Towards Building Deep Networks with Bayesian Factor Graphs

We propose a Multi-Layer Network based on the Bayesian framework of the Factor Graphs in Reduced Normal Form (FGrn) applied to a two-dimensional lattice. The Latent Variable Model (LVM) is the basic building block of a quadtree hierarchy built on top of a bottom layer of random variables that represent pixels of an image, a feature map, or more generally a collection of spatially distributed discrete variables. The multi-layer architecture implements a hierarchical data representation that, via belief propagation, can be used for learning and inference. Typical uses are pattern completion, correction and classification. The FGrn paradigm provides great flexibility and modularity and appears as a promising candidate for building deep networks: the system can be easily extended by introducing new and different (in cardinality and in type) variables. Prior knowledge, or supervised information, can be introduced at different scales. The FGrn paradigm provides a handy way for building all kinds of architectures by interconnecting only three types of units: Single Input Single Output (SISO) blocks, Sources and Replicators. The network is designed like a circuit diagram and the belief messages flow bidirectionally in the whole system. The learning algorithms operate only locally within each block. The framework is demonstrated in this paper in a three-layer structure applied to images extracted from a standard data set.Comment: Submitted for journal publicatio

Acausality and the Machian Mind

In this paper we propose a mechanism in the brain for supporting consciousness. We leave open the question of the origin of consciousness itself, although an acausal origin is suggested since it should mesh with the proposed quasi-acausal network dynamics.  In particular, we propose simply that fixed-point attractors, such as exemplified by the simple deterministic Hopfield network, correspond to conscious moments.  In a sort of dual to Tononi's Integrated Information Theory, we suggest that the "main experience" corresponds to a dominant fixed point that incorporates sub-networks that span the brain and maximizes "relatedness." The dynamics around the dominant fixed point correspond in some parts of the system to associative memory dynamics, and to more binding constraint satisfaction dynamics in other areas. Since the memories that we are familiar with appear to have a conscious origin, it makes sense that a conscious moment itself corresponds in effect to what amounts to memory recollection.  Furthermore, since Hopfield-like networks are generative, a conscious moment can in effect be seen as a living, partially predicted memory. Another primary motivation for this approach is that alternative states can be naturally sensed, or contrasted, at the fixed points

Learning in clustered spiking networks

Neurons spike on a millisecond time scale while behaviour typically spans hundreds of milliseconds to seconds and longer. Neurons have to bridge this time gap when computing and learning behaviours of interest. Recent computational work has shown that neural circuits can bridge this time gap when connected in specific ways. Moreover, the connectivity patterns can develop using plasticity rules typically considered to be biologically plausible. In this thesis, we focus on one type of connectivity where excitatory neurons are grouped in clusters. Strong recurrent connectivity within the clusters reverberates the activity and prolongs the time scales in the network. This way, the clusters of neurons become the basic functional units of the circuit, in line with an increasing number of experimental studies. We study a general architecture where plastic synapses connect the clustered network to a read-out network. We demonstrate the usefulness of this architecture for two different problems: 1) learning and replaying sequences; 2) learning statistical structure. The time scales in both problems range from hundreds of milliseconds to seconds and we address the problems through simulation and analysis of spiking networks. We show that the clustered organization circumvents the need for non-bio-plausible mathematical optimizations and instead allows the use of unsupervised spike-timing-dependent plasticity rules. Additionally, we make qualitative links to experimental findings and predictions for both problems studied. Finally, we speculate about future directions that could extend upon our findings.Open Acces

A Comprehensive Workflow for General-Purpose Neural Modeling with Highly Configurable Neuromorphic Hardware Systems

