing out a direction for future MIR implementations. It is suggested that DLL can help generalization by enforcing the validity of intermediate representations, and by letting the inferred representations establish disentangled structures supporting high-level invariant processing. The second paper proposes an architecture for tempo-invariant processing of rhythm with convolutional neural networks. Log-frequency representations of rhythm-related activations are suggested at the main stage of processing. Methods relying on magnitude, relative phase, and raw phase information are described for a wide variety of rhythm processing tasks.
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| 12. |
- Entezarjou, Artin, et al.
(author)
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Human- Versus Machine Learning-Based Triage Using Digitalized Patient Histories in Primary Care : Comparative Study
- 2020
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In: JMIR Medical Informatics. - : JMIR Publications Inc.. - 2291-9694. ; 8:9
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Journal article (peer-reviewed)abstract
- Background: Smartphones have made it possible for patients to digitally report symptoms before physical primary care visits. Using machine learning (ML), these data offer an opportunity to support decisions about the appropriate level of care (triage). Objective: The purpose of this study was to explore the interrater reliability between human physicians and an automated ML-based triage method. Methods: After testing several models, a naive Bayes triage model was created using data from digital medical histories, capable of classifying digital medical history reports as either in need of urgent physical examination or not in need of urgent physical examination The model was tested on 300 digital medical history reports and classification was compared with the majority vote of an expert panel of 5 primary care physicians (PCPs). Reliability between raters was measured using both Cohen kappa (adjusted for chance agreement) and percentage agreement (not adjusted for chance agreement). Results: Interrater reliability as measured by Cohen kappa was 0.17 when comparing the majority vote of the reference group with the model. Agreement was 74% (138/186) for cases judged not in need of urgent physical examination and 42% (38/90) for cases judged to be in need of urgent physical examination No specific features linked to the model's triage decision could be identified. Between physicians within the panel, Cohen kappa was 0.2. Intrarater reliability when 1 physician retriaged 50 reports resulted in Cohen kappa of 0.55. Conclusions: Low interrater and intrarater agreement in triage decisions among PCPs limits the possibility to use human decisions as a reference for ML to automate triage in primary care.
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| 13. |
- Fiebig, Florian, 1984-, et al.
(author)
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An Indexing Theory for Working Memory based on Fast Hebbian Plasticity
- 2024
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Other publication (other academic/artistic)abstract
- Working memory (WM) is a key component of human memory and cognitive function. Computational models have been used to uncover the underlying neural mechanisms. However, these studies have mostly focused on the short-term memory aspects of WM and neglected the equally important role of interactions between short- and long-term memory (STM, LTM). Here, we concentrate on these interactions within the framework of our new computational model of WM, which accounts for three cortical patches in macaque brain, corresponding to networks in prefrontal cortex (PFC) together with parieto-temporal cortical areas. In particular, we propose a cortical indexing theory that explains how PFC could associate, maintain and update multi-modal LTM representations. Our simulation results demonstrate how simultaneous, brief multi-modal memory cues could build a temporary joint memory representation linked via an "index" in the prefrontal cortex by means of fast Hebbian synaptic plasticity. The latter can then activate spontaneously and thereby reactivate the associated long-term representations. Cueing one long-term memory item rapidly pattern-completes the associated un-cued item via prefrontal cortex. The STM network updates flexibly as new stimuli arrive thereby gradually over-writing older representations. In a wider context, this WM model suggests a novel explanation for "variable binding", a long-standing and fundamental phenomenon in cognitive neuroscience, which is still poorly understood in terms of detailed neural mechanisms.
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| 14. |
- Fiebig, Florian, et al.
(author)
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An Indexing Theory for Working Memory Based on Fast Hebbian Plasticity
- 2020
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In: ENEURO. - : SOC NEUROSCIENCE. - 2373-2822. ; 7:2
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Journal article (peer-reviewed)abstract
- Working memory (WM) is a key component of human memory and cognition. Computational models have been used to study the underlying neural mechanisms, but neglected the important role of short-term memory (STM) and long-term memory (LTM) interactions for WM. Here, we investigate these using a novel multiarea spiking neural network model of prefrontal cortex (PFC) and two parietotemporal cortical areas based on macaque data. We propose a WM indexing theory that explains how PFC could associate, maintain, and update multimodal LTM representations. Our simulations demonstrate how simultaneous, brief multimodal memory cues could build a temporary joint memory representation as an "index" in PFC by means of fast Hebbian synaptic plasticity. This index can then reactivate spontaneously and thereby also the associated LTM representations. Cueing one LTM item rapidly pattern completes the associated uncued item via PFC. The PFC-STM network updates flexibly as new stimuli arrive, thereby gradually overwriting older representations.
