Learnable latent embeddings for joint behavioral and neural analysis

Mapping behavioral actions to neural activity is a fundamental goal of neuroscience. As our ability to record large neural and behavioral data increases, there is growing interest in modeling neural dynamics during adaptive behaviors to probe neural representations. In particular, neural latent embeddings can reveal underlying correlates of behavior, yet, we lack non-linear techniques that can explicitly and flexibly leverage joint behavior and neural data. Here, we fill this gap with a novel method, CEBRA, that jointly uses behavioral and neural data in a hypothesis- or discovery-driven manner to produce consistent, high-performance latent spaces. We validate its accuracy and demonstrate our tool's utility for both calcium and electrophysiology datasets, across sensory and motor tasks, and in simple or complex behaviors across species. It allows for single and multi-session datasets to be leveraged for hypothesis testing or can be used label-free. Lastly, we show that CEBRA can be used for the mapping of space, uncovering complex kinematic features, and rapid, high-accuracy decoding of natural movies from visual cortex.Comment: Website: cebra.a

An enhanced resampling technique for imbalanced data sets

A data set is considered imbalanced if the distribution of instances in one class (majority class) outnumbers the other class (minority class). The main problem related to binary imbalanced data sets is classifiers tend to ignore the minority class. Numerous resampling techniques such as undersampling, oversampling, and a combination of both techniques have been widely used. However, the undersampling and oversampling techniques suffer from elimination and addition of relevant data which may lead to poor classification results. Hence, this study aims to increase classification metrics by enhancing the undersampling technique and combining it with an existing oversampling technique. To achieve this objective, a Fuzzy Distancebased Undersampling (FDUS) is proposed. Entropy estimation is used to produce fuzzy thresholds to categorise the instances in majority and minority class into membership functions. FDUS is then combined with the Synthetic Minority Oversampling TEchnique (SMOTE) known as FDUS+SMOTE, which is executed in sequence until a balanced data set is achieved. FDUS and FDUS+SMOTE are compared with four techniques based on classification accuracy, F-measure and Gmean. From the results, FDUS achieved better classification accuracy, F-measure and G-mean, compared to the other techniques with an average of 80.57%, 0.85 and 0.78, respectively. This showed that fuzzy logic when incorporated with Distance-based Undersampling technique was able to reduce the elimination of relevant data. Further, the findings showed that FDUS+SMOTE performed better than combination of SMOTE and Tomek Links, and SMOTE and Edited Nearest Neighbour on benchmark data sets. FDUS+SMOTE has minimised the removal of relevant data from the majority class and avoid overfitting. On average, FDUS and FDUS+SMOTE were able to balance categorical, integer and real data sets and enhanced the performance of binary classification. Furthermore, the techniques performed well on small record size data sets that have of instances in the range of approximately 100 to 800

Movie’s box office performance prediction: An approach based on movie’s script, text mining and deep learning

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceA capacidade de prever a bilheteria de filmes tem sido atividade de grande interesse para investigadores. Entretanto, parcela significativa destes estudos concentra-se no uso de variáveis disponíveis apenas nos estágios de produção e pós-produção de filmes. O objetivo deste trabalho é desenvolver um modelo preditivo de bilheteria baseando-se apenas em informações dos roteiros dos filmes, por meio do uso de técnicas de processamento de linguagem natural (PLN), mineração de texto e de redes neuronais profundas. Essa abordagem visa otimizar a tomada de decisão de investidores em uma fase ainda inicial dos projetos, com foco específico na melhoria dos processos seletivos da Agência Nacional do Cinema do Brasil.The ability to predict movies box-office has been a field of interest for many researchers. However, most of these studies are concentrated on variables that are available only in later stages as in production and pos-production phase of films. The objective of this work is to develop a predictive model to forecast movie box-office performance based only on information in the movie script, using natural language processing techniques, text mining and deep learning neural networks. This approach aims to optimize the investor’s decision-making process at earlier steps of the project, with special focus on the selection process of the Brazilian Film Agency (ANCINE – Agência Nacional do cinema)