In this paper we present a methodological framework that meets novel requirements emerging from upcoming types of accelerated and highly configurable neuromorphic hardware systems. We describe in detail a device with 45 million programmable and dynamic synapses that is currently under development, and we sketch the conceptual challenges that arise from taking this platform into operation. More specifically, we aim at the establishment of this neuromorphic system as a flexible and neuroscientifically valuable modeling tool that can be used by non-hardware-experts. We consider various functional aspects to be crucial for this purpose, and we introduce a consistent workflow with detailed descriptions of all involved modules that implement the suggested steps: The integration of the hardware interface into the simulator-independent model description language PyNN; a fully automated translation between the PyNN domain and appropriate hardware configurations; an executable specification of the future neuromorphic system that can be seamlessly integrated into this biology-to-hardware mapping process as a test bench for all software layers and possible hardware design modifications; an evaluation scheme that deploys models from a dedicated benchmark library, compares the results generated by virtual or prototype hardware devices with reference software simulations and analyzes the differences. The integration of these components into one hardware-software workflow provides an ecosystem for ongoing preparative studies that support the hardware design process and represents the basis for the maturity of the model-to-hardware mapping software. The functionality and flexibility of the latter is proven with a variety of experimental results

A hierarchical system for a distributed representation of the peripersonal space of a humanoid robot

Reaching a target object in an unknown and unstructured environment is easily performed by human beings. However, designing a humanoid robot that executes the same task requires the implementation of complex abilities, such as identifying the target in the visual field, estimating its spatial location, and precisely driving the motors of the arm to reach it. While research usually tackles the development of such abilities singularly, in this work we integrate a number of computational models into a unified framework, and demonstrate in a humanoid torso the feasibility of an integrated working representation of its peripersonal space. To achieve this goal, we propose a cognitive architecture that connects several models inspired by neural circuits of the visual, frontal and posterior parietal cortices of the brain. The outcome of the integration process is a system that allows the robot to create its internal model and its representation of the surrounding space by interacting with the environment directly, through a mutual adaptation of perception and action. The robot is eventually capable of executing a set of tasks, such as recognizing, gazing and reaching target objects, which can work separately or cooperate for supporting more structured and effective behaviors