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| 15. |
- Gallinaro, Julia, 1986-
(author)
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Neuronal assembly formation and non-random recurrent connectivity induced by homeostatic structural plasticity
- 2020
-
Doctoral thesis (other academic/artistic)abstract
- Plasticity is usually classified into two distinct categories: Hebbian or homeostatic. Hebbian is driven by correlation in the activity of neurons, while homeostatic relies on a negative feedback signal to control neuronal activity. Since correlated activity leads to strengthened synaptic contacts and formation of cell assemblies, Hebbian plasticity is considered to be the basis of learning and memory. Stronger synapses, on the other hand, promote stronger correlation. This positive feedback loop can lead to instability and homeostatic plasticity is thought to play a role of stabilization. The experimentally observed time scales of homeostatic plasticity, however, are too slow to compensate for the fast Hebbian changes. Therefore, the exact way multiple types of plasticity interact in the brain remains to be elucidated. In this thesis, we will show that homeostatic plasticity can also have interesting effects on network structure. We will show that homeostatic structural plasticity has a Hebbian effect on the network level, and it comprises two separate time scales, a faster for learning and a slower for forgetting. Using a model of classical conditioning task, we will show that this rule can perform pattern completion, and that network response upon stimulation is gradual, reflecting the strength of the memory. Furthermore, we will show that networks grown with homeostatic structural plasticity and a broad distribution of target rates exhibit non-random features similar to some of those found in cortical networks. These include a broad distribution of in- and outdegrees, an over-abundance of bidirectional motifs and scaling of synaptic weights with the number of presynaptic partners. Overall, we will use simulations of spiking neural networks and mathematical tools to show that there is more to homeostatic plasticity than just controlling network stability. It remains an open question, however, the extent to which homeostatic plasticity can be accounted for structural features found in the brain.
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| 16. |
- Glackin, C., et al.
(author)
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A comparison of fuzzy strategies for corporate acquisition analysis
- 2007
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In: Fuzzy sets and systems (Print). - : Elsevier BV. - 0165-0114 .- 1872-6801. ; 158:18, s. 2039-2056
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Journal article (peer-reviewed)abstract
- Analysing all prospective companies for acquisition in large market sectors is an onerous task. A strategy that results in a shortlist of companies that meet certain basic criteria is required. The short-listed companies can then be further investigated in more detail later if desired. Fuzzy logic systems (FLSs) imbued with the expertise of a focal organisation's financial experts can be of great assistance in this process. In this paper an investigation into the suitability of FLSs for acquisition analysis is presented. The nuances of training and tuning are discussed. In particular, the difficulty of obtaining suitable amounts of expert data is a recurring theme throughout the paper. A strategy for circumventing this issue is presented that relies on the design of a conventional fuzzy logic rule base with the assistance of a financial expert. With the rule base created, various scenarios such as the simulation of multiple experts and the creation of expert training data are investigated. In particular, two scenarios for the creation of simulated expert data are presented. In the first the responses from the different experts are averaged, and in the second scenario the responses from all the different experts are preserved in the training data. This paper builds on previous work with scalable membership functions, however, the use of fuzzy C-means clustering and backpropagation training, are new developments. Additionally, a type-2 FLS is developed and its potential advantages are discussed for this application. The type-2 system facilitates the inclusion of the opinions of multiple experts. Both the type-1 and type-2 FLSs were trained using the backpropagation algorithm with early stopping and verified with five-fold cross-validation. Multiple runs of the five-fold method were conducted with different random orderings of the data. For this particular application, the type-1 system performed comparably with the type-2 system despite the considerable amount of variation in the expert training data. The training results have proven the methods to be capable of efficient tuning of parameters, and of reliable ranking of prospective companies.
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| 17. |
- Herman, Pawel Andrzej, 1979-, et al.
(author)
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A Fuzzy Logic Classifier Design for Enhancing BCI Performance
- 2006
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Conference paper (peer-reviewed)abstract
- This work is aimed at enhancing inter-session performance of Brain-Computer Interface (BCI) classification. The effective handling of uncertainties associated with changing brain dynamics is considered to be a key issue. Since fuzzy logic (FL) has been recognized as a functional and well-suited approach to capturing the effects of uncertainty, the research has been concentrated on the development of an FL classifier for a BCI system. The emphasis is placed on type-2 (T2) FL methodology that has recently emerged as an expanded version of classical type-1 (T1) FL. In this work a case study was conducted using ECoG recordings made available as part of BCI competition III. Due to high dimensionality of the signal, two-stage feature selection was devised. The overall performance of the developed BCI was assessed in off-line simulations based on the classification accuracy (CA). Comparative analysis of the designed T2FL and T1FL systems with LDA as BCI classifiers suggests that T2FL has superior capability in effective dealing with inter-session variability of the ECoG dynamics in the given subject.
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| 18. |
- Herman, Pawel Andrzej, 1979-, et al.
(author)
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Comparative analysis of spectral approaches to feature extraction for EEG-based motor imagery classification
- 2008
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In: IEEE transactions on neural systems and rehabilitation engineering. - 1534-4320 .- 1558-0210. ; 16:4, s. 317-326
-
Journal article (peer-reviewed)abstract
- The quantification of the spectral content of electroencephalogram (EEG) recordings has a substantial role in clinical and scientific applications. It is of particular relevance in the analysis of event-related brain oscillatory responses. This work is focused on the identification and quantification of relevant frequency patterns in motor imagery (MI) related EEGs utilized for brain--computer interface (BCI) purposes. The main objective of the paper is to perform comparative analysis of different approaches to spectral signal representation such as power spectral density (PSD) techniques, atomic decompositions, time-frequency (t-f) energy distributions, continuous and discrete wavelet approaches, from which band power features can be extracted and used in the framework of MI classification. The emphasis is on identifying discriminative properties of the feature sets representing EEG trials recorded during imagination of either left-- or right-hand movement. Feature separability is quantified in the offline study using the classification accuracy (CA) rate obtained with linear and nonlinear classifiers. PSD approaches demonstrate the most consistent robustness and effectiveness in extracting the distinctive spectral patterns for accurately discriminating between left and right MI induced EEGs. This observation is based on an analysis of data recorded from eleven subjects over two sessions of BCI experiments. In addition, generalization capabilities of the classifiers reflected in their intersession performance are discussed in the paper..
