Localist models of spreading activation (SA) and models assuming distributed representations offer very different takes on semantic priming, a widely investigated paradigm in word recognition and semantic memory research. In this study, we implemented SA in an attractor neural network model with distributed representations and created a unified framework for the two approaches. Our models assume a synaptic depression mechanism leading to autonomous transitions between encoded memory patterns (latching dynamics), which account for the major characteristics of automatic semantic priming (...) in humans. Using computer simulations, we demonstrated how findings that challenged attractor‐based networks in the past, such as mediated and asymmetric priming, are a natural consequence of our present model’s dynamics. Puzzling results regarding backward priming were also given a straightforward explanation. In addition, the current model addresses some of the differences between semantic and associative relatedness and explains how these differences interact with stimulus onset asynchrony in priming experiments. (shrink)

The structure of people’s conceptual knowledge of concrete nouns has traditionally been viewed as hierarchical (Collins & Quillian, 1969). For example, superordinate concepts (vegetable) are assumed to reside at a higher level than basic‐level concepts (carrot). A feature‐based attractor network with a single layer of semantic features developed representations of both basic‐level and superordinate concepts. No hierarchical structure was built into the network. In Experiment and Simulation 1, the graded structure of categories (typicality ratings) is accounted for by the (...) flat attractor network. Experiment and Simulation 2 show that, as with basic‐level concepts, such a network predicts feature verification latencies for superordinate concepts (vegetable ). In Experiment and Simulation 3, counterintuitive results regarding the temporal dynamics of similarity in semantic priming are explained by the model. By treating both types of concepts the same in terms of representation, learning, and computations, the model provides new insights into semantic memory. (shrink)

Semantic priming has long been recognized to reflect, along with automatic semantic mechanisms, the contribution of controlled strategies. However, previous theories of controlled priming were mostly qualitative, lacking common grounds with modern mathematical models of automatic priming based on neural networks. Recently, we introduced a novel attractor network model of automatic semantic priming with latching dynamics. Here, we extend this work to show how the same model can also account for important findings regarding controlled processes. Assuming the rate (...) of semantic transitions in the network can be adapted using simple reinforcement learning, we show how basic findings attributed to controlled processes in priming can be achieved, including their dependency on stimulus onset asynchrony and relatedness proportion and their unique effect on associative, category-exemplar, mediated and backward prime-target relations. We discuss how our mechanism relates to the classic expectancy theory and how it can be further extended in future developments of the model. (shrink)

Buddhist philosophy asserts that human suffering is caused by ignorance regarding the true nature of reality. According to this, perceptions and thoughts are largely fabrications of our own minds, based on conditioned tendencies which often involve problematic fears, aversions, compulsions, etc. In Buddhist psychology, these tendencies reside in a portion of mind known as Store consciousness. Here, I suggest a correspondence between this Buddhist Store consciousness and the neuroscientific idea of stored synaptic weights. These weights are strong synaptic connections built (...) in through experience. Buddhist philosophy claims that humans can find relief from suffering through a process in which the Store consciousness is transformed. Here, I argue that this Buddhist 'transformation at the base' corresponds to a loosening of the learned synaptic connections. I will argue that Buddhist meditation practices create conditions in the brain which are optimal for diminishing the strength of our conditioned perceptual and behavioural tendencies. (shrink)

This article proposes complementing actor-network theory (ANT) with Niklas Luhmann’s communication theory, in order to overcome one of ANT’s major shortcomings, namely, the lack of a conceptual repertoire to describe virtual processes such as sense-making. A highly problematic consequence of ANT’s actualism is that it cannot explain the differentiation of economic, legal, scientific, touristic, religious, medical, artistic, political and other qualities of actual entities, assemblages and relationships. By recasting Luhmann’s theory of functionally differentiated communication forms and sense-making as dealing with (...) different types of virtual attractors calling for actualizations in concrete assemblages, I propose a symmetrical understanding of societal differentiation processes as based on the co-production of virtual attractors and actual assemblages. (shrink)

Further tests of Amit's model are indicated. One strategy is to use the apparent coding sparseness of the model to make predictions about coding sparseness in Miyashita's network. A second approach is to use memory overload to induce false positive responses in modules and biological systems. In closing, the importance of temporal coding and timing requirements in developing biologically plausible attractor networks is mentioned.

