Deep Transfer Learning for Automatic Speech Recognition: Towards Better Generalization

Automatic speech recognition (ASR) has recently become an important challenge when using deep learning (DL). It requires large-scale training datasets and high computational and storage resources. Moreover, DL techniques and machine learning (ML) approaches in general, hypothesize that training and testing data come from the same domain, with the same input feature space and data distribution characteristics. This assumption, however, is not applicable in some real-world artificial intelligence (AI) applications. Moreover, there are situations where gathering real data is challenging, expensive, or rarely occurring, which can not meet the data requirements of DL models. deep transfer learning (DTL) has been introduced to overcome these issues, which helps develop high-performing models using real datasets that are small or slightly different but related to the training data. This paper presents a comprehensive survey of DTL-based ASR frameworks to shed light on the latest developments and helps academics and professionals understand current challenges. Specifically, after presenting the DTL background, a well-designed taxonomy is adopted to inform the state-of-the-art. A critical analysis is then conducted to identify the limitations and advantages of each framework. Moving on, a comparative study is introduced to highlight the current challenges before deriving opportunities for future research

Text-Independent Voice Conversion

This thesis deals with text-independent solutions for voice conversion. It first introduces the use of vocal tract length normalization (VTLN) for voice conversion. The presented variants of VTLN allow for easily changing speaker characteristics by means of a few trainable parameters. Furthermore, it is shown how VTLN can be expressed in time domain strongly reducing the computational costs while keeping a high speech quality. The second text-independent voice conversion paradigm is residual prediction. In particular, two proposed techniques, residual smoothing and the application of unit selection, result in essential improvement of both speech quality and voice similarity. In order to apply the well-studied linear transformation paradigm to text-independent voice conversion, two text-independent speech alignment techniques are introduced. One is based on automatic segmentation and mapping of artificial phonetic classes and the other is a completely data-driven approach with unit selection. The latter achieves a performance very similar to the conventional text-dependent approach in terms of speech quality and similarity. It is also successfully applied to cross-language voice conversion. The investigations of this thesis are based on several corpora of three different languages, i.e., English, Spanish, and German. Results are also presented from the multilingual voice conversion evaluation in the framework of the international speech-to-speech translation project TC-Star

Machine Learning in Resource-constrained Devices: Algorithms, Strategies, and Applications

The ever-increasing growth of technologies is changing people's everyday life. As a major consequence: 1) the amount of available data is growing and 2) several applications rely on battery supplied devices that are required to process data in real time. In this scenario the need for ad-hoc strategies for the development of low-power and low-latency intelligent systems capable of learning inductive rules from data using a modest mount of computational resources is becoming vital. At the same time, one needs to develop specic methodologies to manage complex patterns such as text and images. This Thesis presents different approaches and techniques for the development of fast learning models explicitly designed to be hosted on embedded systems. The proposed methods proved able to achieve state-of-the-art performances in term of the trade-off between generalization capabilities and area requirements when implemented in low-cost digital devices. In addition, advanced strategies for ecient sentiment analysis in text and images are proposed

VaxInsight: an artificial intelligence system to access large-scale public perceptions of vaccination from social media