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| 19. |
- Herman, Pawel Andrzej, 1979-, et al.
(author)
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Critical Observations on Interval Type-2 Fuzzy Logic Approach to Uncertainty Handling in a Brain-Computer Interface Design
- 2006
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In: Proc. IPMU 2006.
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Conference paper (peer-reviewed)abstract
- Effective handling of uncertainties associated with variability in brain dynamics and other factors with stochastic characteristics represents a highly challenging problem particularly for existing methods applied to the classification task within a Brain-Computer Interface (BCI). Recently, type-2 fuzzy logic (T2 FL) has been found effective in modelling uncertain data. This paper presents an enhanced Interval T2 FL methodology to the problem of inter-session classification of movement imagination-related patterns in electroencephalogram (EEG) and electrocorticogram (ECoG) recordings. The performance of the devised BCI is assessed based on the classification accuracy (CA) and is found to compare favourably to that of analogous systems employing well-known classical type-1 (T1) FLS and state-of-the-art linear discriminant analysis (LDA) as classifiers. However, the critical issues concerning learning rate selection, rule-base initialisation, selection of optimal model structure, convergence of model parameters and uncertainty bounds initialisation are observed to have a very decisive effect on the robustness of the designed BCI using T2 methodology. The paper presents some practical approaches to effectively tackle some of the issues and highlights the need for further work so that full potential of T2 FLS concept could be exploited.
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| 20. |
- Herman, Pawel Andrzej, 1979-, et al.
(author)
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Design and on-line evaluation of type-2 fuzzy logic system-based framework for handling uncertainties in BCI classification
- 2008
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In: 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. - 9781424418152 ; , s. 4242-4245
-
Conference paper (peer-reviewed)abstract
- The practical applicability of brain-computer interface (BCI) technology is limited due to its insufficient reliability and robustness. One of the major problems in this regard is the extensive variability and inconsistency of brain signal patterns, observed especially in electroencephalogram (EEG). This paper presents a fuzzy logic (FL) approach to the problem of handling of the resultant uncertainty effects. In particular, it outlines the design of a novel type-2 FL system (T2FLS) classifier within the framework of an EEG-based BCI, and examines its on-line applicability in the presence of shortand long-term nonstationarities of spectral EEG correlates of motor imagery (imagination of left vs. right hand movement). The developed system is shown to effectively cope with realtime constraints. In addition, a comparative post hoc analysis has revealed that the proposed T2FLS classifier outperforms conventional BCI methods, like LDA and SVM, in terms of the maximum classification accuracy (CA) rates by a relatively small, yet statistically significant, margin.
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| 21. |
- Herman, Pawel Andrzej, 1979-, et al.
(author)
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Designing a robust type-2 fuzzy logic classifier for non-stationary systems with application in brain-computer interfacing
- 2008
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In: IEEE SMC 2008. ; , s. 1343-1349
-
Conference paper (peer-reviewed)abstract
- Type-2 (T2) fuzzy logic (FL) systems (T2FLSs) have shown a remarkable potential in dealing with uncertain data resulting from real-world systems with non-stationary characteristics. This paper reports on novel developments in interval T2FLS (IT2FLS) classifier design methodology so that system non-stationarities can be effectively handled. In general, the approach presented here rests on a general concept of twostage FLS design in which an initial rule base structure is first initialized and then system parameters are globally optimized. The proposed incremental enhancements of existing fuzzy techniques, adopted from the area of conventional type-1 (T1) FL, are heuristic in nature. The IT2FLS design methods have been empirically verified in this work in the realm of pattern recognition. In particular, the potential and the suitability of IT2FLS to the problem of classification of motor imagery (MI) related patterns in electroencephalogram (EEG) recordings has been investigated. The outcome of this study bears direct relevance to the development of EEG-based brain-computer interfaces (BCIs) since the problem under examination poses a major difficulty for the state-of-the-art BCI methods. The IT2FLS classifier is evaluated in this work on multi-session EEG data sets in the framework of an off-line BCI. Its performance is quantified in terms of the classification accuracy (CA) rates and has been found to be favorable to that of analogous systems employing a conventional T1 FLS, along with linear discriminant analysis (LDA) and support vector machine (SVM), commonly utilized in MI-based BCI systems.
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| 22. |
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| 23. |
- Herman, Pawel Andrzej, 1979-, et al.
(author)
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Investigation of the Type-2 Fuzzy Logic Approach to Classification in an EEG-based Brain-Computer Interface
- 2005
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In: PROCEEDINGS OF ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ; , s. 5354-5357
-
Conference paper (peer-reviewed)abstract
- Analysis of electroencephalogram (EEG) requires a framework that facilitates handling the uncertainties associated with the varying brain dynamics and the presence of noise. Recently, the type-2 fuzzy logic systems (T2 FLSs) have been found effective in modeling uncertain data. This paper examines the potential of the T2 FLS methodology in devising an EEG-based brain-computer interface (BCI). In particular, a T2 FLS has been designed to classify imaginary left and right hand movements based on time-frequency information extracted from the EEG with the short time Fourier transform (STFT). Robustness of the method has also been verified in the presence of additive noise. The performance of the classifier is quantified with the classification accuracy (CA). The T2 fuzzy classifier has been proven to outperform its type-1 (T1) counterpart on all data sets recorded from three subjects examined. It has also compared favorably to the well known classifier based on linear discriminant analysis (LDA).
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| 24. |
- Herman, Pawel Andrzej, 1979-, et al.
