223 research outputs found

    Adaptive learning in a compartmental model of visual cortexñ€”how feedback enables stable category learning and refinement

    Get PDF
    The categorization of real world objects is often reflected in the similarity of their visual appearances. Such categories of objects do not necessarily form disjunct sets of objects, neither semantically nor visually. The relationship between categories can often be described in terms of a hierarchical structure. For instance, tigers and leopards build two separate mammalian categories, but both belong to the category of felines. In other words, tigers and leopards are subcategories of the category Felidae. In the last decades, the unsupervised learning of categories of visual input stimuli has been addressed by numerous approaches in machine learning as well as in the computational neurosciences. However, the question of what kind of mechanisms might be involved in the process of subcategory learning, or category refinement, remains a topic of active investigation. We propose a recurrent computational network architecture for the unsupervised learning of categorial and subcategorial visual input representations. During learning, the connection strengths of bottom-up weights from input to higher-level category representations are adapted according to the input activity distribution. In a similar manner, top-down weights learn to encode the characteristics of a specific stimulus category. Feedforward and feedback learning in combination realize an associative memory mechanism, enabling the selective top-down propagation of a category's feedback weight distribution. We suggest that the difference between the expected input encoded in the projective field of a category node and the current input pattern controls the amplification of feedforward-driven representations. Large enough differences trigger the recruitment of new representational resources and the establishment of (sub-) category representations. We demonstrate the temporal evolution of such learning and show how the approach successully establishes category and subcategory representations

    The Complementary Brain: From Brain Dynamics To Conscious Experiences

    Full text link
    How do our brains so effectively achieve adaptive behavior in a changing world? Evidence is reviewed that brains are organized into parallel processing streams with complementary properties. Hierarchical interactions within each stream and parallel interactions between streams create coherent behavioral representations that overcome the complementary deficiencies of each stream and support unitary conscious experiences. This perspective suggests how brain design reflects the organization of the physical world with which brains interact, and suggests an alternative to the computer metaphor suggesting that brains are organized into independent modules. Examples from perception, learning, cognition, and action are described, and theoretical concepts and mechanisms by which complementarity is accomplished are summarized.Defense Advanced Research Projects and the Office of Naval Research (N00014-95-1-0409); National Science Foundation (ITI-97-20333); Office of Naval Research (N00014-95-1-0657

    A model for cerebral cortical neuron group electric activity and its implications for cerebral function

    Get PDF
    Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.Includes bibliographical references (p. 245-265).The electroencephalogram, or EEG, is a recording of the field potential generated by the electric activity of neuronal populations of the brain. Its utility has long been recognized as a monitor which reflects the vigilance states of the brain, such as arousal, drowsiness, and sleep stages. Moreover, it is used to detect pathological conditions such as seizures, to calibrate drug action during anesthesia, and to understand cognitive task signatures in healthy and abnormal subjects. Being an aggregate measure of neural activity, understanding the neural origins of EEG oscillations has been limited. With the advent of recording techniques, however, and as an influx of experimental evidence on cellular and network properties of the neocortex has become available, a closer look into the neuronal mechanisms for EEG generation is warranted. Accordingly, we introduce an effective neuronal skeleton circuit at a neuronal group level which could reproduce basic EEG-observable slow ( 3mm). The effective circuit makes use of the dynamic properties of the layer 5 network to explain intra-cortically generated augmenting responses, restful alpha, slow wave (< 1Hz) oscillations, and disinhibition-induced seizures. Based on recent cellular evidence, we propose a hierarchical binding mechanism in tufted layer 5 cells which acts as a controlled gate between local cortical activity and inputs arriving from distant cortical areas. This gate is manifested by the switch in output firing patterns in tufted(cont.) layer 5 cells between burst firing and regular spiking, with specific implications on local functional connectivity. This hypothesized mechanism provides an explanation of different alpha band (10Hz) oscillations observed recently under cognitive states. In particular, evoked alpha rhythms, which occur transiently after an input stimulus, could account for initial reogranization of local neural activity based on (mis)match between driving inputs and modulatory feedback of higher order cortical structures, or internal expectations. Emitted alpha rhythms, on the other hand, is an example of extreme attention where dominance of higher order control inputs could drive reorganization of local cortical activity. Finally, the model makes predictions on the role of burst firing patterns in tufted layer 5 cells in redefining local cortical dynamics, based on internal representations, as a prelude to high frequency oscillations observed in various sensory systems during cognition.by Fadi Nabih Karameh.Ph.D