Head direction (HD) cells, abundant in the rat postsubiculum and anterior thalamic nuclei, fire maximally when the rat’s head is facing a particular direction. The activity of a population of these cells forms a distributed representation of the animal’s current heading. We describe a neural network model that creates a stable, distributed representation of head direction and updates that representation in response to angular velocity information. In contrast to earlier models, our model of the head direction system accurately tracks a (...) series of actual rat head rotations, and, using biologically plausible neurons, it fits the single-cell tuning curves of real HD cells recorded from rats executing those same rotations. The model makes neurophysiological predictions that can be tested using current technologies. (shrink)

Multi-level institutional designs with distributed power and authority arrangements among federal, state, regional, and local government agencies could lead to the emergence of differential patterns of socioeconomic and infrastructure development pathways in complex social–ecological systems. Both exogenous drivers and endogenous processes in social–ecological systems can lead to changes in the number of “basins of attraction,” changes in the positions of the basins within the state space, and changes in the positions of the thresholds between basins. In an effort to advance (...) the theory and practice of the governance of policy systems, this study addresses a narrower empirical question: how do intergovernmental institutional rules set by federal, state, and regional government agencies generate and sustain basins of attraction in funding infrastructure projects? A pattern-oriented, agent-based model (ABM) of an intergovernmental network has been developed to simulate real-world transportation policy implementation processes across the federal, the state of Vermont, regional, and local governments for prioritizing transportation projects. The ABM simulates baseline and alternative intergovernmental institutional rule structures and assesses their impacts on financial investment flows. The ABM was calibrated with data from multiple focus groups, individual interviews, and analysis of federal, state, and regional scale transportation projects and programs. The results from experimental simulations are presented to test system-wide effects of alternative multi-level institutional designs, in particular different power and authority arrangements between state and regional governments, on the emergence of roadway project prioritization patterns and funding allocations across regions and towns. (shrink)

I hypothesize that re-occurring prior experience of complex systems mobilizes a fast response, whose attractor is encoded by their strongly connected network core. In contrast, responses to novel stimuli are often slow and require the weakly connected network periphery. Upon repeated stimulus, peripheral network nodes remodel the network core that encodes the attractor of the new response. This “core-periphery learning” theory reviews and generalizes the heretofore fragmented knowledge on attractor formation by neural networks, periphery-driven innovation, and (...) a number of recent reports on the adaptation of protein, neuronal, and social networks. The core-periphery learning theory may increase our understanding of signaling, memory formation, information encoding and decision-making processes. Moreover, the power of network periphery-related “wisdom of crowds” inventing creative, novel responses indicates that deliberative democracy is a slow yet efficient learning strategy developed as the success of a billion-year evolution. Also see the video abstract here: https://youtu.be/IIjP7zWGjVE. The network core triggers fast responses to previously experienced stimuli. Innovations require the network periphery that encodes a novel attractor by core-remodeling. This core-periphery learning theory is introduced as a general adaptation, learning, decision-making, and forgetting mechanism of all complex systems including protein, metabolic, signaling, neuronal, social networks, and ecosystems. (shrink)

Studies have failed to yield definitive evidence for the existence and/or role of well-defined chaotic attractors in real brain systems. Tsuda's transients stabilized on unstable manifolds of unstable fixed points using mechanisms similar to Ott's algorithmic “control of chaos” are demonstrable. Grebogi's order in preserving “strange nonchaotic” attractor with fractal dimension but Lyapounov is suggested for neural network tasks dependent on sequence.

