204 research outputs found

    Computational models of attachment and self-attachment

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    We explore, using a variety of models grounded in computational neuroscience, the dynamics of attachment formation and change. In the first part of the thesis we consider the formation of the traditional organised forms of attachment (as defined by Mary Ainsworth) within the context of the free energy principle, showing how each type of attachment might arise in infant agents who minimise free energy over interoceptive states while interacting with caregivers with varying responsiveness. We show how exteroceptive cues (in the form of disrupted affective communication from the caregiver) can result in disorganised forms of attachment (as first uncovered by Mary Main) in infants of caregivers who consistently increase stress on approach, but can have an organising (towards ambivalence) effect in infants of inconsistent caregivers. The second part of the thesis concerns Self-Attachment: a new self-administrable attachment-based psychotherapy recently introduced by Abbas Edalat, which aims to induce neural plasticity in order to retrain an individual's suboptimal attachment schema. We begin with a model of the hypothesised neurobiological underpinnings of the Self-Attachment bonding protocols, which are concerned with the formation of an abstract, self-directed bond. Finally, using neuroscientific findings related to empathy and the self-other distinction within the context of pain, we propose a simple spiking neural model for how empathic states might serve to motivate application of the aforementioned bonding protocols.Open Acces

    Efficient Learning Machines

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    Computer scienc

    Higher-order interactions in single-cell gene expression: towards a cybergenetic semantics of cell state

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    Finding and understanding patterns in gene expression guides our understanding of living organisms, their development, and diseases, but is a challenging and high-dimensional problem as there are many molecules involved. One way to learn about the structure of a gene regulatory network is by studying the interdependencies among its constituents in transcriptomic data sets. These interdependencies could be arbitrarily complex, but almost all current models of gene regulation contain pairwise interactions only, despite experimental evidence existing for higher-order regulation that cannot be decomposed into pairwise mechanisms. I set out to capture these higher-order dependencies in single-cell RNA-seq data using two different approaches. First, I fitted maximum entropy (or Ising) models to expression data by training restricted Boltzmann machines (RBMs). On simulated data, RBMs faithfully reproduced both pairwise and third-order interactions. I then trained RBMs on 37 genes from a scRNA-seq data set of 70k astrocytes from an embryonic mouse. While pairwise and third-order interactions were revealed, the estimates contained a strong omitted variable bias, and there was no statistically sound and tractable way to quantify the uncertainty in the estimates. As a result I next adopted a model-free approach. Estimating model-free interactions (MFIs) in single-cell gene expression data required a quasi-causal graph of conditional dependencies among the genes, which I inferred with an MCMC graph-optimisation algorithm on an initial estimate found by the Peter-Clark algorithm. As the estimates are model-free, MFIs can be interpreted either as mechanistic relationships between the genes, or as substructures in the cell population. On simulated data, MFIs revealed synergy and higher-order mechanisms in various logical and causal dynamics more accurately than any correlation- or information-based quantities. I then estimated MFIs among 1,000 genes, at up to seventh-order, in 20k neurons and 20k astrocytes from two different mouse brain scRNA-seq data sets: one developmental, and one adolescent. I found strong evidence for up to fifth-order interactions, and the MFIs mostly disambiguated direct from indirect regulation by preferentially coupling causally connected genes, whereas correlations persisted across causal chains. Validating the predicted interactions against the Pathway Commons database, gene ontology annotations, and semantic similarity, I found that pairwise MFIs contained different but a similar amount of mechanistic information relative to networks based on correlation. Furthermore, third-order interactions provided evidence of combinatorial regulation by transcription factors and immediate early genes. I then switched focus from mechanism to population structure. Each significant MFI can be assigned a set of single cells that most influence its value. Hierarchical clustering of the MFIs by cell assignment revealed substructures in the cell population corresponding to diverse cell states. This offered a new, purely data-driven view on cell states because the inferred states are not required to localise in gene expression space. Across the four data sets, I found 69 significant and biologically interpretable cell states, where only 9 could be obtained by standard approaches. I identified immature neurons among developing astrocytes and radial glial cells, D1 and D2 medium spiny neurons, D1 MSN subtypes, and cell-cycle related states present across four data sets. I further found evidence for states defined by genes associated to neuropeptide signalling, neuronal activity, myelin metabolism, and genomic imprinting. MFIs thus provide a new, statistically sound method to detect substructure in single-cell gene expression data, identifying cell types, subtypes, or states that can be delocalised in gene expression space and whose hierarchical structure provides a new view on the semantics of cell state. The estimation of the quasi-causal graph, the MFIs, and inference of the associated states is implemented as a publicly available Nextflow pipeline called Stator