(author)
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Support vector-enhanced design of a T2FL approach to motor imagery-related EEG pattern recognition
- 2007
-
In: IEEE International Conference on Fuzzy Systems. - 1424412102 ; , s. 1938-1943
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Conference paper (peer-reviewed)abstract
- The significance of the initialization procedure in the development of Type-2 fuzzy logic (T2FL) system-based classifiers should be highlighted considering their intrinsically non-linear nature. Initial structure identification has been recognized as a crucial stage in the design of an interval T2FL (IT2FL) classifier utilized in the framework of electroencephalogram (EEG)-based brain - computer interface (BCI). In conjunction with an efficient gradient-based learning algorithm it has allowed for robust exploitation of T2FL's capabilities to effectively handle uncertainties inherently associated with changing dynamics of electrical brain activity. This paper builds on the previous experiences in tackling the problem of inter-session classification of motor imagery (MI)-related EEG patterns. The major contribution of this work is an empirical investigation of the concept of support vector (SV) learning applied to structure identification of the IT2FL classifier. The SV-enhanced initialization scheme is found to compare favorably to both an arbitrary initialization and the clustering approach utilized in the preceding work in terms of the inter-session BCI classification performance of the fully trained IT2FLS evaluated on three subjects.
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| 25. |
- Herman, Pawel, 1979-, et al.
(author)
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Odor recognition in an attractor network model of the mammalian olfactory cortex
- 2017
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In: 2017 International Joint Conference on Neural Networks (IJCNN). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781509061815 ; , s. 3561-3568
-
Conference paper (peer-reviewed)abstract
- 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.
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| 26. |
- Hornung, Maximilian, et al.
(author)
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Detection of Ischemic Infarct Core in Non-contrast Computed Tomography
- 2020
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In: Machine Learning in Medical Imaging. - Cham : Springer Nature. ; , s. 260-269
-
Conference paper (peer-reviewed)abstract
- Fast diagnosis is of critical importance for stroke treatment. In clinical routine, a non-contrast computed tomography scan (NCCT) is typically acquired immediately to determine whether the stroke is ischemic or hemorrhagic and plan therapy accordingly. In case of ischemia, early signs of infarction may appear due to increased water uptake. These signs may be subtle, especially if observed only shortly after symptom onset, but hold the potential to provide a crucial first assessment of the location and extent of the infarction. In this paper, we train a deep neural network to predict the infarct core from NCCT in an image-to-image fashion. To facilitate exploitation of anatomic correspondences, learning is carried out in the standardized coordinate system of a brain atlas to which all images are deformably registered. Apart from binary infarct core masks, perfusion maps such as cerebral blood volume and flow are employed as additional training targets to enrich the physiologic information available to the model. This extension is demonstrated to substantially improve the predictions of our model, which is trained on a data set consisting of 141 cases. It achieves a higher volumetric overlap (statistically significant,) of the predicted core with the reference mask as well as a better localization, although significance could not be shown for the latter. Agreement with human and automatic assessment of affected ASPECTS regions is likewise improved, measured as an increase of the area under the receiver operating characteristic curve from 72.7% to 75.1% and 71.9% to 83.5%, respectively.
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| 27. |
- Iatropoulos, Georgios, et al.
(author)
-
The language of smell : Connecting linguistic and psychophysical properties of odor descriptors
- 2018
-
In: Cognition. - : Elsevier BV. - 0010-0277 .- 1873-7838. ; 178, s. 37-49
-
Journal article (peer-reviewed)abstract
- 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 OM 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 OM). 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.
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| 28. |
- Karvonen, Andrew, et al.
(author)
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The ‘New Urban Science’: towards the interdisciplinary and transdisciplinary pursuit of sustainable transformations
- 2021
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In: Urban Transformations. - : Springer Nature. - 2524-8162. ; 3:1
-
Journal article (peer-reviewed)abstract
- Digitalisation is an increasingly important driver of urban development. The ‘New Urban Science’ is one particular approach to urban digitalisation that promises new ways of knowing and managing cities more effectively. Proponents of the New Urban Science emphasise urban data analytics and modelling as a means to develop novel insights on how cities function. However, there are multiple opportunities to broaden and deepen these practices through collaborations between the natural and social sciences as well as with public authorities, private companies, and civil society. In this article, we summarise the history and critiques of urban science and then call for a New Urban Science that embraces interdisciplinary and transdisciplinary approaches to scientific knowledge production and application. We argue that such an expanded version of the New Urban Science can be used to develop urban transformative capacity and achieve ecologically resilient, economically prosperous, and socially robust cities of the twenty-first century.
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| 29. |
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| 30. |
- Lansner, Anders, Professor, 1949-, et al.
(author)
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Fast Hebbian plasticity and working memory
- 2023
-
In: Current Opinion in Neurobiology. - : Elsevier Ltd. - 0959-4388 .- 1873-6882. ; 83
-
Journal article (peer-reviewed)abstract
- Theories and models of working memory (WM) were at least since the mid-1990s dominated by the persistent activity hypothesis. The past decade has seen rising concerns about the shortcomings of sustained activity as the mechanism for short-term maintenance of WM information in the light of accumulating experimental evidence for so-called activity-silent WM and the fundamental difficulty in explaining robust multi-item WM. In consequence, alternative theories are now explored mostly in the direction of fast synaptic plasticity as the underlying mechanism. The question of non-Hebbian vs Hebbian synaptic plasticity emerges naturally in this context. In this review, we focus on fast Hebbian plasticity and trace the origins of WM theories and models building on this form of associative learning.
