Training deep neural density estimators to identify mechanistic models of neural dynamics

Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of underlying causes. However, determining which model parameters agree with complex and stochastic neural data presents a significant challenge. We address this challenge with a machine learning tool which uses deep neural density estimators-- trained using model simulations-- to carry out Bayesian inference and retrieve the full space of parameters compatible with raw data or selected data features. Our method is scalable in parameters and data features, and can rapidly analyze new data after initial training. We demonstrate the power and flexibility of our approach on receptive fields, ion channels, and Hodgkin-Huxley models. We also characterize the space of circuit configurations giving rise to rhythmic activity in the crustacean stomatogastric ganglion, and use these results to derive hypotheses for underlying compensation mechanisms. Our approach will help close the gap between data-driven and theory-driven models of neural dynamics

Evolutionary robotics and neuroscience

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Deep learning for EEG-based Motor Imagery classification: Accuracy-cost trade-off

Electroencephalography (EEG) datasets are often small and high dimensional, owing to cumbersome recording processes. In these conditions, powerful machine learning techniques are essential to deal with the large amount of information and overcome the curse of dimensionality. Artificial Neural Networks (ANNs) have achieved promising performance in EEG-based Brain-Computer Interface (BCI) applications, but they involve computationally intensive training algorithms and hyperparameter optimization methods. Thus, an awareness of the quality-cost trade-off, although usually overlooked, is highly beneficial. In this paper, we apply a hyperparameter optimization procedure based on Genetic Algorithms to Convolutional Neural Networks (CNNs), Feed-Forward Neural Networks (FFNNs), and Recurrent Neural Networks (RNNs), all of them purposely shallow. We compare their relative quality and energy-time cost, but we also analyze the variability in the structural complexity of networks of the same type with similar accuracies. The experimental results show that the optimization procedure improves accuracy in all models, and that CNN models with only one hidden convolutional layer can equal or slightly outperform a 6-layer Deep Belief Network. FFNN and RNN were not able to reach the same quality, although the cost was significantly lower. The results also highlight the fact that size within the same type of network is not necessarily correlated with accuracy, as smaller models can and do match, or even surpass, bigger ones in performance. In this regard, overfitting is likely a contributing factor since deep learning approaches struggle with limited training examples.Spanish Ministerio de Ciencia, Innovacion y Universidades PGC2018-098813-B-C31 PGC2018-098813-B-C32 PSI201565848-

Deep learning for EEG-based Motor Imagery classification: Accuracy-cost trade-off

Electroencephalography (EEG) datasets are often small and high dimensional, owing to cumbersome recording processes. In these conditions, powerful machine learning techniques are essential to deal with the large amount of information and overcome the curse of dimensionality. Artificial Neural Networks (ANNs) have achieved promising performance in EEG-based Brain-Computer Interface (BCI) applications, but they involve computationally intensive training algorithms and hyperparameter optimization methods. Thus, an awareness of the quality-cost trade-off, although usually overlooked, is highly beneficial. In this paper, we apply a hyperparameter optimization procedure based on Genetic Algorithms to Convolutional Neural Networks (CNNs), Feed-Forward Neural Networks (FFNNs), and Recurrent Neural Networks (RNNs), all of them purposely shallow. We compare their relative quality and energy-time cost, but we also analyze the variability in the structural complexity of networks of the same type with similar accuracies. The experimental results show that the optimization procedure improves accuracy in all models, and that CNN models with only one hidden convolutional layer can equal or slightly outperform a 6-layer Deep Belief Network. FFNN and RNN were not able to reach the same quality, although the cost was significantly lower. The results also highlight the fact that size within the same type of network is not necessarily correlated with accuracy, as smaller models can and do match, or even surpass, bigger ones in performance. In this regard, overfitting is likely a contributing factor since deep learning approaches struggle with limited training examples

Computational and Robotic Models of Early Language Development: A Review

We review computational and robotics models of early language learning and development. We first explain why and how these models are used to understand better how children learn language. We argue that they provide concrete theories of language learning as a complex dynamic system, complementing traditional methods in psychology and linguistics. We review different modeling formalisms, grounded in techniques from machine learning and artificial intelligence such as Bayesian and neural network approaches. We then discuss their role in understanding several key mechanisms of language development: cross-situational statistical learning, embodiment, situated social interaction, intrinsically motivated learning, and cultural evolution. We conclude by discussing future challenges for research, including modeling of large-scale empirical data about language acquisition in real-world environments. Keywords: Early language learning, Computational and robotic models, machine learning, development, embodiment, social interaction, intrinsic motivation, self-organization, dynamical systems, complexity.Comment: to appear in International Handbook on Language Development, ed. J. Horst and J. von Koss Torkildsen, Routledg

Testing location memory for threatening and nonthreatening stimuli : implications for evolutionary psychological theorizing.