    Predicting book sales trend using deep learning framework

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    A deep learning framework like Generative Adversarial Network (GAN) has gained popularity in recent years for handling many different computer visions related problems. In this research, instead of focusing on generating the near-real images using GAN, the aim is to develop a comprehensive GAN framework for book sales ranks prediction, based on the historical sales rankings and different attributes collected from the Amazon site. Different analysis stages have been conducted in the research. In this research, a comprehensive data preprocessing is required before the modeling and evaluation. Extensive predevelopment on the data, related features selections for predicting the sales rankings, and several data transformation techniques are being applied before generating the models. Later then various models are being trained and evaluated on prediction results. In the GAN architecture, the generator network that used to generate the features is being built, and the discriminator network that used to differentiate between real and fake features is being trained before the predictions. Lastly, the regression GAN model prediction results are compared against the different neural network models like multilayer perceptron, deep belief network, convolution neural network

    Neurocomputational model for learning, memory consolidation and schemas

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    This thesis investigates how through experience the brain acquires and stores memories, and uses these to extract and modify knowledge. This question is being studied by both computational and experimental neuroscientists as it is of relevance for neuroscience, but also for artificial systems that need to develop knowledge about the world from limited, sequential data. It is widely assumed that new memories are initially stored in the hippocampus, and later are slowly reorganised into distributed cortical networks that represent knowledge. This memory reorganisation is called systems consolidation. In recent years, experimental studies have revealed complex hippocampal-neocortical interactions that have blurred the lines between the two memory systems, challenging the traditional understanding of memory processes. In particular, the prior existence of cortical knowledge frameworks (also known as schemas) was found to speed up learning and consolidation, which seemingly is at odds with previous models of systems consolidation. However, the underlying mechanisms of this effect are not known. In this work, we present a computational framework to explore potential interactions between the hippocampus, the prefrontal cortex, and associative cortical areas during learning as well as during sleep. To model the associative cortical areas, where the memories are gradually consolidated, we have implemented an artificial neural network (Restricted Boltzmann Machine) so as to get insight into potential neural mechanisms of memory acquisition, recall, and consolidation. We analyse the network’s properties using two tasks inspired by neuroscience experiments. The network gradually built a semantic schema in the associative cortical areas through the consolidation of multiple related memories, a process promoted by hippocampal-driven replay during sleep. To explain the experimental data we suggest that, as the neocortical schema develops, the prefrontal cortex extracts characteristics shared across multiple memories. We call this information meta-schema. In our model, the semantic schema and meta-schema in the neocortex are used to compute consistency, conflict and novelty signals. We propose that the prefrontal cortex uses these signals to modulate memory formation in the hippocampus during learning, which in turn influences consolidation during sleep replay. Together, these results provide theoretical framework to explain experimental findings and produce predictions for hippocampal-neocortical interactions during learning and systems consolidation

