40 research outputs found
The Neural Mechanisms Underlying Visual Target Search
The task of finding specific objects and switching between targets is ubiquitous in everyday life. Searching for a particular object requires our brains to activate and maintain a representation of the target (working memory), identify each encountered object (object recognition), and determine whether the currently viewed object matches the sought target (decision making). The comparison of working memory and visual information is thought to happen via feedback of target information from higher-order brain areas to the ventral visual pathway. However, what is exactly represented by these areas and how do they implement this comparison still remains unknown. To investigate these questions, we employed a combined approach involving electrophysiology experiments and computational modeling. In particular, we recorded neural responses in inferotemporal (IT) and perirhinal (PRH) cortex as monkeys performed a visual target search task, and we adopted population-based read-outs to measure the amount and format of information contained in these neural populations. In Chapter 2 we report that the total amount of target match information was matched in IT and PRH, but this information was contained in a more explicit (i.e. linearly separable) format in PRH. These results suggest that PRH implements an untangling computation to reformat its inputs from IT. Consistent with this hypothesis, a simple linear-nonlinear model was sufficient to capture the transformation between the two areas. In Chapter 3, we report that the untangling computation in PRH takes time to evolve. While this type of dynamic reformatting is normally attributed to complex recurrent circuits, here we demonstrated that this phenomenon could be accounted by the same instantaneous linear-nonlinear model presented in Chapter 2. This counterintuitive finding was due to the existence of non-stationarities in the IT neural representation. Finally, in Chapter 4 we completely describe a novel set of methods that we developed and applied in Chapters 2 and 3 to quantify the task-specific signals contained in the heterogeneous neural responses in IT and PRH, and to relate these signals to measures of task performance. Together, this body of work revealed a previously unknown untangling computation in PRH during visual search, and demonstrated that a feed-forward linear-nonlinear model is sufficient to describe this computation
Assessing Credibility In Subjective Probability Judgment
Subjective probability judgments (SPJs) are an essential component of decision making under uncertainty. Yet, research shows that SPJs are vulnerable to a variety of errors and biases. From a practical perspective, this exposes decision makers to risk: if SPJs are (reasonably) valid, then expectations and choices will be rational; if they are not, then expectations may be erroneous and choices suboptimal. However, existing methods for evaluating SPJs depend on information that is typically not available to decision makers (e.g., ground truth; correspondence criteria). To address this issue, I develop a method for evaluating SPJs based on a construct I call credibility. At the conceptual level, credibility describes the relationship between an individual’s SPJs and the most defensible beliefs that one could hold, given all available information. Thus, coefficients describing credibility (i.e., “credibility estimates”) ought to reflect an individual’s tendencies towards error and bias in judgment. To determine whether empirical models of credibility can capture this information, this dissertation examines the reliability, validity, and utility of credibility estimates derived from a model that I call the linear credibility framework. In Chapter 1, I introduce the linear credibility framework and demonstrate its potential for validity and utility in a proof-of-concept simulation. In Chapter 2, I apply the linear credibility framework to SPJs from three empirical sources and examine the reliability and validity of credibility estimates as predictors of judgmental accuracy (among other measures of “good” judgment). In Chapter 3, I use credibility estimates from the same three sources to recalibrate and improve SPJs (i.e., increase accuracy) out-of-sample. In Chapter 4, I discuss the robustness of empirical models of credibility and present two studies in which I use exploratory research methods to (a) tailor the linear credibility framework to the data at hand; and (b) boost performance. Across nine studies, I conclude that the linear credibility framework is a robust (albeit imperfect) model of credibility that can provide reliable, valid, and useful estimates of credibility. Because the linear credibility framework is an intentionally weak model, I argue that these results represent a lower-bound for the performance of empirical models of credibility, more generally
Development of an R package to learn supervised classification techniques
This TFG aims to develop a custom R package for teaching supervised classification algorithms, starting
with the identification of requirements, including algorithms, data structures, and libraries. A strong
theoretical foundation is essential for effective package design. Documentation will explain each function’s
purpose, accompanied by necessary paperwork.
The package will include R scripts and data files in organized directories, complemented by a user
manual for easy installation and usage, even for beginners. Built entirely from scratch without external
dependencies, it’s optimized for accuracy and performance.
In conclusion, this TFG provides a roadmap for creating an R package to teach supervised classification
algorithms, benefiting researchers and practitioners dealing with real-world challenges.Grado en Ingeniería Informátic
Heuristic ensembles of filters for accurate and reliable feature selection
Feature selection has become increasingly important in data mining in recent years. However, the accuracy and stability of feature selection methods vary considerably when used individually, and yet no rule exists to indicate which one should be used for a particular dataset. Thus, an ensemble method that combines the outputs of several individual feature selection methods appears to be a promising approach to address the issue and hence is investigated in this research.