Vaccination is considered one of the greatest public health achievements of the 20th century. A high vaccination rate is required to reduce the prevalence and incidence of vaccine-preventable diseases. However, in the last two decades, there has been a significant and increasing number of people who refuse or delay getting vaccinated and who prohibit their children from receiving vaccinations. Importantly, under-vaccination is associated with infectious disease outbreaks. A good understanding of public perceptions regarding vaccinations is important if we are to develop effective vaccination promotion strategies. Traditional methods of research, such as surveys, suffer limitations that impede our understanding of public perceptions, including resources cost, delays in data collection and analysis, especially in large samples. The popularity of social media (e.g. Twitter), combined with advances in artificial intelligence algorithms (e.g. natural language processing, deep learning), open up new avenues for accessing large scale data on public perceptions related to vaccinations. This dissertation reports on an original and systematic effort to develop artificial intelligence algorithms that will increase our ability to use Twitter discussions to understand vaccine-related perceptions and intentions. The research is framed within the perspectives offered by grounded behavior change theories. Tweets concerning the human papillomavirus (HPV) vaccine were used to accomplish three major aims: 1) Develop a deep learning-based system to better understand public perceptions of the HPV vaccine, using Twitter data and behavior change theories; 2) Develop a deep learning-based system to infer Twitter users’ demographic characteristics (e.g. gender and home location) and investigate demographic differences in public perceptions of the HPV vaccine; 3) Develop a web-based interactive visualization system to monitor real-time Twitter discussions of the HPV vaccine. For Aim 1, the bi-directional long short-term memory (LSTM) network with attention mechanism outperformed traditional machine learning and competitive deep learning algorithms in mapping Twitter discussions to the theoretical constructs of behavior change theories. Domain-specific embedding trained on HPV vaccine-related Twitter corpus by fastText algorithms further improved performance on some tasks. Time series analyses revealed evolving trends of public perceptions regarding the HPV vaccine. For Aim 2, the character-based convolutional neural network model achieved favorable state-of-the-art performance in Twitter gender inference on a Public Author Profiling challenge. The trained models then were applied to the Twitter corpus and they identified gender differences in public perceptions of the HPV vaccine. The findings on gender differences were largely consistent with previous survey-based studies. For the Twitter users’ home location inference, geo-tagging was framed as text classification tasks that resulted in a character-based recurrent neural network model. The model outperformed machine learning and deep learning baselines on home location tagging. Interstate variations in public perceptions of the HPV vaccine also were identified. For Aim 3, a prototype web-based interactive dashboard, VaxInsight, was built to synthesize HPV vaccine-related Twitter discussions in a comprehendible format. The usability test of VaxInsight showed high usability of the system. Notably, this maybe the first study to use deep learning algorithms to understand Twitter discussions of the HPV vaccine within the perspective of grounded behavior change theories. VaxInsight is also the first system that allows users to explore public health beliefs of vaccine related topics from Twitter. Thus, the present research makes original and systematical contributions to medical informatics by combining cutting-edge artificial intelligence algorithms and grounded behavior change theories. This work also builds a foundation for the next generation of real-time public health surveillance and research

An enhanced binary bat and Markov clustering algorithms to improve event detection for heterogeneous news text documents

Event Detection (ED) works on identifying events from various types of data. Building an ED model for news text documents greatly helps decision-makers in various disciplines in improving their strategies. However, identifying and summarizing events from such data is a non-trivial task due to the large volume of published heterogeneous news text documents. Such documents create a high-dimensional feature space that influences the overall performance of the baseline methods in ED model. To address such a problem, this research presents an enhanced ED model that includes improved methods for the crucial phases of the ED model such as Feature Selection (FS), ED, and summarization. This work focuses on the FS problem by automatically detecting events through a novel wrapper FS method based on Adapted Binary Bat Algorithm (ABBA) and Adapted Markov Clustering Algorithm (AMCL), termed ABBA-AMCL. These adaptive techniques were developed to overcome the premature convergence in BBA and fast convergence rate in MCL. Furthermore, this study proposes four summarizing methods to generate informative summaries. The enhanced ED model was tested on 10 benchmark datasets and 2 Facebook news datasets. The effectiveness of ABBA-AMCL was compared to 8 FS methods based on meta-heuristic algorithms and 6 graph-based ED methods. The empirical and statistical results proved that ABBAAMCL surpassed other methods on most datasets. The key representative features demonstrated that ABBA-AMCL method successfully detects real-world events from Facebook news datasets with 0.96 Precision and 1 Recall for dataset 11, while for dataset 12, the Precision is 1 and Recall is 0.76. To conclude, the novel ABBA-AMCL presented in this research has successfully bridged the research gap and resolved the curse of high dimensionality feature space for heterogeneous news text documents. Hence, the enhanced ED model can organize news documents into distinct events and provide policymakers with valuable information for decision making

A Comprehensive Exploration of Personalized Learning in Smart Education: From Student Modeling to Personalized Recommendations