    Complex event recognition through wearable sensors

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    Complex events are instrumental in understanding advanced behaviours and properties of a system. They can represent more meaningful events as compared to simple events. In this thesis we propose to use wearable sensor signals to detect complex events. These signals are pertaining to the user's state and therefore allow us to understand advanced characteristics about her. We propose a hierarchical approach to detect simple events from the wearable sensors data and then build complex events on top of them. In order to address privacy concerns that rise from the use of sensitive signals, we propose to perform all the computation on device. While this ensures the privacy of the data, it poses the problem of having limited computational resources. This problem is tackled by introducing energy efficient approaches based on incremental algorithms. A second challenge is the multiple levels of noise in the process. A first level of noise concerns the raw signals that are inherently imprecise (e.g. inaccuracy in GPS readings). A second level of noise, that we call semantic noise, is present among the simple events detected. Some of these simple events can disturb the detection of complex events effectively acting as noise. We apply the hierarchical approach in two different contexts defining the two different parts of our thesis. In the first part, we present a mobile system that builds a representation of the user's life. This system is based on the episodic memory model, which is responsible for the storage and recollection of past experiences. Following the hierarchical approach, the system processes raw signals to detect simple events such as places where the user stayed a certain amount of time to perform an activity, therefore building sequences of detected activities. These activities are in turn processed to detect complex events that we call routines and that represent recurrent patterns in the life of the user. In the second part of this thesis, we focus on the detection of glycemic events for diabetes type-1 patients in a non-invasive manner. Diabetics are not able to properly regulate their glucose, leading to periods of high and low blood sugar. We leverage signals (Electrocardiogram (ECG), accelerometer, breathing rate) from a sport belt to infer such glycemic events. We propose a physiological model based on the variations of the ECG when the patient has low blood sugar, and an energy-based model that computes the current glucose level of the user based on her glucose intake, insulin intake and glucose consumption via physical activity. For both contexts, we evaluate our systems in term of accuracy by assessing wether the detected routines are meaningful, and wether the glycemic events are correctly detected, and in term of mobile performance, which confirms the fitness of our approaches for mobile computation

    Exploring Low-Dimensional Structures in Images Using Deep Fourier Machines

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    The ground-breaking results achieved by Deep Generative Models, when given merely a dataset representing the desired distribution of generated images have caught the interest of scholars. In this work, we introduce a novel structure designed for image generation utilizing the idea behind Fourier Series and Deep Learning function composition. By composing low-dimensional structures, we will first compress a high-dimensional image, and then we will use this latent space to generate fake images. Our compression algorithm gives comparable results to the JPEG algorithm and even, in some cases, outperforms it. Also, our image generation model can generate decent fake images on MNIST and CIFAR-10 datasets and can surpass the first generation of Variational Autoencoders

    Optimisation Method for Training Deep Neural Networks in Classification of Non- functional Requirements

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    Non-functional requirements (NFRs) are regarded critical to a software system's success. The majority of NFR detection and classification solutions have relied on supervised machine learning models. It is hindered by the lack of labelled data for training and necessitate a significant amount of time spent on feature engineering. In this work we explore emerging deep learning techniques to reduce the burden of feature engineering. The goal of this study is to develop an autonomous system that can classify NFRs into multiple classes based on a labelled corpus. In the first section of the thesis, we standardise the NFRs ontology and annotations to produce a corpus based on five attributes: usability, reliability, efficiency, maintainability, and portability. In the second section, the design and implementation of four neural networks, including the artificial neural network, convolutional neural network, long short-term memory, and gated recurrent unit are examined to classify NFRs. These models, necessitate a large corpus. To overcome this limitation, we proposed a new paradigm for data augmentation. This method uses a sort and concatenates strategy to combine two phrases from the same class, resulting in a two-fold increase in data size while keeping the domain vocabulary intact. We compared our method to a baseline (no augmentation) and an existing approach Easy data augmentation (EDA) with pre-trained word embeddings. All training has been performed under two modifications to the data; augmentation on the entire data before train/validation split vs augmentation on train set only. Our findings show that as compared to EDA and baseline, NFRs classification model improved greatly, and CNN outperformed when trained using our suggested technique in the first setting. However, we saw a slight boost in the second experimental setup with just train set augmentation. As a result, we can determine that augmentation of the validation is required in order to achieve acceptable results with our proposed approach. We hope that our ideas will inspire new data augmentation techniques, whether they are generic or task specific. Furthermore, it would also be useful to implement this strategy in other languages
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