This research aims to develop an effective ensemble that can improve the accuracy and stability of the feature selection. We proposed a novel heuristic ensemble of filters (HEF). It combines two types of filters: subset filters and ranking filters with a heuristic consensus algorithm in order to utilise the strength of each type. The ensemble is tested on ten benchmark datasets and its performance is evaluated by two stability measures and three classifiers. The experimental results demonstrate that HEF improves the stability and accuracy of the selected features and in most cases outperforms the other ensemble algorithms, individual filters and the full feature set.
The research on the HEF algorithm is extended in several dimensions; including more filter members, three novel schemes of mean rank aggregation with partial lists, and three novel schemes for a weighted heuristic ensemble of filters. However, the experimental results demonstrate that adding weight to filters in HEF does not achieve the expected improvement in accuracy, but increases time and space complexity, and clearly decreases stability. Therefore, the core ensemble algorithm (HEF) is demonstrated to be not just simpler but also more reliable and consistent than the later more complicated and weighted ensembles.
In addition, we investigated how to use data in feature selection, using ALL or PART of it. Systematic experiments with thirty five synthetic and benchmark real-world datasets were carried out
Classifier Ensemble Feature Selection for Automatic Fault Diagnosis
"An efficient ensemble feature selection scheme applied for fault diagnosis is
proposed, based on three hypothesis:
a. A fault diagnosis system does not need to be restricted to a single feature
extraction model, on the contrary, it should use as many feature models as
possible, since the extracted features are potentially discriminative and the
feature pooling is subsequently reduced with feature selection;
b. The feature selection process can be accelerated, without loss of classification
performance, combining feature selection methods, in a way that faster and
weaker methods reduce the number of potentially non-discriminative features,
sending to slower and stronger methods a filtered smaller feature set;
c. The optimal feature set for a multi-class problem might be different for each
pair of classes. Therefore, the feature selection should be done using an one
versus one scheme, even when multi-class classifiers are used. However, since
the number of classifiers grows exponentially to the number of the classes,
expensive techniques like Error-Correcting Output Codes (ECOC) might have
a prohibitive computational cost for large datasets. Thus, a fast one versus one
approach must be used to alleviate such a computational demand.
These three hypothesis are corroborated by experiments.
The main hypothesis of this work is that using these three approaches
together is possible to improve significantly the classification performance of a
classifier to identify conditions in industrial processes. Experiments have shown such
an improvement for the 1-NN classifier in industrial processes used as case study.
A Computational Model of Auditory Feature Extraction and Sound Classification
This thesis introduces a computer model that incorporates responses similar to
those found in the cochlea, in sub-corticai auditory processing, and in auditory
cortex. The principle aim of this work is to show that this can form the basis
for a biologically plausible mechanism of auditory stimulus classification. We will
show that this classification is robust to stimulus variation and time compression.
In addition, the response of the system is shown to support multiple, concurrent,
behaviourally relevant classifications of natural stimuli (speech).
The model incorporates transient enhancement, an ensemble of spectro -
temporal filters, and a simple measure analogous to the idea of visual salience
to produce a quasi-static description of the stimulus suitable either for classification
with an analogue artificial neural network or, using appropriate rate coding,
a classifier based on artificial spiking neurons. We also show that the spectotemporal
ensemble can be derived from a limited class of 'formative' stimuli, consistent
with a developmental interpretation of ensemble formation. In addition,
ensembles chosen on information theoretic grounds consist of filters with relatively
simple geometries, which is consistent with reports of responses in mammalian
thalamus and auditory cortex.
A powerful feature of this approach is that the ensemble response, from
which salient auditory events are identified, amounts to stimulus-ensemble driven
method of segmentation which respects the envelope of the stimulus, and leads
to a quasi-static representation of auditory events which is suitable for spike rate
coding.