With the development of artificial intelligence, personalized learning has attracted much attention as an integral part of intelligent education. China, the United States, the European Union, and others have put forward the importance of personalized learning in recent years, emphasizing the realization of the organic combination of large-scale education and personalized training. The development of a personalized learning system oriented to learners' preferences and suited to learners' needs should be accelerated. This review provides a comprehensive analysis of the current situation of personalized learning and its key role in education. It discusses the research on personalized learning from multiple perspectives, combining definitions, goals, and related educational theories to provide an in-depth understanding of personalized learning from an educational perspective, analyzing the implications of different theories on personalized learning, and highlighting the potential of personalized learning to meet the needs of individuals and to enhance their abilities. Data applications and assessment indicators in personalized learning are described in detail, providing a solid data foundation and evaluation system for subsequent research. Meanwhile, we start from both student modeling and recommendation algorithms and deeply analyze the cognitive and non-cognitive perspectives and the contribution of personalized recommendations to personalized learning. Finally, we explore the challenges and future trajectories of personalized learning. This review provides a multidimensional analysis of personalized learning through a more comprehensive study, providing academics and practitioners with cutting-edge explorations to promote continuous progress in the field of personalized learning.Comment: 82 pages,5 figure

Executive function & semantic memory impairments in Alzheimer’s disease — investigating the decline of executive function and semantic memory in Alzheimer’s disease through computer-supported qualitative analysis of semantic verbal fluency and its applications in clinical decision support