Data-driven neural mass modelling

The brain is a complex organ whose activity spans multiple scales, both spatial and temporal. The computational unit of the brain is thought to be the neurone. At the microscopic level, neurones communicate via action potentials. These may be observed experimentally by means of precise techniques that work with a small number of these cells and their interactions, and that can be modelled mathematically in a variety of ways. Other techniques consider the averaged activity of large groups of neurones in the mesoscale, or cortical columns; theoretical models of these signals also abound. The problem of relating the microscopic scale to the mesoscopic is not trivial. Analytical derivations of mesoscopic models are based on assumptions that are not always justified. Also, traditionally there has been a separation between the clinically oriented analysts that process neural signals for medical purposes and the theoretical modelling community. This Thesis aims to lay bridges both between the microscopic and mesoscopic scales of brain activity, and between the experimental and theoretical angles of its study. This is achieved via the unscented Kalman filter (UKF), which allows us to combine knowledge from different sources (microscopic/mesoscopic and experimental/theoretical). The outcome is a better understanding of the system than each of the sources of information could provide separately. The Thesis is organised as follows. Chapter 1 is a brief reflection on the current methodology in Science and its underlying motivations. This is followed by chapters 2 to 4, which introduce and contextualise the concepts discussed in the remainder of the work. Chapter 5 tackles the interrelationship of the microscopic and mesoscopic scales. Although efforts have been made to derive mesoscopic equations from models of microscopic networks, they are based on assumptions that may not always hold. We use the UKF to assimilate the output of microscopic networks into a mesoscopic model and study a variety of dynamical situations. Our results show that using the Kalman filter compensates for the loss of information that is common in analytical derivations. Chapters 6 and 7 address the combination of experimental data with neural mass models. More specifically, we extend Jansen and Rit's model of a cortical column with a model of the head, which allows us to use electroencephalography (EEG) data. With this, we estimate the state of the system and a relevant parameter of choice. In chapter 6 we use in silico data to test the UKF under a variety of dynamical conditions, comparing simulated intracranial data with simulated EEG. Extracranial estimation is always superior in speed and quality to intracortical estimation, even though intracortical electrodes are closer to the source of activity than extracranial electrodes. We suggest that this is due to the more complete picture of the cortex that is visible with the set of extracranial electrodes. Chapter 7 feeds experimental EEG data of an epileptic patient into Jansen and Rit's model; the goal is to estimate a parameter that governs the dynamical behaviour of the system, again with the UKF. The estimation of the state closely follows the experimental data, while the parameter shows sensitivity to the changes in brain regimes, especially seizures. These results show promise for using data assimilation to address some shortcomings of brain modelling techniques. On the one hand, the mutual influence of neural structures at the microscopic and the mesoscopic scales may become better characterised, by means of filtering approaches that bypass analytical limitations. On the other hand, fusing experimental EEG data with mathematical models of the brain may enable us to determine the underlying dynamics of observed physiological signals, and at the same time to improve our models with patient-specific information. The potential of these enhanced algorithms spans a wide range of brain-related applications.El cervell humà és un òrgan de gran complexitat l’activitat del qual es desenvolupa en múltiples escales, tant espacials com temporals. Es creu que la unitat computacional del cervell és la neurona, una cèl·lula altament especialitzada que té com a funció rebre, processar i transmetre informació. A nivell microscòpic, les neurones es comuniquen les unes amb les altres per potencials d’acció. Aquests es poden observar experimentalment “in vivo” per mitjà de tècniques de gran precisió que només poden tenir en compte un nombre relativament reduït de cèl·lules i interaccions, i que es poden modelar matemàticament de diverses maneres. Altres tècniques tracten amb grans grups de neurones a escala mesoscòpica, o columnes corticals, i detecten l’activitat mitjana de la població neuronal; en aquest cas també abunden els models teòrics que intenten reproduir aquests senyals. Malgrat que està ben establert que hi ha una intercomunicació entre les escales microscòpica i mesoscòpica, relacionar una escala amb una altra no és gens trivial. Les derivacions analítiques de models mesoscòpics a partir de xarxes microscòpiques es basen en suposicions que no sempre es poden justificar. A part, tradicionalment hi ha hagut una frontera de separació entre els analistes clínics que processen senyals neuronals amb fins mèdics (i que sovint usen tècniques molt invasives i/o costoses), i la comunitat teòrica que modelitza aquests senyals, per a qui el repte més gran és caracteritzar els paràmetres que governen els models perquè aquests s’acostin el més possible a la realitat. Aquesta Tesi té com a objectiu, per una banda, fer un pas més a caracteritzar la relació entre les escales microscòpica