We also present evidence that the encoded auditory events may form the
basis of a representation-of-similarity, or second order isomorphism, which implies
a representational space that respects similarity relationships between stimuli
including novel stimuli
Critical bistability and large-scale synchrony in human brain dynamics
Neurophysiological dynamics of the brain, overt behaviours, and private experiences of the mind are co-emergent and co-evolving phenomena. An adult human brain contains ~100 billion neurons that are hierarchically organized into intricate networks of functional units comprised of interconnected neurons. It has been hypothesized that neurons within a functional unit communicate with each other or neurons from other units via synchronized activity. At any moment, cascades of synchronized activity from millions of neurons propagate through networks of all sizes, and the levels of synchronization wax and wane. How to understand cognitive functions or diseases from such rich dynamics poses a great challenge. The brain criticality hypothesis proposes that the brain, like many complex systems, optimize its performance by operating near a critical point of phase transition between disorder and order, which suggests complex brain dynamics be effectively studied by combining computational and empirical approaches. Hence, the brain criticality framework requires both classic reductionist and reconstructionist approaches. Reconstructionism in the current context refers to addressing the “Wholeness” of macro-level emergence due to fundamental mechanisms such as synchrony between neurons in the brain. This thesis includes five studies and aims to advance theory, empirical evidence, and methodology in the research of neuronal criticality and large-scale synchrony in the human brain.
Study I: The classic criticality theory is based on the hypothesis that the brain operates near a continuous, second order phase transition between order and disorder in resource-conserving systems. This idea, however, cannot explain why the brain, a non-conserving system, often shows bistability, a hallmark of first order, discontinuous phase transition. We used computational modeling and found that bistability may occur exclusively within the critical regime so that the first-order phase transition emerged progressively with increasing local resource demands. We observed that in human resting-state brain activity, moderate α-band (11 Hz) bistability during rest predicts cognitive performance, but excessive resting-state bistability in fast (> 80 Hz) oscillations characterizes epileptogenic zones in patients’ brain. These findings expand the framework of brain criticality and show that near-critical neuronal dynamics involve both first- and second-order phase transitions in a frequency-, neuroanatomy-, and state-dependent manner.
Study II: Long-range synchrony between cortical oscillations below ~100 Hz is pervasive in brain networks, whereas oscillations and broad-band activities above ~100 Hz have been considered to be strictly local phenomena. We showed with human intracerebral recordings that high-frequency oscillations (HFOs, 100−400 Hz) may be synchronized between brain regions separated by several centimeters. We discovered subject-specific frequency peaks of HFO synchrony and found the group-level HFO synchrony to exhibit laminar-specific connectivity and robust community structures. Importantly, the HFO synchrony was both transiently enhanced and suppressed in separate sub-bands during tasks. These findings showed that HFO synchrony constitutes a functionally significant form of neuronal spike-timing relationships in brain activity and thus a new mesoscopic indication of neuronal communication per se.
Studies III: Signal linear mixing in magneto- (MEG) and electro-encephalography (EEG) artificially introduces linear correlations between sources and confounds the separability of cortical current estimates. This linear mixing effect in turn introduces false positives into synchrony estimates between MEG/EEG sources. Several connectivity metrics have been proposed to supress the linear mixing effects. We show that, although these metrics can remove false positives caused by instantaneous mixing effects, all of them discover false positive ghost interactions (SIs). We also presented major difficulties and technical concerns in mapping brain functional connectivity when using the most popular pairwise correlational metrics.
Study IV and V: We developed a novel approach as a solution to the SIs problem. Our approach is to bundle observed raw edges, i.e., true interactions or SIs, into hyperedges by raw edges’ adjacency in signal mixing. We showed that this bundling approach yields hyperedges with optimal separability between true interactions while suffers little loss in the true positive rate. This bundling approach thus significantly decreases the noise in connectivity graphs by minimizing the false-positive to true-positive ratio. Furthermore, we demonstrated the advantage of hyperedge bundling in visualizing connectivity graphs derived from MEG experimental data. Hence, the hyperedges represent well the true cortical interactions that are detectable and dissociable in MEG/EEG sources.