Alzheimer’s Disease (AD) has a huge impact on an ever-aging society in highly developed industrialized countries such as the EU member states: according to the World Alzheimer’s Association the number one risk factor for AD is age. AD patients suffer from neurodegenerative processes driving cognitive decline which eventually results in the loss of patients’ ability of independent living. Episodic memory impairment is the most prominent cognitive symptom of AD in its clinical stage. In addition, also executive function and semantic memory impairments significantly affect activities of daily living and are discussed as important cognitive symptoms during prodromal as well as acute clinical stages of AD. Most of the research on semantic memory impairments in AD draws evidence from the Semantic Verbal Fluency (SVF) task which evidentially also places high demands on the executive function level. At the same time, the SVF is one of the most-applied routine assessments in clinical neuropsychology especially in the diagnosis of AD. Therefore, the SVF is a prime task to study semantic memory and executive function impairment side-by-side and draw conclusions about their parallel or successive impairments across the clinical trajectory of AD. To effectively investigate semantic memory and executive function processes in the SVF, novel computational measures have been proposed that tap into data-driven semantic as well as temporal metrics scoring an SVF performance on the item-level. With a better and more differentiated understanding of AD-related executive function and semantic memory impairments in the SVF, the SVF can grow from a well-established screening into a more precise diagnostic tool for early AD. As the SVF is one of the most-applied easy-to-use and low-burden neurocognitive assessments in AD, such advancements have a direct impact on clinical practice as well. For the last decades huge efforts have been put on the discovery of disease-modifying compounds responding to specific AD biomarker-related cognitive decline characteristics. However, as most pharmaceutical trials failed, the focus has shifted towards population-wide early screening with cost-effective and scalable cognitive tests representing an effective mid-term strategy. Computer-supported SVF analysis responds to this demand. This thesis pursues a two-fold objective: (1) improve our understanding of the progressive executive function and semantic memory impairments and their interplay in clinical AD as measured by the SVF and (2) harness those insights for applied early and specific AD screening. To achieve both objectives, this thesis comprises work on subjects from different clinical stages of AD (Healthy Aging, amnestic Mild Cognitive Impairment—aMCI, and AD dementia) and in different languages (German & French). All results are based on SVF speech data generated either as a one-time assessment or a repeated within-participant testing. From these SVF speech samples, qualitative markers are extracted with different amount of computational support (ranging from manual processing of speech to fully automated evaluation). The results indicate, that semantic memory is structurally affected from an early clinical—amnestic Mild Cognitive Impairment (aMCI)—stage on and is even more affected in the later acute dementia stage. The semantic memory impairment in AD is particularly worsened through the patients’ inability to compensate by engaging executive functions. Hence, over the course of the disease, hampered executive functioning and therefore the inability to compensate for corrupt semantic memory structures might be the main driver of later-stage AD patients’ notably poor cognitive performance. These insights generated on the SVF alone are only made possible through computer-supported qualitative analysis on an item-per-item level which leads the way towards potential applications in clinical decision support. The more fine-grained qualitative analysis of the SVF is clinically valuable for AD diagnosis and screening but very time-consuming if performed manually. This thesis shows though that automatic analysis pipelines can reliably and validly generate this diagnostic information from the SVF. Automatic transcription of speech plus automatic extraction of the novel qualitative SVF features result in clinical interpretation comparable to manual transcripts and improved diagnostic decision support simulated through machine learning classification experiments. This indicates that the computer-supported SVF could ultimately be used for cost-effective fully automated early clinical AD screening. This thesis advances current AD research in a two-fold manner. First it improves the understanding of the decline of executive function and semantic memory in AD as measured through computational qualitative analysis of the SVF. Secondly, this thesis embeds these theoretical advances into practical clinical decision support concepts that help screen population-wide and cost-effective for early-stage AD.Die Alzheimer-Krankheit (AD) stellt eine enorme Herausforderung für die immer älter werdende Gesellschaft in hochentwickelten Industrieländern wie den EU-Mitgliedsstaaten dar. Nach Angaben der World Alzheimer's Association ist der größte Risikofaktor für AD das Alter. Alzheimer-Patienten leiden unter neurodegenerativen Prozessen, die kognitiven Abbau verursachen und schließlich dazu führen, dass Patienten nicht länger selbstbestimmt leben können. Die Beeinträchtigung des episodischen Gedächtnisses ist das prominenteste kognitive Symptom von AD im klinischen Stadium. Darüber hinaus führen auch Störungen der Exekutivfunktionen sowie der semantischen Gedächtnisleistung zu erheblichen Einschränkungen bei Aktivitäten des täglichen Lebens und werden als wichtige kognitive Symptome sowohl im Prodromal- als auch im akuten klinischen Stadium von AD diskutiert. Der Großteil der Forschung zu semantischen Gedächtnisbeeinträchtigungen bei AD stützt sich auf Ergebnisse aus dem Semantic Verbal Fluency Tests (SVF), der auch die Exekutivfunktionen stark fordert. In der Praxis ist die SVF eines der am häufigsten eingesetzten Routine- Assessments in der klinischen Neuropsychologie, insbesondere bei der Diagnose von AD. Daher ist die SVF eine erstklassige Aufgabe, um die Beeinträchtigung des semantischen Gedächtnisses und der exekutiven Funktionen Seite an Seite zu untersuchen und Rückschlüsse auf ihre parallelen oder sukzessiven Beeinträchtigungen im klinischen Verlauf von AD zu ziehen. Um semantische Gedächtnis- und Exekutivfunktionsprozesse in der SVF effektiv zu untersuchen, wurden jüngst neuartige computergestützte Verfahren vorgeschlagen, die sowohl datengetriebene semantische als auch temporäre Maße nutzen, die eine SVF-Leistung auf Item-Ebene bewerten. Mit einem besseren und differenzierteren Verständnis von ADbedingten Beeinträchtigungen der Exekutivfunktionen und des semantischen Gedächtnisses in der SVF kann sich die SVF von einem gut etablierten Screening zu einem präziseren Diagnoseinstrument für frühe AD entwickeln. Da die SVF eines der am häufigsten angewandten, einfach zu handhabenden und wenig belastenden neurokognitiven Assessments bei AD ist, haben solche Fortschritte auch einen direkten Einfluss auf die klinische Praxis. In den letzten Jahrzehnten wurden enorme Anstrengungen unternommen, um krankheitsmodifizierende Substanzen zu finden, die auf spezifische, mit AD-Biomarkern verbundene Merkmale des kognitiven Abbaus reagieren. Da jedoch die meisten pharmazeutischen Studien in jüngster Vergangenheit fehlgeschlagen sind, wird heute als mittelfristige Strategie bevölkerungsweite Früherkennung mit kostengünstigen und skalierbaren kognitiven Tests gefordert. Die computergestützte SVF-Analyse ist eine Antwort auf diese Forderung. Diese Arbeit verfolgt deshalb zwei Ziele: (1) Verbesserung des Verständnisses der fortschreitenden Beeinträchtigungen der Exekutivfunktionen und des semantischen Gedächtnisses und ihres Zusammenspiels bei klinischer AD, gemessen durch die SVF, und (2) Nutzung dieser Erkenntnisse für angewandte AD-Früherkennung. Um beide Ziele zu erreichen, umfasst diese Thesis Forschung mit Probanden aus verschiedenen klinischen AD Stadien (gesundes Altern, amnestisches Mild Cognitive Impairment-aMCI, und AD-Demenz) und in verschiedenen Sprachen (Deutsch & Französisch). Alle Ergebnisse basieren auf SVF Sprachdaten, erhoben im Querschnittdesign oder als wiederholte Testung in einem Längsschnittdesign. Aus diesen SVF-Sprachproben werden mit unterschiedlicher rechnerischer Unterstützung qualitative Marker extrahiert (von manueller Verarbeitung der Sprache bis hin zu vollautomatischer Auswertung). Die Ergebnisse zeigen, dass das semantische Gedächtnis bereits im frühen aMCI Stadium strukturell beeinträchtigt ist und im späteren akuten Demenzstadium noch stärker betroffen ist. Die strukturelle Beeinträchtigung des semantischen Gedächtnisses bei Alzheimer wird insbesondere dadurch verschlimmert, dass die Patienten nicht in der Lage sind, dies durch den Einsatz exekutiver Funktionen zu kompensieren. Daher könnten im Verlauf der Erkrankung eingeschränkte Exekutivfunktionen und damit die Unfähigkeit, degenerierte semantische Gedächtnisstrukturen zu kompensieren, die Hauptursache für die auffallend schlechten kognitiven Leistungen von AD-Patienten im Akutstadium sein. Diese Erkenntnisse basierend auf der SVF alleine werden erst durch die computergestützte qualitative Analyse auf Item-per-Item-Ebene möglich und weisen den Weg zu möglichen Anwendungen in der klinischen Entscheidungsunterstützung. Die feinkörnigere qualitative Analyse der SVF ist klinisch wertvoll für die AD-Diagnose und das Screening, aber sehr zeitaufwändig, wenn sie manuell durchgeführt wird. Diese Arbeit zeigt jedoch, dass automatische Analysepipelines diese diagnostischen Informationen zuverlässig und valide aus der SVF generieren können. Die automatische Transkription von Sprache plus die automatische Extraktion der neuartigen qualitativen SVF-Merkmale führen zu einer klinischen Interpretation, die mit manuellen Analysen vergleichbar ist. Diese Verarbeitung führt auch zu einer verbesserten diagnostischen Entscheidungsunterstützung, die durch Klassifikationsexperimente mit maschinellem Lernen simuliert wurde. Dies deutet darauf hin, dass die computergestützte SVF letztendlich für ein kostengünstiges vollautomatisches klinisches AD-Frühscreening eingesetzt werden könnte. Diese Arbeit bringt die aktuelle AD-Forschung auf zweifache Weise voran. Erstens verbessert sie unser Verständnis der kognitiven Einschränkungen im Bereich der Exekutivfunktionen und des semantischen Gedächtnisses bei AD, gemessen durch die computergestützte qualitative Analyse der SVF. Zweitens bettet diese Arbeit diese theoretischen Fortschritte in ein praktisches Konzept zur klinischen Entscheidungsunterstützung ein, das zukünftig ein bevölkerungsweites und kosteneffektives Screening für AD im Frühstadium ermöglichen könnte