Neural Encoding and Decoding with Deep Learning for Natural Vision

The overarching objective of this work is to bridge neuroscience and artificial intelligence to ultimately build machines that learn, act, and think like humans. In the context of vision, the brain enables humans to readily make sense of the visual world, e.g. recognizing visual objects. Developing human-like machines requires understanding the working principles underlying the human vision. In this dissertation, I ask how the brain encodes and represents dynamic visual information from the outside world, whether brain activity can be directly decoded to reconstruct and categorize what a person is seeing, and whether neuroscience theory can be applied to artificial models to advance computer vision. To address these questions, I used deep neural networks (DNN) to establish encoding and decoding models for describing the relationships between the brain and the visual stimuli. Using the DNN, the encoding models were able to predict the functional magnetic resonance imaging (fMRI) responses throughout the visual cortex given video stimuli; the decoding models were able to reconstruct and categorize the visual stimuli based on fMRI activity. To further advance the DNN model, I have implemented a new bidirectional and recurrent neural network based on the predictive coding theory. As a theory in neuroscience, predictive coding explains the interaction among feedforward, feedback, and recurrent connections. The results showed that this brain-inspired model significantly outperforms feedforward-only DNNs in object recognition. These studies have positive impact on understanding the neural computations under human vision and improving computer vision with the knowledge from neuroscience

A conceptual model of enhanced undersampling technique

Imbalanced datasets often lead to decrement of classifiers’ performance.Undersampling technique is one of the approaches that is used when dealing with imbalanced datasets problem.This paper discusses on the advantages and disadvantages of several undersampling techniques.An enhanced Distancebased undersampling technique is proposed to balance the imbalanced data that will be used for classification. The fuzzy logic has been integrated in the distance-based undersampling technique to resolve the ambiguity and bias issues

Measuring quality of video of internet protocol television (IPTV)

141 p.La motivación para el desarrollo de esta tesis es la necesidad que existe de monitorizar la calidad de experiencia del vídeo que se proporciona en una red IPTV (Internet Protocol Television). Esta necesidad surge del deseo de los operadores de telecomunicaciones de proporcionar un servicio más satisfactorio a sus clientes y alcanzar mayor penetración en el mercado. Los servicios sólo pueden tener éxito si la calidad de experiencia se garantiza. Las redes IPTV (Television sobre IP) son por naturaleza susceptibles a pérdidas de paquetes de datos que afectan a la calidad del vídeo que recibe el usuario. Entre los factores que contribuyen a la existencia de pérdida de paquetes de datos se encuentran la congestión de red, una planificación de red inadecuada o el fallo de algún equipamiento de la red. La calidad de experiencia de un vídeo se ve afectada por una serie de factores como por ejemplo la resolución, la ausencia de errores en las imágenes, la calidad de la televisión, las expectativas previas del usuario y muchos otros factores que se estudian en esta tesis

Measuring quality of video of internet protocol television (IPTV)

141 p.La motivación para el desarrollo de esta tesis es la necesidad que existe de monitorizar la calidad de experiencia del vídeo que se proporciona en una red IPTV (Internet Protocol Television). Esta necesidad surge del deseo de los operadores de telecomunicaciones de proporcionar un servicio más satisfactorio a sus clientes y alcanzar mayor penetración en el mercado. Los servicios sólo pueden tener éxito si la calidad de experiencia se garantiza. Las redes IPTV (Television sobre IP) son por naturaleza susceptibles a pérdidas de paquetes de datos que afectan a la calidad del vídeo que recibe el usuario. Entre los factores que contribuyen a la existencia de pérdida de paquetes de datos se encuentran la congestión de red, una planificación de red inadecuada o el fallo de algún equipamiento de la red. La calidad de experiencia de un vídeo se ve afectada por una serie de factores como por ejemplo la resolución, la ausencia de errores en las imágenes, la calidad de la televisión, las expectativas previas del usuario y muchos otros factores que se estudian en esta tesis