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| 31. |
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| 32. |
- Lenninger, Movitz, et al.
(author)
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Are single-peaked tuning curves tuned for speed rather than accuracy?
- 2023
-
In: eLIFE. - : eLife Sciences Publications, Ltd. - 2050-084X. ; 12
-
Journal article (peer-reviewed)abstract
- According to the efficient coding hypothesis, sensory neurons are adapted to provide maximal information about the environment, given some biophysical constraints. In early visual areas, stimulus-induced modulations of neural activity (or tunings) are predominantly single-peaked. However, periodic tuning, as exhibited by grid cells, has been linked to a significant increase in decoding performance. Does this imply that the tuning curves in early visual areas are sub-optimal? We argue that the time scale at which neurons encode information is imperative to understand the advantages of single-peaked and periodic tuning curves, respectively. Here, we show that the possibility of catastrophic (large) errors creates a trade-off between decoding time and decoding ability. We investigate how decoding time and stimulus dimensionality affect the optimal shape of tuning curves for removing catastrophic errors. In particular, we focus on the spatial periods of the tuning curves for a class of circular tuning curves. We show an overall trend for minimal decoding time to increase with increasing Fisher information, implying a trade-off between accuracy and speed. This trade-off is reinforced whenever the stimulus dimensionality is high, or there is ongoing activity. Thus, given constraints on processing speed, we present normative arguments for the existence of the single-peaked tuning organization observed in early visual areas.
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| 33. |
- Lundqvist, Mikael, et al.
(author)
-
Beta : bursts of cognition
- 2024
-
In: Trends in cognitive sciences. - : Elsevier BV. - 1364-6613 .- 1879-307X. ; 28:7, s. 662-676
-
Research review (peer-reviewed)abstract
- Beta oscillations are linked to the control of goal-directed processing of sensory information and the timing of motor output. Recent evidence demonstrates they are not sustained but organized into intermittent high-power bursts mediating timely functional inhibition. This implies there is a considerable moment-tomoment variation in the neural dynamics supporting cognition. Beta bursts thus offer new opportunities for studying how sensory inputs are selectively processed, reshaped by inhibitory cognitive operations and ultimately result in motor actions. Recent method advances reveal diversity in beta bursts that provide deeper insights into their function and the underlying neural circuit activity motifs. We propose that brain-wide, spatiotemporal patterns of beta bursting reflect various cognitive operations and that their dynamics reveal nonlinear aspects of cortical processing.
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| 34. |
- Lundqvist, Mikael, et al.
(author)
-
Gamma and beta bursts during working memory readout suggest roles in its volitional control
- 2018
-
In: Nature Communications. - : NATURE PUBLISHING GROUP. - 2041-1723. ; 9
-
Journal article (peer-reviewed)abstract
- 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.
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| 35. |
- Lundqvist, Mikael, et al.
(author)
-
Reduced variability of bursting activity during working memory
- 2022
-
In: Scientific Reports. - Stockholm : Karolinska Institutet, Dept of Clinical Neuroscience. - 2045-2322.
-
Journal article (peer-reviewed)abstract
- Working memories have long been thought to be maintained by persistent spiking. However, mounting evidence from multiple-electrode recording (and single-trial analyses) shows that the underlying spiking is better characterized by intermittent bursts of activity. A counterargument suggested this intermittent activity is at odds with observations that spike-time variability reduces during task performance. However, this counterargument rests on assumptions, such as randomness in the timing of the bursts, which may not be correct. Thus, we analyzed spiking and LFPs from monkeys' prefrontal cortex (PFC) to determine if task-related reductions in variability can co-exist with intermittent spiking. We found that it does because both spiking and associated gamma bursts were task-modulated, not random. In fact, the task-related reduction in spike variability could largely be explained by a related reduction in gamma burst variability. Our results provide further support for the intermittent activity models of working memory as well as novel mechanistic insights into how spike variability is reduced during cognitive tasks.
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| 36. |
- Lundqvist, Mikael, et al.
(author)
-
Working Memory : Delay Activity, Yes! Persistent Activity? Maybe Not
- 2018
-
In: Journal of Neuroscience. - : Society for Neuroscience. - 0270-6474 .- 1529-2401. ; 38:32, s. 7013-7019
-
Journal article (peer-reviewed)abstract
- 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.
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| 37. |
- Lundqvist, Mikael, et al.
(author)
-
Working memory control dynamics follow principles of spatial computing
- 2023
-
In: Nature communications. - Stockholm : Karolinska Institutet, Dept of Clinical Neuroscience. - 2041-1723.
-
Journal article (peer-reviewed)abstract
- Working memory (WM) allows us to remember and selectively control a limited set of items. Neural evidence suggests it is achieved by interactions between bursts of beta and gamma oscillations. However, it is not clear how oscillations, reflecting coherent activity of millions of neurons, can selectively control individual WM items. Here we propose the novel concept of spatial computing where beta and gamma interactions cause item-specific activity to flow spatially across the network during a task. This way, control-related information such as item order is stored in the spatial activity independent of the detailed recurrent connectivity supporting the item-specific activity itself. The spatial flow is in turn reflected in low-dimensional activity shared by many neurons. We verify these predictions by analyzing local field potentials and neuronal spiking. We hypothesize that spatial computing can facilitate generalization and zero-shot learning by utilizing spatial component as an additional information encoding dimension.
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| 38. |
- Markidis, Stefano, et al.