Detecting subjectivity through lexicon-grammar. strategies databases, rules and apps for the italian language

2014 - 2015The present research handles the detection of linguistic phenomena connected to subjectivity, emotions and opinions from a computational point of view. The necessity to quickly monitor huge quantity of semi-structured and unstructured data from the web, poses several challenges to Natural Language Processing, that must provide strategies and tools to analyze their structures from a lexical, syntactical and semantic point of views. The general aim of the Sentiment Analysis, shared with the broader fields of NLP, Data Mining, Information Extraction, etc., is the automatic extraction of value from chaos; its specific focus instead is on opinions rather than on factual information. This is the aspect that differentiates it from other computational linguistics subfields. The majority of the sentiment lexicons has been manually or automatically created for the English language; therefore, existent Italian lexicons are mostly built through the translation and adaptation of the English lexical databases, e.g. SentiWordNet and WordNet-Affect. Unlike many other Italian and English sentiment lexicons, our database SentIta, made up on the interaction of electronic dictionaries and lexicon dependent local grammars, is able to manage simple and multiword structures, that can take the shape of distributionally free structures, distributionally restricted structures and frozen structures. Moreover, differently from other lexicon-based Sentiment Analysis methods, our approach has been grounded on the solidity of the Lexicon-Grammar resources and classifications, that provides fine-grained semantic but also syntactic descriptions of the lexical entries. According with the major contribution in the Sentiment Analysis literature, we did not consider polar words in isolation. We computed they elementary sentence contexts, with the allowed transformations and, then, their interaction with contextual valence shifters, the linguistic devices that are able to modify the prior polarity of the words from SentIta, when occurring with them in the same sentences. In order to do so, we took advantage of the computational power of the finite-state technology. We formalized a set of rules that work for the intensification, downtoning and negation modeling, the modality detection and the analysis of comparative forms. With regard to the applicative part of the research, we conducted, with satisfactory results, three experiments on the same number of Sentiment Analysis subtasks: the sentiment classification of documents and sentences, the feature-based Sentiment Analysis and the Semantic Role Labeling based on sentiments. [edited by author]XIV n.s