We propose a neural model of multiattribute-decision processes, based on an attractor neural network with dynamic thresholds. The model may be viewed as a generalization of the elimination by aspects model, whereby simultaneous selection of several aspects is allowed. Depending on the amount of synaptic inhibition, various kinds of scanning strategies may be performed, leading in some cases to vacillations among the alternatives. The model predicts that decisions of a longer time duration exhibit a lower violation of the simple (...) scalability law, as opposed to shorter decisions. Furthermore, the model is suggested as a general attribute-based decision module. Accordingly, various decision strategies are manifested depending on the module's parameters. (shrink)

There is some complementarity of models for the origin of the electroencephalogram (EEG) and neural network models for information storage in brainlike systems. From the EEG models of Freeman, of Nunez, and of the authors' group we argue that the wavelike processes revealed in the EEG exhibit linear and near-equilibrium dynamics at macroscopic scale, despite extremely nonlinear – probably chaotic – dynamics at microscopic scale. Simulations of cortical neuronal interactions at global and microscopic scales are then presented. The simulations depend (...) on anatomical and physiological estimates of synaptic densities, coupling symmetries, synaptic gain, dendritic time constants, and axonal delays. It is shown that the frequency content, wave velocities, frequency/wavenumber spectra and response to cortical activation of the electrocorticogram (ECoG) can be reproduced by a “lumped” simulation treating small cortical areas as single-function units. The corresponding cellular neural network simulation has properties that include those of attractor neural networks proposed by Amit and by Parisi. Within the simulations at both scales, sharp transitions occur between low and high cell firing rates. These transitions may form a basis for neural interactions across scale. To maintain overall cortical dynamics in the normal low firing-rate range, interactions between the cortex and the subcortical systems are required to prevent runaway global excitation. Thus, the interaction of cortex and subcortex via corticostriatal and related pathways may partly regulate global dynamics by a principle analogous to adiabatic control of artificial neural networks. (shrink)

A neural network is a model of the brain’s cognitive process, with a highly interconnected multiprocessor architecture. The neural network has incredible potential, in the view of these artificial neural networks inherently having good learning capabilities and the ability to learn different input features. Based on this, this paper proposes a new chaotic neuron model and a new chaotic neural network model. It includes a linear matrix, a sine function, and a chaotic neural network composed of three chaotic neurons. (...) One of the chaotic neurons is affected by the sine function. The network has rich chaotic dynamics and can produce multiscroll hidden chaotic attractors. This paper studied its dynamic behaviors, including bifurcation behavior, Lyapunov exponent, Poincaré surface of section, and basins of attraction. In the process of analyzing the bifurcation and the basins of attraction, it was found that the network demonstrated hidden bifurcation phenomena, and the relevant properties of the basins of attraction were obtained. Thereafter, a chaotic neural network was implemented by using FPGA, and the experiment proved that the theoretical analysis results and FPGA implementation were consistent with each other. Finally, an energy function was constructed to optimize the calculation based on the CNN in order to provide a new approach to solve the TSP problem. (shrink)

The concept of attractors is considered critical in the study of dynamical systems as they represent the set of states that a system gravitates toward. However, it is generally difficult to analyze attractors in complex systems due to multiple reasons including chaos, high-dimensionality, and stochasticity. This paper explores a novel approach to analyzing attractors in complex systems by utilizing networks to represent phase spaces. We accomplish this by discretizing phase space and defining node associations with attractors by finding sink (...) strongly connected components within these networks. Moreover, the network representation of phase space facilitates the use of well-established techniques of network analysis to study the phase space of a complex system. We show the latter by introducing a new node-based metric called attractivity which can be used in conjunction with the SSCC as they are highly correlated. We demonstrate the proposed method by applying it to several chaotic dynamical systems and a large-scale agent-based social simulation model. (shrink)

Persistent activity can be the product of mechanisms other than attractor reverberations. The single-unit data presented by Amit cannot discriminate between the different mechanisms. In fact, single-unit data do not appear to be adequate for testing neural network models.

This commentary questions the target articles inferences from a limited set of empirical data to support this model and conceptual scheme. Especially questionable is the attribution of internal representation properties to an assembly of cells in a discrete cortical module firing at a discrete attractor frequency. Alternative inferences are drawn from cortical cooling and cell-firing data that point to the internal representation as a broad and specific cortical network defined by cortico-cortical connectivity. Active memory, it is proposed, consists in (...) the sustained activation of the component neuron populations of the network. (shrink)

The new business opportunities and prospects emerging in Europe within the Common Market and other Western and European countries also raise important ethical challenges. This work comprises a collection of ethical insights to enhance the conduct of business in an evolving Europe.

