Revoluciones científicas y escepticismo

The skepticism is a philosophical attitude that doubts the existence of all eternal and immutable epistemology truth; this skeptical attitude is necessary to go in the search of new scientific paradigms, and new forms to include/understand the physical and social reality, physical reality.El escepticismo es una actitud filosófica que duda de la existencia de toda verdad epistemológica eterna e inmutable; esta actitud escéptica es necesaria para ir en la búsqueda de nuevos paradigmas científicos, y nuevas formas de comprender la realidad física y social

Unsupervised adaptation for acceleration-based activity recognition: robustness to sensor displacement and rotation

A common assumption in activity recognition is that the system remains unchanged between its design and its posterior operation. However, many factors affect the data distribution between two different experimental sessions. One of these factors is the potential change in the sensor location (e.g. due to replacement or slippage) affecting the classification performance. Assuming that changes in the sensor placement mainly result in shifts in the feature distributions, we propose an unsupervised adaptive classifier that calibrates itself using an online version of expectation-maximisation. Tests using three activity recognition scenarios show that the proposed adaptive algorithm is robust against shift in the feature space due to sensor displacement and rotation. Moreover, since the method estimates the change in the feature distribution, it can also be used to roughly evaluate the reliability of the system during online operatio

Spatial learning and navigation in the rat:a biomimetic model

Animals behave in different ways depending on the specific task they are required to solve. In certain cases, if a cue marks the goal location, they can rely on simple stimulusresponse associations. In contrast, other tasks require the animal to be endowed with a representation of space. Such a representation (i.e. cognitive map) allows the animal to locate itself within a known environment and perform complex target-directed behaviour. In order to efficiently perform, the animal not only should be able to exhibit these types of behaviour, but it should be able to select which behaviour is the most appropriate at any given task conditions. Neurophysiological and behavioural experiments provide important information on how such processes may take place in the rodent's brain. Specifically, place- and orientation sensitive cells in the rat Hippocampus have been interpreted as a neural substrate for spatial abilities related to the theory of the cognitive map proposed in the late 1940s by Tolman. Moreover, recent dissociation experiments using selectively located lesions, as well as pharmacological studies have shown that different brain regions may be involved in different types of behaviour. Accordingly, one memory system involving the hippocampus and the ventral striatum would be responsible for cognitive navigation, while navigation based on stimulus-response associations would be mediated by the dorsolateral striatum. Based on these studies, the aim of this work is to develop a neural network model of the spatial abilities of the rat. The model, based on functional properties and anatomical inter-connections of the brain areas involved in spatial learning should be able to establish a distributed representation of space composed of place-sensitive units. Such a representation takes into account both internal and external sensory information, and the model reproduces physiological properties of place cells such as changes in their directional dependence. Moreover, the spatial representation may be used to perform cognitive navigation. Modelled place cells drive an extra-hippocampal population of action-coding cells, allowing the establishment of place-response associations. These associations encoded in synaptic connections between place- and action-cells are modified by means of reinforcement learning. In a similar way, simple sensory input can be used to establish stimulus-response associations. These associations are encoded in a different set of action cells which corresponds to a different neural substrate encoding for non-cognitive navigation strategies (i.e. taxon or praxic). Both cognitive and non-cognitive navigation strategies compete for action control to determine the actual behaviour of the agent. Tests of the performance of the model show that it is able to establish a representation of space, and modelled place cells reproduce some physiological properties of their biological counterparts. Furthermore, the model reproduces goal-based behaviour based on both cognitive and non-cognitive strategies as well as behaviour in conflicting situations reported in experimental studies in animals

A computational model of parallel navigation systems in rodents

Several studies in rats support the idea of multiple neural systems competing to select the best action for reaching a goal or food location. Locale navigation strategies, necessary for reaching invisible goals, seem to be mediated by the hippocampus and the ventral and dorsomedial striatum whereas taxon strategies, applied for approaching goals in the visual field, are believed to involve the dorsolateral striatum. A computational model of action selection is presented, in which different experts, implementing locale and taxon strategies, compete in order to select the appropriate behavior for the current task. The model was tested in a simulated robot using an experimental paradigm that dissociates the use of cue and spatial informatio

Revoluciones científicas y escepticismo

Una efectiva reflexión acerca de la Ciencia debe realizarse al margen de la Ciencia misma. Esta afirmación comporta una característica y dos consecuencias: la característica es que se afirma en un contexto en el que la Ciencia posee la hegemonía del saber. Las consecuencias son que para hacerla posible se debe desactivar la función del lenguaje al servicio de la ciencia y atender al hecho de que la verdad no debe entenderse como una mera obra humana, pese a que habite en el hombre.Palabras clave: Revolución científica, verdad, apariencia, lenguaje, distancia dialógica, modernidad, Galileo, Platón.Abstract:An effective reflection for the Science must be made apart from the Science itself. This affirmation implies one characteristic and two consequences: The characteristic is that the affirmation is rinsed from the context in what the science has the hegemony of knowledge. The consequences are that it becomes possible if we deactivate the function of the language at science´s service and attend the fact that the truth must not be understood like a mere workmanship, in spite of it inhabiting into the man.Keywords: Scientific Revolution, truth, appearance, language, dialogical distance, modernity, Galileo, Platoon.</p

Context–aware Learning for Generative Models

This work studies the class of algorithms for learning with side-information that emerges by extending generative models with embedded context-related variables. Using finite mixture models (FMMs) as the prototypical Bayesian network, we show that maximum-likelihood estimation (MLE) of parameters through expectation-maximization (EM) improves over the regular unsupervised case and can approach the performances of supervised learning, despite the absence of any explicit ground-truth data labeling. By direct application of the missing information principle (MIP), the algorithms' performances are proven to range between the conventional supervised and unsupervised MLE extremities proportionally to the information content of the contextual assistance provided. The acquired benefits regard higher estimation precision, smaller standard errors, faster convergence rates, and improved classification accuracy or regression fitness shown in various scenarios while also highlighting important properties and differences among the outlined situations. Applicability is showcased with three real-world unsupervised classification scenarios employing Gaussian mixture models. Importantly, we exemplify the natural extension of this methodology to any type of generative model by deriving an equivalent context-aware algorithm for variational autoencoders (VAs), thus broadening the spectrum of applicability to unsupervised deep learning with artificial neural networks. The latter is contrasted with a neural-symbolic algorithm exploiting side information

Context-Aware Brain-Computer Interfaces

Systems using brain-generated signals can control complex, smart devices by taking into account information about the situation at hand, as well as the operator’s cognitive state

Learning from EEG Error-related Potentials in Noninvasive Brain-Computer Interfaces

We describe error-related potentials generated while a human user monitors the performance of an external agent and discuss their use for a new type of Brain-Computer Interaction. In this approach, single trial detection of error-related EEG potentials is used to infer the optimal agent behavior by decreasing the probability of agent decisions that elicited such potentials. Contrasting with traditional approaches, the user acts as a critic of an external autonomous system instead of continuously generating control commands. This sets a cognitive monitoring loop where the human directly provides information about the overall system performance that, in turn, can be used for its improvement. We show that it is possible to recognize erroneous and correct agent decisions from EEG (average recognition rates of 75.8% and 63.2%, respectively), and that the elicited signals are stable over long periods of time (from 50 to $>$600 days). Moreover, these performances allow to infer the optimal behavior of a simple agent in a Brain-Computer Interaction paradigm after a few trials