Logical openness in Cognitive Models

It is here proposed an analysis of symbolic and sub-symbolic models for studying cognitive processes, centered on emergence and logical openness notions. The Theory of logical openness connects the Physics of system/environment relationships to the system informational structure. In this theory, cognitive models can be ordered according to a hierarchy of complexity depending on their logical openness degree, and their descriptive limits are correlated to Gödel-Turing Theorems on formal systems. The symbolic models with low logical openness describe cognition by means of semantics which fix the system/environment relationship (cognition in vitro), while the sub-symbolic ones with high logical openness tends to seize its evolutive dynamics (cognition in vivo). An observer is defined as a system with high logical openness. In conclusion, the characteristic processes of intrinsic emergence typical of “bio-logic” - emerging of new codes-require an alternative model to Turing-computation, the natural or bio-morphic computation, whose essential features we are going here to outline

Spatio-Temporal Patterns act as Computational Mechanisms governing Emergent behavior in Robotic Swarms

open access articleOur goal is to control a robotic swarm without removing its swarm-like nature. In other words, we aim to intrinsically control a robotic swarm emergent behavior. Past attempts at governing robotic swarms or their selfcoordinating emergent behavior, has proven ineffective, largely due to the swarm’s inherent randomness (making it difficult to predict) and utter simplicity (they lack a leader, any kind of centralized control, long-range communication, global knowledge, complex internal models and only operate on a couple of basic, reactive rules). The main problem is that emergent phenomena itself is not fully understood, despite being at the forefront of current research. Research into 1D and 2D Cellular Automata has uncovered a hidden computational layer which bridges the micromacro gap (i.e., how individual behaviors at the micro-level influence the global behaviors on the macro-level). We hypothesize that there also lie embedded computational mechanisms at the heart of a robotic swarm’s emergent behavior. To test this theory, we proceeded to simulate robotic swarms (represented as both particles and dynamic networks) and then designed local rules to induce various types of intelligent, emergent behaviors (as well as designing genetic algorithms to evolve robotic swarms with emergent behaviors). Finally, we analysed these robotic swarms and successfully confirmed our hypothesis; analyzing their developments and interactions over time revealed various forms of embedded spatiotemporal patterns which store, propagate and parallel process information across the swarm according to some internal, collision-based logic (solving the mystery of how simple robots are able to self-coordinate and allow global behaviors to emerge across the swarm)

Soft thought (in architecture and choreography)

This article is an introduction to and exploration of the concept of ‘soft thought’. What we want to propose through the definition of this concept is an aesthetic of digital code that does not necessarily presuppose a relation with the generative aspects of coding, nor with its sensorial perception and evaluation. Numbers do not have to produce something, and do not need to be transduced into colours and sounds, in order to be considered as aesthetic objects. Starting from this assumption, our main aim will be to reconnect the numerical aesthetic of code with a more ‘abstract’ kind of feeling, the feeling of numbers indirectly felt as conceptual contagions’, that are ‘conceptually felt but not directly sensed. The following pages will be dedicated to the explication and exemplification of this particular mode of feeling, and to its possible definition as ‘soft thought’

A geometric model of multi-scale orientation preference maps via Gabor functions

In this paper we present a new model for the generation of orientation preference maps in the primary visual cortex (V1), considering both orientation and scale features. First we undertake to model the functional architecture of V1 by interpreting it as a principal fiber bundle over the 2-dimensional retinal plane by introducing intrinsic variables orientation and scale. The intrinsic variables constitute a fiber on each point of the retinal plane and the set of receptive profiles of simple cells is located on the fiber. Each receptive profile on the fiber is mathematically interpreted as a rotated Gabor function derived from an uncertainty principle. The visual stimulus is lifted in a 4-dimensional space, characterized by coordinate variables, position, orientation and scale, through a linear filtering of the stimulus with Gabor functions. Orientation preference maps are then obtained by mapping the orientation value found from the lifting of a noise stimulus onto the 2-dimensional retinal plane. This corresponds to a Bargmann transform in the reducible representation of the $\text{SE}(2)=\mathbb{R}^2\times S^1$ group. A comparison will be provided with a previous model based on the Bargman transform in the irreducible representation of the $\text{SE}(2)$ group, outlining that the new model is more physiologically motivated. Then we present simulation results related to the construction of the orientation preference map by using Gabor filters with different scales and compare those results to the relevant neurophysiological findings in the literature