(author)
-
Automatic Particle Trajectory Classification in Plasma Simulations
- 2020
-
In: 2020 IEEE/ACM workshop on machine learning in high performance computing environments (mlhpc 2020) and workshop on artificial intelligence and machine learning for scientific applications (ai4s 2020). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 64-71
-
Conference paper (peer-reviewed)abstract
- Numerical simulations of plasma flows are crucial for advancing our understanding of microscopic processes that drive the global plasma dynamics in fusion devices, space, and astrophysical systems. Identifying and classifying particle trajectories allows us to determine specific on-going acceleration mechanisms, shedding light on essential plasma processes. Our overall goal is to provide a general workflow for exploring particle trajectory space and automatically classifying particle trajectories from plasma simulations in an unsupervised manner. We combine pre-processing techniques, such as Fast Fourier Transform (FFT), with Machine Learning methods, such as Principal Component Analysis (PCA), k-means clustering algorithms, and silhouette analysis. We demonstrate our workflow by classifying electron trajectories during magnetic reconnection problem. Our method successfully recovers existing results from previous literature without a priori knowledge of the underlying system. Our workflow can be applied to analyzing particle trajectories in different phenomena, from magnetic reconnection, shocks to magnetospheric flows. The workflow has no dependence on any physics model and can identify particle trajectories and acceleration mechanisms that were not detected before.
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| 39. |
- Martinez Mayorquin, Ramon Heberto, et al.
(author)
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Probabilistic associative learning suffices for learning the temporal structure of multiple sequences
- 2019
-
In: PLOS ONE. - : PUBLIC LIBRARY SCIENCE. - 1932-6203. ; 14:8
-
Journal article (peer-reviewed)abstract
- From memorizing a musical tune to navigating a well known route, many of our underlying behaviors have a strong temporal component. While the mechanisms behind the sequential nature of the underlying brain activity are likely multifarious and multi-scale, in this work we attempt to characterize to what degree some of this properties can be explained as a consequence of simple associative learning. To this end, we employ a parsimonious firing-rate attractor network equipped with the Hebbian-like Bayesian Confidence Propagating Neural Network (BCPNN) learning rule relying on synaptic traces with asymmetric temporal characteristics. The proposed network model is able to encode and reproduce temporal aspects of the input, and offers internal control of the recall dynamics by gain modulation. We provide an analytical characterisation of the relationship between the structure of the weight matrix, the dynamical network parameters and the temporal aspects of sequence recall. We also present a computational study of the performance of the system under the effects of noise for an extensive region of the parameter space. Finally, we show how the inclusion of modularity in our network structure facilitates the learning and recall of multiple overlapping sequences even in a noisy regime.
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| 40. |
- Martinez Mayorquin, Ramon Heberto, et al.
(author)
-
Sequence Disambiguation with Synaptic Traces in Associative Neural Networks
- 2019
-
In: 28th International Conference on Artificial Neural Networks, ICANN 2019. - Cham : Springer Nature. ; , s. 793-805
-
Conference paper (peer-reviewed)abstract
- Among the abilities that a sequence processing network should possess sequence disambiguation, that is, the ability to let temporal context information influence the evolution of the network dynamics, is one of the most important. In this work we propose an instance of the Bayesian Confidence Propagation Neural Network (BCPNN) that learns sequences with probabilistic associative learning and is able to disambiguate sequences with the use of synaptic traces (low pass filtered versions of the activity). We describe first how the BCPNN achieves both sequence recall and sequence learning from temporal input. Our main result is that the BCPNN network equipped with dynamical memory in the form of synaptic traces is capable of solving the sequence disambiguation problem in a reliable way. We characterize the relationship between the sequence disambiguation capabilities of the network and its dynamical parameters. Furthermore, we show that the inclusion of an additional fast synaptic trace greatly increases the network disambiguation capabilities.
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| 41. |
- Martinez Mayorquin, Ramon Heberto
(author)
-
Sequence learning in the Bayesian Confidence Propagation Neural Network
- 2022
-
Doctoral thesis (other academic/artistic)abstract
- This thesis examines sequence learning in the Bayesian Confidence PropagationNeural Network (BCPNN). The methodology utilized throughout this work is com-putational and analytical in nature and the contributions here presented can beunderstood along the following four major themes: 1) this work starts by revisitingthe properties of the BCPNN as an attractor neural network and then provides anovel formalization of some of those properties. First, a bayesian theoretical frame-work for the lower bounds in the BCPNN. Second, a differential formulation ofthe BCPNN plasticity rule that highlights its relationship to similar rules in thelearning literature. Third, closed form analytical results for the BCPNN trainingprocess. 2) After that, this work describes how the addition of an adaptation processto the BCPNN enables its sequence recall capabilities. The specific mechanisms ofsequence learning are then studied in detail as well as the properties of sequencerecall such as the persistence time (how long does the network last in a specific stateduring sequence recall) and its robustness to noise. 3) This work also shows howthe BCPNN can be enhanced with memory traces of the activity (z-traces) to pro-vide the network with disambiguation capabilities. 4) Finally, this works provides acomputational study to quantify the number of the sequences that the BCPNN canstore successfully. Alongside these central themes, results concerning robustness,stability and the relationship between the learned patterns and the input statisticsare presented in either computational or analytical form. The thesis concludes witha discussion of the sequence learning capabilities of the BCPNN in the context of thewider literature and describes both his advantages and disadvantages with respectto other attractor neural networks.
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| 42. |
- Mbuvha, R., et al.