    Columnar Connectivity and Laminar Processing in Cat Primary Auditory Cortex

    Get PDF
    Radial intra- and interlaminar connections form a basic microcircuit in primary auditory cortex (AI) that extracts acoustic information and distributes it to cortical and subcortical networks. Though the structure of this microcircuit is known, we do not know how the functional connectivity between layers relates to laminar processing.We studied the relationships between functional connectivity and receptive field properties in this columnar microcircuit by simultaneously recording from single neurons in cat AI in response to broadband dynamic moving ripple stimuli. We used spectrotemporal receptive fields (STRFs) to estimate the relationship between receptive field parameters and the functional connectivity between pairs of neurons. Interlaminar connectivity obtained through cross-covariance analysis reflected a consistent pattern of information flow from thalamic input layers to cortical output layers. Connection strength and STRF similarity were greatest for intralaminar neuron pairs and in supragranular layers and weaker for interlaminar projections. Interlaminar connection strength co-varied with several STRF parameters: feature selectivity, phase locking to the stimulus envelope, best temporal modulation frequency, and best spectral modulation frequency. Connectivity properties and receptive field relationships differed for vertical and horizontal connections.Thus, the mode of local processing in supragranular layers differs from that in infragranular layers. Therefore, specific connectivity patterns in the auditory cortex shape the flow of information and constrain how spectrotemporal processing transformations progress in the canonical columnar auditory microcircuit

    Laminar-specific cortico-cortical loops in mouse visual cortex

    Get PDF
    "Muitas teorias propÔem interacçÔes recorrentes através da hierarquia cortical, mas não é claro se os circuitos corticais são selectivamente ligados para implementar cålculos em ciclo. Usando o mapeamento de circuitos subcelulares do método de canal de rodopsina 2 assistido no córtex visual do rato, comparamos a entrada sinåptica de alimentação direta (feedforward, FF) ou retroalimentação (feedback, FB) cortico-cortical (CC) às células que se projectam de volta à fonte de entrada (neurónios em ciclo) com células que se projectam para uma årea cortical ou subcortical diferente.(...)

    How models of canonical microcircuits implement cognitive functions

    Get PDF
    Major cognitive functions such as language, memory, and decision-making are thought to rely on distributed networks of a large number of fundamental neural elements, called canonical microcircuits. A mechanistic understanding of the interaction of these canonical microcircuits promises a better comprehension of cognitive functions as well as their potential disorders and corresponding treatment techniques. This thesis establishes a generative modeling framework that rests on canonical microcircuits and employs it to investigate composite mechanisms of cognitive functions. A generic, biologically plausible neural mass model was derived to parsimoniously represent conceivable architectures of canonical microcircuits. Time domain simulations and bifurcation and stability analyses were used to evaluate the model’s capability for basic information processing operations in response to transient stimulations, namely signal flow gating and working memory. Analysis shows that these basic operations rest upon the bistable activity of a neural population and the selectivity for the stimulus’ intensity and temporal consistency and transiency. In the model’s state space, this selectivity is marked by the distance of the system’s working point to a saddle-node bifurcation and the existence of a Hopf separatrix. The local network balance, in regard of synaptic gains, is shown to modify the model’s state space and thus its operational repertoire. Among the investigated architectures, only a three-population model that separates input-receiving and output-emitting excitatory populations exhibits the necessary state space characteristics. It is thus specified as minimal canonical microcircuit. In this three-population model, facilitative feedback information modifies the retention of sensory feedforward information. Consequently, meta-circuits of two hierarchically interacting minimal canonical microcircuits feature a temporal processing history that enables state-dependent processing operations. The relevance of these composite operations is demonstrated for the neural operations of priming and structure-building. Structure-building, that is the sequential and selective activation of neural circuits, is identified as an essential mechanism in a neural network for syntax parsing. This insight into cognitive processing proves the modeling framework’s potential in neurocognitive research. This thesis substantiates the connectionist notion that higher processing operations emerge from the combination of minimal processing elements and advances the understanding how cognitive functions are implemented in the neocortical matter of the brain.Kognitive FĂ€higkeiten wie Sprache, GedĂ€chtnis und Entscheidungsfindung resultieren vermutlich aus der Interaktion vieler fundamentaler neuronaler Elemente, sogenannter kanonischer Schaltkreise. Eine vertiefte Einsicht in das Zusammenwirken dieser kanonischen Schaltkreise verspricht ein besseres VerstĂ€ndnis kognitiver FĂ€higkeiten, möglicher Funktionsstörungen und TherapieansĂ€tze. Die vorliegende Dissertation untersucht ein generatives Modell kanonischer Schaltkreise und erforscht mit dessen Hilfe die Zusammensetzung kognitiver FĂ€higkeiten aus konstitutiven Mechanismen neuronaler Interaktion. Es wurde ein biologisch-plausibles neuronales Massenmodell erstellt, das mögliche Architekturen kanonischer Schaltkreise generisch berĂŒcksichtigt. Anhand von Simulationen sowie Bifurkations- und StabilitĂ€tsanalysen wurde untersucht, inwiefern das Modell grundlegende Operationen der Informationsverarbeitung, nĂ€mlich Selektion und temporĂ€re Speicherung einer transienten Stimulation, unterstĂŒtzt. Die Untersuchung zeigt, dass eine bistabile AktivitĂ€t einer neuronalen Population und die Beurteilung der Salienz des Signals den grundlegenden Operationen zugrunde liegen. Die Beurteilung der Salienz beruht dabei hinsichtlich der SignalstĂ€rke auf dem Abstand des Arbeitspunktes zu einer Sattel-Knoten-Bifurkation und hinsichtlich der Signalkonsistenz und-–vergĂ€nglichkeit auf einer Hopf-Separatrix im Zustandsraum des Systems. Die Netzwerkbalance modifiziert diesen Zustandsraum und damit die FunktionsfĂ€higkeit des Modells. Nur ein Drei-Populationenmodell mit getrennten erregenden Populationen fĂŒr Signalempfang und -emission weist die notwendigen Bedingungen im Zustandsraum auf und genĂŒgt der Definition eines minimalen kanonischen Schaltkreises. In diesem Drei-Populationenmodell erleichtert ein Feedbacksignal die SpeicherfĂ€higkeit fĂŒr sensorische Feedforwardsignale. Dementsprechend weisen hierarchisch interagierende minimale kanonische Schaltkreise ein zeitliches VerarbeitungsgedĂ€chtnis auf, das zustandsabhĂ€ngige Verarbeitungsoperationen erlaubt. Die Bedeutung dieser konstitutiven Operationen wird fĂŒr die neuronalen Operationen Priming und Strukturbildung verdeutlicht. Letztere wurde als wichtiger Mechanismus in einem Netzwerk zur Syntaxanalyse identifiziert und belegt das Potential des Modellansatzes fĂŒr die neurokognitive Forschung. Diese Dissertation konkretisiert die konnektionistische Ansicht höhere Verarbeitungsoperationen als Ergebnis der Kombination minimaler Verarbeitungselemente zu verstehen und befördert das VerstĂ€ndnis fĂŒr die Frage wie kognitive FĂ€higkeiten im Nervengewebe des Gehirns implementiert sind