A sub-Riemannian model of the visual cortex with frequency and phase

In this paper we present a novel model of the primary visual cortex (V1) based on orientation, frequency and phase selective behavior of the V1 simple cells. We start from the first level mechanisms of visual perception: receptive profiles. The model interprets V1 as a fiber bundle over the 2-dimensional retinal plane by introducing orientation, frequency and phase as intrinsic variables. Each receptive profile on the fiber is mathematically interpreted as a rotated, frequency modulated and phase shifted Gabor function. We start from the Gabor function and show that it induces in a natural way the model geometry and the associated horizontal connectivity modeling the neural connectivity patterns in V1. We provide an image enhancement algorithm employing the model framework. The algorithm is capable of exploiting not only orientation but also frequency and phase information existing intrinsically in a 2-dimensional input image. We provide the experimental results corresponding to the enhancement algorithm

Generative Models for Novelty Detection Applications in abnormal event and situational changedetection from data series

Novelty detection is a process for distinguishing the observations that differ in some respect from the observations that the model is trained on. Novelty detection is one of the fundamental requirements of a good classification or identification system since sometimes the test data contains observations that were not known at the training time. In other words, the novelty class is often is not presented during the training phase or not well defined. In light of the above, one-class classifiers and generative methods can efficiently model such problems. However, due to the unavailability of data from the novelty class, training an end-to-end model is a challenging task itself. Therefore, detecting the Novel classes in unsupervised and semi-supervised settings is a crucial step in such tasks. In this thesis, we propose several methods to model the novelty detection problem in unsupervised and semi-supervised fashion. The proposed frameworks applied to different related applications of anomaly and outlier detection tasks. The results show the superior of our proposed methods in compare to the baselines and state-of-the-art methods

El núcleo posterior medial del tálamo y su implicación en los procesos perceptivos y cognitivos

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Medicina, Departamento de Anatomía, Histología y Neurociencia. Fecha de lectura: 11-05-2021Se desconoce en gran medida cómo el sistema somestésico extrae información del flujo de señales sensoriales. La información procedente de las vibrisas es procesada principalmente por dos vías ascendentes paralelas hacia la corteza cerebral. Sin embargo, se desconoce la implicación funcional de las diferentes estructuras que componen dichas rutas. Mediante la combinación de técnicas electrofisiológicas y farmacológicas in vivo en ratas, encontramos diferencias significativas entre estas vías. Aunque está bien asumido que el POm y el VPM responden a la estimulación de las vibrisas contralaterales, encontramos que el primero es capaz de responder también a las ipsilaterales. Mediante la integración de señales simultaneas procedentes de vibrisas en ambos lados de la cara, está implicado en la representación de eventos táctiles bilaterales. Esto demuestra la implicación de los núcleos talámicos sensoriales de tipo 'higher-order' en la percepción bilateral. Encontramos que los núcleos POm están mutuamente conectados a través de la corteza formando un bucle o 'loop' funcional. Revelamos la naturaleza y el contenido de los mensajes transmitidos a través de este circuito mostrando que dichos mensajes son 'patrones estructurados de actividad sostenida'. Estos mensajes son transmitidos preservando su estructura integrada. La implicación de diferentes áreas fue investigada descubriendo que S1 juega un papel protagonista en dicho 'loop' POm-POm. También encontramos diferente implicación laminar en esta área en el procesamiento de actividad sostenida y en su transmisión entre hemisferios. Proponemos un modelo teórico en el que dichos 'patrones estructurados de actividad sostenida' generados por el POm pueden jugar un papel relevante en las funciones perceptivas, motoras y cognitivas. Además, demostramos que el POm está involucrado en la representación de patrones sensoriales complejos. Este núcleo es muy sensible a la activación simultanea de las vibrisas y a las complejas interacciones espaciotemporales que se producen entre ellas. La estructura espaciotemporal de dichos patrones y la complejidad de sus partes son reflejados en precisos cambios de actividad en el POm. Nuestros resultados sugieren que este núcleo podría ser un codificador general de patrones. La naturaleza (estructurada versus discreta), el tipo (sostenido versus transitorio) y el contenido (integrado versus segregado) de la actividad neural procesada y transmitida por estos núcleos determina su implicación funcional y puede permitir clasificarlos. Proponemos la hipótesis de los Componentes Complementarios para explicar estas diferencias. Además, revelamos la capacidad del POm para ajustar el procesamiento en las cortezas S1 y S2 mediante la inducción de una precisa inhibición en determinadas capas corticales. Esta modulación está mediada por neuronas GABAérgicas de la capa 1. La hipótesis de Computación Cortical por Resultados Discretos propuesta aquí puede explicar la implicación funcional de dicho ajust