In this volume philosophers from throughout the Asia-Pacific region discuss a wide range of topics related to the development of democratic values and ways of life. The papers explore ideas, values and practices related to democracy from the different perspectives of the great religious and philosophical traditions of Asia, as well as considering both philosophical issues and the place of philosophy in a democratic society. While the contributors represent different philosophical traditions, they are connected through a common concern with humanity, (...) a belief in the value of philosophical inquiry and scholarship and by a commitment to the value of philosophy education in building and sustaining democracy. (shrink)

The Neo‐Darwinian concept of natural selection is plausible when one assumes a straightforward causation of phenotype by genotype. However, such simple 1:1 mapping must now give place to the modern concepts of gene regulatory networks and gene expression noise. Both can, in the absence of genetic mutations, jointly generate a diversity of inheritable randomly occupied phenotypic states that could also serve as a substrate for natural selection. This form of epigenetic dynamics challenges Neo‐Darwinism. It needs to incorporate the non‐linear, (...) stochastic dynamics of gene networks. A first step is to consider the mathematical correspondence between gene regulatory networks and Waddington's metaphoric ‘epigenetic landscape’, which actually represents the quasi‐potential function of global network dynamics. It explains the coexistence of multiple stable phenotypes within one genotype. The landscape's topography with its attractors is shaped by evolution through mutational re‐wiring of regulatory interactions – offering a link between genetic mutation and sudden, broad evolutionary changes. (shrink)

Here I present and discuss a model that, among other things, appears able to describe the dynamics of cancer cell origin from the perspective of stable and unstable gene expression profiles. In identifying suchaberrantgene expression profiles as lying outside the normal stable states attracted through development and normal cell differentiation, the hypothesis explains why cancer cells accumulate mutations, to which they are not robust, and why these mutations create a new stable state far from the normal gene expression profile space. (...) Such cells are in strong contrast with normal cell types that appeared as an attractor state in the gene expression dynamical system under cell‐cell interaction and achieved robustness to noise through evolution, which in turn also conferred robustness to mutation. In complex gene regulation networks, otheraberrantcellular states lacking such high robustness are expected to remain, which would correspond to cancer cells. (shrink)

The functioning of a subject in a changing environment is most effective from the point of view of survival if the subject can form, maintain and use internal representations of the external world for decision-making. These representations are also called reflection in a broad sense. Using it, one can win in reflexive games since an internal representation of the enemy allows predicting their future moves. The goal is to assess the reflexive potential of heuristic model objects – artificial neural (...) class='Hi'>networks – in the reflexive games “Even-Odd” (or “Matching pennies”) and “Rock-Paper-Scissors”. We used homogeneous fully connected neural networks of small sizes (from 8 to 45 neurons). Games were played between neural networks with different configurations and parameters (size, step size for modifying weight coefficients). A set of reflexivity criteria is presented, corresponding to different levels of consideration: neuronal, behavioral, formal. The transitivity of formal success in the game is shown. The most successful configurations, however, may not meet other criteria of reflexivity. We hypothesize that the best compliance with the criteria and, as a consequence, universal success in reflection tasks is achievable for heterogeneous configurations with a structure in which the formation of hierarchical systems of attractors is possible. (shrink)

The stunning possibility of “reprogramming” differentiated somatic cells to express a pluripotent stem cell phenotype (iPS, induced pluripotent stem cell) and the “ground state” character of pluripotency reveal fundamental features of cell fate regulation that lie beyond existing paradigms. The rarity of reprogramming events appears to contradict the robustness with which the unfathomably complex phenotype of stem cells can reliably be generated. This apparent paradox, however, is naturally explained by the rugged “epigenetic landscape” with valleys representing “preprogrammed” attractor states (...) that emerge from the dynamical constraints of the gene regulatory network. This article provides a pedagogical primer to the fundamental principles of gene regulatory networks as integrated dynamic systems and reviews recent insights in gene expression noise and fate determination, thereby offering a formal framework that may help us to understand why cell fate reprogramming events are inherently rare and yet so robust. (shrink)

Hebbian cell-assembly theory and attractor networks are good starting points for modeling cortical processing. Detailed cell models can be useful in understanding the dynamics of attractor networks. Cell assemblies are likely to be distributed, with the cortical column as the local processing unit. Synaptic memory may be dominant in all but the first couple of seconds.