i mesoscòpica d’activitat cerebral, i, per l’altra, establir ponts entre els punts de vista experimental i teòric del seu estudi. Ho aconseguim amb un algoritme d’assimilació de dades, el filtre de Kalman desodorat (UKF, de les sigles en anglès), que ens permet combinar informació de diverses procedències (microscòpica/mesoscòpica o experimental/teòrica). El resultat és una comprensió més àmplia del sistema estudiat que la que haurien permès les fonts d’informació per separat. La Tesi està organitzada de la següent manera. El capítol 1 comença amb una breu reflexió sobre la metodologia científica actual i les seves motivacions subjacents (segons l’autora). El segueixen els capítols del 2 al 4, que introdueixen i posen en context els conceptes que s’exposen a la resta del treball. El capítol 5 aborda el problema de la relació entre l’escala microscòpica i la mesoscòpica. Tot i que existeixen diverses derivacions d’equacions mesoscòpiques partint de models de xarxes neuronals, sovint es basen en suposicions fràgils que no es compleixen en situacions més complicades. Aquí utilitzem l’UKF per assimilar la sortida de xarxes microscòpiques en un model mesoscòpic simple i estudiar diverses situacions dinàmiques. Els resultats mostren que la manera que el filtre de Kalman gestiona les incerteses del model compensa les pèrdues d’informació pròpies de les derivacions analítiques de models mesoscòpics. Els capítols 6 i 7 tracten la combinació de dades experimentals del cervell amb models de masses neurals que descriuen la dinàmica de grups de neurones. Concretament, estenem el model de Jansen i Rit d’una columna cortical amb un model del cap, el qual ens permet fer servir dades extracranials no invasives. Amb això estimem l’estat del sistema i un paràmetre d’interès de possible rellevància en l’estudi clínic d’afeccions com l’epilèpsia. En el capítol 6 fem servir dades “in silico” per provar l’UKF en diversos escenaris dinàmics: conjunts de paràmetres que causen comportaments diferents en les columnes corticals, diferents nivells de soroll de mesura i dues modalitats de transmissió d’informació; tot això comparant dades intracranials simulades amb simulacions d’electroencefalogrames (EEG). En totes les situacions estudiades, l’estimació extracranial és sempre superior, en velocitat i precisió, a l’estimació intracortical, encara que els elèctrodes intracorticals són molt més propers a la font de l’activitat que els elèctrodes de la superfície cranial. Suggerim que això pot ser causat per la visió més completa del còrtex que es pot obtenir amb el conjunt d’elèctrodes extracranials. Aquesta idea ve reforçada pels resultats observats amb elèctrodes extracranials individuals treballant de manera independent, que apunten a la sensibilitat espacial de les mesures. En el capítol 7 alimentem el model de Jansen i Rit amb dades experimentals de l’EEG d’un pacient epilèptic; l’objectiu és estimar un paràmetre significatiu que governa l’evolució dinàmica del sistema, de nou amb l’UKF. L’estimació de l’estat és precisa i el paràmetre es veu afectat pels canvis de règim, especialment (però no exclusivament) per les convulsions. Aquests resultats són prometedors a l’hora d’utilitzar l’assimilació de dades per superar les diverses carències de les tècniques de modelització cerebral. Per una banda, la influència mútua entre estructures a escala microscòpica i a escala mesoscòpica es pot caracteritzar millor, gràcies a tècniques de filtrat que permeten esquivar les habituals limitacions analítiques. Això dóna com a resultat una millor comprensió de l’estructura i funció cerebrals. Per una altra banda, fusionar dades experimentals d’EEG amb els models matemàtics del cervell existents ens pot permetre determinar les dinàmiques subjacents dels senyals fisiològics que tenim disponibles, a la vegada que millorem els nostres models amb informació individual de cada pacient. Aquests algoritmes augmentats tenen potencial per a un ampli espectre d’aplicacions en el camp de les neurociències, des d’interfícies cervell/ordinador fins a tota mena d’usos en medicina personalitzada com el diagnòstic precoç de malalties neurodegeneratives, la predicció de crisis convulsives o la monitorització de la rehabilitació postisquèmica o posttraumàtica, entre molts altres.Postprint (published version

A hierarchical system for a distributed representation of the peripersonal space of a humanoid robot

Reaching a target object in an unknown and unstructured environment is easily performed by human beings. However, designing a humanoid robot that executes the same task requires the implementation of complex abilities, such as identifying the target in the visual field, estimating its spatial location, and precisely driving the motors of the arm to reach it. While research usually tackles the development of such abilities singularly, in this work we integrate a number of computational models into a unified framework, and demonstrate in a humanoid torso the feasibility of an integrated working representation of its peripersonal space. To achieve this goal, we propose a cognitive architecture that connects several models inspired by neural circuits of the visual, frontal and posterior parietal cortices of the brain. The outcome of the integration process is a system that allows the robot to create its internal model and its representation of the surrounding space by interacting with the environment directly, through a mutual adaptation of perception and action. The robot is eventually capable of executing a set of tasks, such as recognizing, gazing and reaching target objects, which can work separately or cooperate for supporting more structured and effective behaviors