(author)
-
Bayesian neural networks for one-hour ahead wind power forecasting
- 2017
-
In: 2017 6th International Conference on Renewable Energy Research and Applications, ICRERA 2017. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781538620953 ; , s. 591-596
-
Conference paper (peer-reviewed)abstract
- 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).
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| 43. |
- Olshevsky, Viacheslav, et al.
(author)
-
Automated Classification of Plasma Regions Using 3D Particle Energy Distributions
- 2021
-
In: Journal of Geophysical Research - Space Physics. - : American Geophysical Union (AGU). - 2169-9380 .- 2169-9402. ; 126:10
-
Journal article (peer-reviewed)abstract
- We investigate the properties of the ion sky maps produced by the Dual Ion Spectrometers (DIS) from the Fast Plasma Investigation (FPI). We have trained a convolutional neural network classifier to predict four regions crossed by the Magnetospheric Multiscale Mission (MMS) on the dayside magnetosphere: solar wind, ion foreshock, magnetosheath, and magnetopause using solely DIS spectrograms. The accuracy of the classifier is >98%. We use the classifier to detect mixed plasma regions, in particular to find the bow shock regions. A similar approach can be used to identify the magnetopause crossings and reveal regions prone to magnetic reconnection. Data processing through the trained classifier is fast and efficient and thus can be used for classification for the whole MMS database.
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| 44. |
- Pereira, Patricia, et al.
(author)
-
Incremental Attractor Neural Network Modelling of the Lifespan Retrieval Curve
- 2022
-
In: 2022 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN). - : Institute of Electrical and Electronics Engineers (IEEE).
-
Conference paper (peer-reviewed)abstract
- The human lifespan retrieval curve describes the proportion of recalled memories from each year of life. It exhibits a reminiscence bump - a tendency for aged people to better recall memories formed during their young adulthood than from other periods of life. We have modelled this using an attractor Bayesian Confidence Propagation Neural Network (BCPNN) with incremental learning. We systematically studied the synaptic mechanisms underlying the reminiscence bump in this network model after introduction of an exponential decay of the synaptic learning rate and examined its sensitivity to network size and other relevant modelling mechanisms. The most influential parameters turned out to be the synaptic learning rate at birth and the time constant of its exponential decay with age, which set the bump position in the lifespan retrieval curve. The other parameters mainly influenced the general magnitude of this curve. Furthermore, we introduced the parametrization of the recency phenomenon - the tendency to better remember the most recent memories - reflected in the curve's upwards tail in the later years of the lifespan. Such recency was achieved by adding a constant baseline component to the exponentially decaying synaptic learning rate.
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| 45. |
- Podobas, Artur, et al.
(author)
-
StreamBrain : An HPC Framework for Brain-like Neural Networks on CPUs, GPUs and FPGAs
- 2021
-
In: ACM International Conference Proceeding Series. - New York, NY, USA : Association for Computing Machinery (ACM).
-
Conference paper (peer-reviewed)abstract
- The modern deep learning method based on backpropagation has surged in popularity and has been used in multiple domains and application areas. At the same time, there are other - less-known - machine learning algorithms with a mature and solid theoretical foundation whose performance remains unexplored. One such example is the brain-like Bayesian Confidence Propagation Neural Network (BCPNN). In this paper, we introduce StreamBrain - a framework that allows neural networks based on BCPNN to be practically deployed in High-Performance Computing systems. StreamBrain is a domain-specific language (DSL), similar in concept to existing machine learning (ML) frameworks, and supports backends for CPUs, GPUs, and even FPGAs. We empirically demonstrate that StreamBrain can train the well-known ML benchmark dataset MNIST within seconds, and we are the first to demonstrate BCPNN on STL-10 size networks. We also show how StreamBrain can be used to train with custom floating-point formats and illustrate the impact of using different bfloat variations on BCPNN using FPGAs.
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| 46. |
- Prasad, G., et al.
(author)
-
Applying a brain-computer interface to support motor imagery practice in people with stroke for upper limb recovery : A feasibility study
- 2010
-
In: Journal of NeuroEngineering and Rehabilitation. - : Springer Science and Business Media LLC. - 1743-0003. ; 7:1, s. 60-
-
Journal article (peer-reviewed)abstract
- There is now sufficient evidence that using a rehabilitation protocol involving motor imagery (MI) practice in conjunction with physical practice (PP) of goal-directed rehabilitation tasks leads to enhanced functional recovery of paralyzed limbs among stroke sufferers. It is however difficult to confirm patient engagement during an MI in the absence of any on-line measure. Fortunately an EEG-based brain-computer interface (BCI) can provide an on-line measure of MI activity as a neurofeedback for the BCI user to help him/her focus better on the MI task. However initial performance of novice BCI users may be quite moderate and may cause frustration. This paper reports a pilot study in which a BCI system is used to provide a computer game-based neurofeedback to stroke participants during the MI part of a protocol. Methods. The participants included five chronic hemiplegic stroke sufferers. Participants received up to twelve 30-minute MI practice sessions (in conjunction with PP sessions of the same duration) on 2 days a week for 6 weeks. The BCI neurofeedback performance was evaluated based on the MI task classification accuracy (CA) rate. A set of outcome measures including action research arm test (ARAT) and grip strength (GS), was made use of in assessing the upper limb functional recovery. In addition, since stroke sufferers often experience physical tiredness, which may influence the protocol effectiveness, their fatigue and mood levels were assessed regularly. Results. Positive improvement in at least one of the outcome measures was observed in all the participants, while improvements approached a minimal clinically important difference (MCID) for the ARAT. The on-line CA of MI induced sensorimotor rhythm (SMR) modulation patterns in the form of lateralized event-related desynchronization (ERD) and event-related synchronization (ERS) effects, for novice participants was in a moderate range of 60-75% within the limited 12 training sessions. The ERD/ERS change from the first to the last session was statistically significant for only two participants. Conclusions. Overall the crucial observation is that the moderate BCI classification performance did not impede the positive rehabilitation trends as quantified with the rehabilitation outcome measures adopted in this study. Therefore it can be concluded that the BCI supported MI is a feasible intervention as part of a post-stroke rehabilitation protocol combining both PP and MI practice of rehabilitation tasks. Although these findings are promising, the scope of the final conclusions is limited by the small sample size and the lack of a control group.