    The Complementary Brain: A Unifying View of Brain Specialization and Modularity

    Full text link
    Defense Advanced Research Projects Agency and Office of Naval Research (N00014-95-I-0409); National Science Foundation (ITI-97-20333); Office of Naval Research (N00014-95-I-0657

    Bio-Inspired Computer Vision: Towards a Synergistic Approach of Artificial and Biological Vision

    Get PDF
    To appear in CVIUStudies in biological vision have always been a great source of inspiration for design of computer vision algorithms. In the past, several successful methods were designed with varying degrees of correspondence with biological vision studies, ranging from purely functional inspiration to methods that utilise models that were primarily developed for explaining biological observations. Even though it seems well recognised that computational models of biological vision can help in design of computer vision algorithms, it is a non-trivial exercise for a computer vision researcher to mine relevant information from biological vision literature as very few studies in biology are organised at a task level. In this paper we aim to bridge this gap by providing a computer vision task centric presentation of models primarily originating in biological vision studies. Not only do we revisit some of the main features of biological vision and discuss the foundations of existing computational studies modelling biological vision, but also we consider three classical computer vision tasks from a biological perspective: image sensing, segmentation and optical flow. Using this task-centric approach, we discuss well-known biological functional principles and compare them with approaches taken by computer vision. Based on this comparative analysis of computer and biological vision, we present some recent models in biological vision and highlight a few models that we think are promising for future investigations in computer vision. To this extent, this paper provides new insights and a starting point for investigators interested in the design of biology-based computer vision algorithms and pave a way for much needed interaction between the two communities leading to the development of synergistic models of artificial and biological vision

    Functional connectivity and dendritic integration of feedback in visual cortex

    Get PDF
    A fundamental question in neuroscience is how different brain regions communicate with each other. Sensory processing engages distributed circuits across many brain areas and involves information flow in the feedforward and feedback direction. While feedforward processing is conceptually well understood, feedback processing has remained mysterious. Cortico-cortical feedback axons are enriched in layer 1, where they form synapses with the apical dendrites of pyramidal neurons. The organization and dendritic integration of information conveyed by these axons, however, are unknown. This thesis describes my efforts to link the circuit-level and dendritic-level organization of cortico-cortical feedback in the mouse visual system. First, using cellular resolution all-optical interrogation across cortical areas, I characterized the functional connectivity between the lateromedial higher visual area (LM) and primary visual cortex (V1). Feedback influence had both facilitating and suppressive effects on visually-evoked activity in V1 neurons, and was spatially organized: retinotopically aligned feedback was relatively more suppressive, while retinotopically offset feedback was relatively more facilitating. Second, to examine how feedback inputs are integrated in apical dendrites, I optogenetically stimulated presynaptic neurons in LM while using 2-photon calcium imaging to map feedback-recipient spines in the apical tufts of layer 5 neurons in V1. Activation of a single feedback-providing input was sufficient to boost calcium signals and recruit branch-specific local events in the recipient dendrite, suggesting that feedback can engage dendritic nonlinearities directly. Finally, I measured the recruitment of apical dendrites during visual stimulus processing. Surround visual stimuli, which should recruit relatively more facilitating feedback, drove local calcium events in apical tuft branches. Moreover, global dendritic event size was not purely determined by somatic activity but modulated by visual stimuli and behavioural state, in a manner consistent with the spatial organization of feedback. In summary, these results point toward a possible involvement of active dendritic processing in the integration of feedback signals. Active dendrites could thus provide a biophysical substrate for the integration of essential top-down information streams, including contextual or predictive processing
    • 

    corecore