Humans respond to the presence of threatening stimuli more rapidly than nonthreatening stimuli, a trait that some authors believe humans have been selected for. Based on this finding, it has been proposed that humans should also have superior location memory for threatening stimuli, possibly depending on whether stimuli have ancestral (e.g., snakes) or modern (e.g., guns) ecological relevance. This is herein called the Superior Location Memory for Threatening Stimuli (SLMTS) hypothesis. Some authors believe that humans possess a domain-specific adaptation that gives rise to the hypothesized memory advantage for threatening stimuli. The primary aim of this dissertation is to test the SLMTS hypothesis. Three experiments were performed using stimuli that fully crossed threat level (threatening versus nonthreatening) and ecological relevance (ancestral versus modern). Each experiment included a learning phase, in which subjects responded to threatening or nonthreatening stimuli in various locations, and a subsequent location memory phase. Experiments 1 and 2 tested explicit location memory. Experiment 1 compared recall and recognition tests of conscious location memory. Experiment 2 used a version of the Process Dissociation Procedure to test both conscious and unconscious influences of location memory. Location memory for ancestral nonthreatening, ancestral threatening, and modern threatening stimuli was better than for modern nonthreatening stimuli. These results do not support the SLMTS hypothesis but rather support the general mnemonic principles (GMP) hypothesis, which is that location memory is best for stimuli that are uncommon, arousing, and valenced (either positive or negative). However, Experiment 3 tested implicit location memory and supported the SLMTS hypothesis: Implicit memory was greater for threatening than nonthreatening stimuli. I argue that, taken together, the results of the three experiments do not require the invocation of a specific adaptation for explanatory purposes. Finding support for the GMP hypothesis in Experiments 1 and 2 and the SLMTS hypothesis in Experiment 3 is consistent with a domain-general explanation: Location memory is best for stimuli that are deemed most relevant to the memory system given current circumstances and goals. The relevance of these findings to evolutionary psychological theories of memory is discussed and suggestions for future research are offered

Nuevos Modelos de Aprendizaje Híbrido para Clasificación y Ordenamiento Multi-Etiqueta

En la última década, el aprendizaje multi-etiqueta se ha convertido en una importante tarea de investigación, debido en gran parte al creciente número de problemas reales que contienen datos multi-etiqueta. En esta tesis se estudiaron dos problemas sobre datos multi-etiqueta, la mejora del rendimiento de los algoritmos en datos multi-etiqueta complejos y la mejora del rendimiento de los algoritmos a partir de datos no etiquetados. El primer problema fue tratado mediante métodos de estimación de atributos. Se evaluó la efectividad de los métodos de estimación de atributos propuestos en la mejora del rendimiento de los algoritmos de vecindad, mediante la parametrización de las funciones de distancias empleadas para recuperar los ejemplos más cercanos. Además, se demostró la efectividad de los métodos de estimación en la tarea de selección de atributos. Por otra parte, se desarrolló un algoritmo de vecindad inspirado en el enfoque de clasifcación basada en gravitación de datos. Este algoritmo garantiza un balance adecuado entre eficiencia y efectividad en su solución ante datos multi-etiqueta complejos. El segundo problema fue resuelto mediante técnicas de aprendizaje activo, lo cual permite reducir los costos del etiquetado de datos y del entrenamiento de un mejor modelo. Se propusieron dos estrategias de aprendizaje activo. La primer estrategia resuelve el problema de aprendizaje activo multi-etiqueta de una manera efectiva y eficiente, para ello se combinaron dos medidas que representan la utilidad de un ejemplo no etiquetado. La segunda estrategia propuesta se enfocó en la resolución del problema de aprendizaje activo multi-etiqueta en modo de lotes, para ello se formuló un problema multi-objetivo donde se optimizan tres medidas, y el problema de optimización planteado se resolvió mediante un algoritmo evolutivo. Como resultados complementarios derivados de esta tesis, se desarrolló una herramienta computacional que favorece la implementación de métodos de aprendizaje activo y la experimentación en esta tarea de estudio. Además, se propusieron dos aproximaciones que permiten evaluar el rendimiento de las técnicas de aprendizaje activo de una manera más adecuada y robusta que la empleada comunmente en la literatura. Todos los métodos propuestos en esta tesis han sido evaluados en un marco experimental adecuado, se utilizaron numerosos conjuntos de datos y se compararon los rendimientos de los algoritmos frente a otros métodos del estado del arte. Los resultados obtenidos, los cuales fueron verificados mediante la aplicación de test estadísticos no paramétricos, demuestran la efectividad de los métodos propuestos y de esta manera comprueban las hipótesis planteadas en esta tesis.In the last decade, multi-label learning has become an important area of research due to the large number of real-world problems that contain multi-label data. This doctoral thesis is focused on the multi-label learning paradigm. Two problems were studied, rstly, improving the performance of the algorithms on complex multi-label data, and secondly, improving the performance through unlabeled data. The rst problem was solved by means of feature estimation methods. The e ectiveness of the feature estimation methods proposed was evaluated by improving the performance of multi-label lazy algorithms. The parametrization of the distance functions with a weight vector allowed to recover examples with relevant label sets for classi cation. It was also demonstrated the e ectiveness of the feature estimation methods in the feature selection task. On the other hand, a lazy algorithm based on a data gravitation model was proposed. This lazy algorithm has a good trade-o between e ectiveness and e ciency in the resolution of the multi-label lazy learning. The second problem was solved by means of active learning techniques. The active learning methods allowed to reduce the costs of the data labeling process and training an accurate model. Two active learning strategies were proposed. The rst strategy e ectively solves the multi-label active learning problem. In this strategy, two measures that represent the utility of an unlabeled example were de ned and combined. On the other hand, the second active learning strategy proposed resolves the batch-mode active learning problem, where the aim is to select a batch of unlabeled examples that are informative and the information redundancy is minimal. The batch-mode active learning was formulated as a multi-objective problem, where three measures were optimized. The multi-objective problem was solved through an evolutionary algorithm. This thesis also derived in the creation of a computational framework to develop any active learning method and to favor the experimentation process in the active learning area. On the other hand, a methodology based on non-parametric tests that allows a more adequate evaluation of active learning performance was proposed. All methods proposed were evaluated by means of extensive and adequate experimental studies. Several multi-label datasets from di erent domains were used, and the methods were compared to the most signi cant state-of-the-art algorithms. The results were validated using non-parametric statistical tests. The evidence showed the e ectiveness of the methods proposed, proving the hypotheses formulated at the beginning of this thesis