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| 47. |
- Prasad, G., et al.
(author)
-
Using motor imagery based brain-computer interface for post-stroke rehabilitation
- 2009
-
In: 2009 4TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING. - 9781424420735 ; , s. 251-255
-
Conference paper (peer-reviewed)abstract
- There is now sufficient evidence that using a rehabilitation protocol involving motor imagery (MI) practice (or mental practice (MP)) in conjunction with physical practice (PP) of goal-directed rehabilitation tasks leads to enhanced functional recovery of paralyzed limbs among stroke sufferers. It is however difficult to ensure patient engagement during MP in the absence of any on-line measure of the MP. Fortunately in an EEG-based brain-computer interface (BCI), an on-line measure of MI activity is used to devise neurofeedback for the BCI user to help him/her focus better on the task. This paper reports a pilot study in which an EEG-based BCI system is used to provide neurofeedback to stroke participants during the MP part of the rehabilitation protocol. This helps patients to undertake the MP with stronger focus. The participants included five chronic stroke sufferers. The trial was undertaken for 12 sessions over a period of 6 weeks. A set of rehabilitation outcome measures including action research arm test (ARAT) and motricity index was made use of in assessing functional recovery. Moderate improvements approaching a minimal clinically important difference (MCID) were observed for the ARAT. Small positive improvements were also observed in other outcome measures. Participants appeared highly enthusiastic about participating in the study and regularly attended all the sessions. Although without a randomized control trial, it is difficult to ascertain whether the enhanced rehabilitation gain is primarily because of BCI neurofeedack, the positive gains in outcome measures demonstrate the potential and feasibility of using BCI for post-stroke rehabilitation.
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| 48. |
- Ravichandran, Naresh Balaji, et al.
(author)
-
Brain-Like Approaches to Unsupervised Learning of Hidden Representations - A Comparative Study
- 2021
-
In: Artificial Neural Networks And Machine Learning, ICANN 2021, Pt V. - Cham : Springer Nature. ; , s. 162-173
-
Conference paper (peer-reviewed)abstract
- Unsupervised learning of hidden representations has been one of the most vibrant research directions in machine learning in recent years. In this work we study the brain-like Bayesian Confidence Propagating Neural Network (BCPNN) model, recently extended to extract sparse distributed high-dimensional representations. The usefulness and class-dependent separability of the hidden representations when trained on MNIST and Fashion-MNIST datasets is studied using an external linear classifier and compared with other unsupervised learning methods that include restricted Boltzmann machines and autoencoders.
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| 49. |
- Ravichandran, Naresh Balaji, et al.
(author)
-
Brain-like Combination of Feedforward and Recurrent Network Components Achieves Prototype Extraction and Robust Pattern Recognition
- 2023
-
In: Lecture Notes in Computer Science. - Cham : Springer Nature. ; , s. 488-501
-
Conference paper (peer-reviewed)abstract
- Associative memory has been a prominent candidate for the computation performed by the massively recurrent neocortical networks. Attractor networks implementing associative memory have offered mechanistic explanation for many cognitive phenomena. However, attractor memory models are typically trained using orthogonal or random patterns to avoid interference between memories, which makes them unfeasible for naturally occurring complex correlated stimuli like images. We approach this problem by combining a recurrent attractor network with a feedforward network that learns distributed representations using an unsupervised Hebbian-Bayesian learning rule. The resulting network model incorporates many known biological properties: unsupervised learning, Hebbian plasticity, sparse distributed activations, sparse connectivity, columnar and laminar cortical architecture, etc. We evaluate the synergistic effects of the feedforward and recurrent network components in complex pattern recognition tasks on the MNIST handwritten digits dataset. We demonstrate that the recurrent attractor component implements associative memory when trained on the feedforward-driven internal (hidden) representations. The associative memory is also shown to perform prototype extraction from the training data and make the representations robust to severely distorted input. We argue that several aspects of the proposed integration of feedforward and recurrent computations are particularly attractive from a machine learning perspective.
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| 50. |
- Ravichandran, Naresh Balaji, et al.
(author)
-
Learning representations in Bayesian Confidence Propagation neural networks
- 2020
-
In: 2020 International joint conference on neural networks (IJCNN). - : IEEE.
-
Conference paper (peer-reviewed)abstract
- Unsupervised learning of hierarchical representations has been one of the most vibrant research directions in deep learning during recent years. In this work we study biologically inspired unsupervised strategies in neural networks based on local Hebbian learning. We propose new mechanisms to extend the Bayesian Confidence Propagating Neural Network (BCPNN) architecture, and demonstrate their capability for unsupervised learning of salient hidden representations when tested on the MNIST dataset.
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