Leveraging audio-visual speech effectively via deep learning

The rising popularity of neural networks, combined with the recent proliferation of online audio-visual media, has led to a revolution in the way machines encode, recognize, and generate acoustic and visual speech. Despite the ubiquity of naturally paired audio-visual data, only a limited number of works have applied recent advances in deep learning to leverage the duality between audio and video within this domain. This thesis considers the use of neural networks to learn from large unlabelled datasets of audio-visual speech to enable new practical applications. We begin by training a visual speech encoder that predicts latent features extracted from the corresponding audio on a large unlabelled audio-visual corpus. We apply the trained visual encoder to improve performance on lip reading in real-world scenarios. Following this, we extend the idea of video learning from audio by training a model to synthesize raw speech directly from raw video, without the need for text transcriptions. Remarkably, we find that this framework is capable of reconstructing intelligible audio from videos of new, previously unseen speakers. We also experiment with a separate speech reconstruction framework, which leverages recent advances in sequence modeling and spectrogram inversion to improve the realism of the generated speech. We then apply our research in video-to-speech synthesis to advance the state-of-the-art in audio-visual speech enhancement, by proposing a new vocoder-based model that performs particularly well under extremely noisy scenarios. Lastly, we aim to fully realize the potential of paired audio-visual data by proposing two novel frameworks that leverage acoustic and visual speech to train two encoders that learn from each other simultaneously. We leverage these pre-trained encoders for deepfake detection, speech recognition, and lip reading, and find that they consistently yield improvements over training from scratch.Open Acces

Circuit motifs for sensory integration, learning, and the initiation of adaptive behavior in Drosophila

Goal-directed behavior is crucial for survival in complex, dynamic environments. It requires the detection of relevant sensory stimuli and the formation of separable neuronal representations. Learning the contingencies of these sensory stimuli with innately positive or negative valent stimuli (reinforcement) forms associations, allowing the former to cue the latter. This yields cue-based predictions to upgrade the behavioral repertoire from reactive to anticipatory. In this thesis, the Trias of sensory integration, learning of contingencies, and the initiation of anticipatory behavior are studied in the framework of the fruit fly Drosophila olfactory pathway and mushroom body, a higher-order brain center for integrating sensory input and coincidence detection using computational network models representing the mushroom body architecture with varying degrees of abstraction. Additionally, simulations of larval locomotion were employed to investigate how the output of the mushroom body relates to behavior and to foster comparability with animal experiments. We showed that inhibitory feedback within the mushroom body produces sparse stimulus representations, increasing the separability of different sensory stimuli. This separability reduced reinforcement generalization in learning experiments through the decreased overlap of stimulus representations. Furthermore, we showed that feedback from the valence-signaling output to the reinforcement-signaling dopaminergic neurons that innervate the mushroom body could explain experimentally observed temporal dynamics of the formation of associations between sensory cues and reinforcement. This supports the hypothesis that dopaminergic neurons encode the difference between predicted and received reinforcement, which in turn drives the learning process. These dopaminergic neurons have also been argued to convey an indirect reinforcement signal in second-order learning experiments. A new sensory cue is paired with an already established one that activates dopaminergic neurons due to its association with the reinforcement. We demonstrated how different pathways for feedforward or feedback input from the mushroom body’s intrinsic or output neurons can provide an indirect reinforcement signal to the dopaminergic neurons. Any direct or indirect association of sensory cues with reinforcement yielded a reinforcement expectation, biasing the fly’s behavioral response towards the approach or avoidance of the respective sensory cue. We then showed that the simulated locomotory behavior of individual animals in a virtual environment depends on the biasing output of the mushroom body. In conclusion, our results contribute to understanding the implementation of mechanisms for separable stimulus representations, postulated key features of associative learning, and the link between MB output and adaptive behavior in the mushroom body and confirm their explanatory power for animal behavior