Taken together, these studies have advanced theory, empirical evidence, and methodology in the research of neuronal criticality and large-scale synchrony in the human brain. Study I provided modeling and empirical evidence for linking bistable criticality and the classic criticality hypothesis into a unified framework. Study II was the first to reveal HFO phase synchrony in large-scale neocortical networks, which was a fundamental discovery of long-range neuronal interactions on fast time-scale per se. Study III raised awareness of the ghost interaction (SI) problem for a critical view on reliable interpretation of MEG/EEG connectivity, and for the development of novel approaches to address the SI problem. Study IV offered a practical solution to the SI problem and opened a new avenue for mapping reliable MEG/EEG connectivity. Study V described the technical details of the hyperedge bundling approach, shared the source code and specified the simulation parameters used in Study IV.Ihmisaivojen neurofysiologinen dynamiikka, ihmisen käyttäytyminen, sekä yksityiset mielen kokemukset syntyvät ja kehittyvät rinnakkaisina ilmiöinä. Ihmisen aivot koostuvat ~100 miljardista hierarkisesti järjestäytyneestä hermosolusta, jotka toisiinsa kytkeytyneinä muodostavat monimutkaisen verkoston toiminnallisia yksiköitä. Hermosolujen aktiivisuuden synkronoitumisen on esitetty mahdollistavan neuronien välisen kommunikoinnin toiminnallisten yksiköiden sisällä sekä niiden välillä. Hetkenä minä hyvänsä, synkronoidun aktiivisuuden kaskadit etenevät aivojen erikokoisissa verkostoissa jatkuvasti heikentyen ja voimistuen. Kognitiivisten funktioiden ja erilaisten aivosairauksien ymmärtäminen tulkitsemalla aivojen rikasta dynamiikkaa on suuri haaste. Kriittiset aivot -hypoteesi ehdottaa aivojen, kuten monien muidenkin kompleksisten systeemien, optimoivan suorituskykyään operoimalla lähellä kriittistä pistettä järjestyksen ja epäjärjestyksen välissä, puoltaen sitä, että aivojen kompleksisia dynamiikoita voitaisiin tutkia yhdistämällä laskennallisia ja empiirisiä lähestymistapoja. Aivojen kriittisyyden viitekehys edellyttää perinteistä reduktionismia ja rekonstruktionismia. Erityisesti, rekonstruktionismi tähtää kuvaamaan aivojen makrotason “yhteneväisyyden” syntymistä perustavanlaatuisten mekaniikoiden, kuten aivojen toiminnallisten yksiköiden välisen synkronian avulla.
Tämä väitöskirja sisältää viisi tutkimusta, jotka edistävät teoriaa, empiirisiä todisteita ja metodologiaa aivojen kriittisyyden ja laajamittaisen synkronian tutkimuksessa.
Tutkimus I tarjosi mallinnuksia ja empiirisiä todisteita bistabiilin kriittisyyden ja klassisen kriittisyyden hypoteesien yhdistämiseksi yhdeksi viitekehykseksi.
Tutkimus II oli ensimmäinen laatuaan paljastaen korkeataajuisten oskillaatioiden (high-frequency oscillation, HFO) vaihesynkronian laajamittaisissa neokortikaalisissa verkostoissa, mikä oli perustavanlaatuinen löytö pitkän matkan neuronaalisista vuorovaikutuksista nopeilla aikaskaaloilla. Tutkimus III lisäsi tietoisuutta aave-vuorovaikutuksien (spurious interactions, SI) ongelmasta MEG/EEG kytkeytyvyyden luotettavassa tulkinnassa sekä uudenlaisten menetelmien kehityksessä SI-ongelman ratkaisemiseksi. Tutkimus IV tarjosi käytännöllisen “hyperedge bundling” -ratkaisun SI-ongelmaan ja avasi uudenlaisen tien luotettavaan MEG/EEG kytkeytyvyyden kartoittamiseen. Tutkimus V kuvasi teknisiä yksityiskohtia hyperedge bundling -menetelmästä, jakoi menetelmän lähdekoodin ja täsmensi tutkimuksessa IV käytettyjä simulaatioparametreja. Yhdessä nämä tutkimukset ovat edistäneet teoriaa, empiirisiä todisteita ja metodologiaa neuronaalisen kriittisyyden sekä laajamittaisen synkronian hyödyntämisessä ihmisaivojen tutkimuksessa
A Bias-Variance Analysis of Bootstrapped Class-Separability Weighting for Error-Correcting Output Code Ensembles
We investigate the effects, in terms of a bias-variance decomposition of error, of applying class-separability weighting plus bootstrapping in the construction of error-correcting output code ensembles of binary classifiers. Evidence is presented to show that bias tends to be reduced at low training strength values whilst variance tends to be reduced across the full range. The relative importance of these effects, however, varies depending on the stability of the base classifier type
A Bias-Variance Analysis of Bootstrapped Class-Separability Weighting for Error-Correcting Output Code Ensembles
We investigate the effects, in terms of a bias-variance decomposition of error, of applying class-separability weighting plus bootstrapping in the construction of error-correcting output code ensembles of binary classifiers. Evidence is presented to show that bias tends to be reduced at low training strength values whilst variance tends to be reduced across the full range. The relative importance of these effects, however, varies depending on the stability of the base classifier type
A Bias-Variance Analysis of Bootstrapped Class-Separability Weighting for Error-Correcting Output Code Ensembles
We investigate the effects, in terms of a bias-variance decomposition of error, of applying class-separability weighting plus bootstrapping in the construction of error-correcting output code ensembles of binary classifiers. Evidence is presented to show that bias tends to be reduced at low training strength values whilst variance tends to be reduced across the full range. The relative importance of these effects, however, varies depending on the stability of the base classifier type