The parallel distributed processing (PDP) perspective brings forward the important point that all semantic phenomena are based on analog underlying mechanisms, involving the weighted summation of multiple inputs by individual neurons. It falls short of indicating, however, how the essentially discrete nature of semantic processing may emerge at the cognitive level. Bridging this gap probably requires attractor networks.

Understanding spoken words involves a rapid mapping from speech to conceptual representations. One distributed feature-based conceptual account assumes that the statistical characteristics of concepts’ features—the number of concepts they occur in and likelihood of co-occurrence —determine conceptual activation. To test these claims, we investigated the role of distinctiveness/sharedness and correlational strength in speech-to-meaning mapping, using a lexical decision task and computational simulations. Responses were faster for concepts with higher sharedness, suggesting that shared features are facilitatory in tasks like lexical decision (...) that require access to them. Correlational strength facilitated responses for slower participants, suggesting a time-sensitive co-occurrence-driven settling mechanism. The computational simulation showed similar effects, with early effects of shared features and later effects of correlational strength. These results support a general-to-specific account of conceptual processing, whereby early activation of shared features is followed by the gradual emergence of a specific target representation. (shrink)

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. (shrink)

Most words in English are ambiguous between different interpretations; words can mean different things in different contexts. We investigate the implications of different types of semantic ambiguity for connectionist models of word recognition. We present a model in which there is competition to activate distributed semantic representations. The model performs well on the task of retrieving the different meanings of ambiguous words, and is able to simulate data reported by Rodd, Gaskell, and Marslen‐Wilson [J. Mem. Lang. 46 (2002) 245] on (...) how semantic ambiguity affects lexical decision performance. In particular, the network shows a disadvantage forwords with multiple unrelated meanings (e.g., bark) that coexists with a benefit for words with multiple related word senses (e.g., twist). The ambiguity disadvantage arises because of interference between the different meanings, while the sense benefit arises because of differences in the structure of the attractor basins formed during learning. Words with few senses develop deep, narrow attractor basins, while words with many senses develop shallow, broad basins. We conclude that the mental representations of word meanings can be modelled as stable states within a high‐dimensional semantic space, and that variations in the meanings of words shape the landscape of this space. (shrink)

The Visual World Paradigm (VWP) presents listeners with a challenging problem: They must integrate two disparate signals, the spoken language and the visual context, in support of action (e.g., complex movements of the eyes across a scene). We present Impulse Processing, a dynamical systems approach to incremental eye movements in the visual world that suggests a framework for integrating language, vision, and action generally. Our approach assumes that impulses driven by the language and the visual context impinge minutely on a (...) dynamical landscape of attractors corresponding to the potential eye-movement behaviors of the system. We test three unique predictions of our approach in an empirical study in the VWP, and describe an implementation in an artificial neural network. We discuss the Impulse Processing framework in relation to other models of the VWP. (shrink)

Through statistical analysis of datasets describing single cell shape following systematic gene depletion, we have found that the morphological landscapes explored by cells are composed of a small number of attractor states. We propose that the topology of these landscapes is in large part determined by cell‐intrinsic factors, such as biophysical constraints on cytoskeletal organization, and reflects different stable signaling and/or transcriptional states. Cell‐extrinsic factors act to determine how cells explore these landscapes, and the topology of the landscapes themselves. (...) Informational stimuli primarily drive transitions between stable states by engaging signaling networks, while mechanical stimuli tune, or even radically alter, the topology of these landscapes. As environments fluctuate, the topology of morphological landscapes explored by cells dynamically adapts to these fluctuations. Finally we hypothesize how complex cellular and tissue morphologies can be generated from a limited number of simple cell shapes. (shrink)

Amit's “Attractor Neural Network” perspective on cognition raises difficult technical problems already met by prior dynamical models. This commentary sketches briefly some of them concerning the internal topological structure of attractors, the constituency problem, the possibility of activating simultaneously several attractors, and the different kinds of dynamical structures one can use to model brain activity: point attractors, strange attractors, synchronized arrays of oscillators, synfire chains, and so forth.

This paper proposes how various disciplinary theories of cognition may be combined into a unifying, sub-symbolic, computational theory of cognition. The following theories are considered for integration: psychological theories, including the theory of event coding, event segmentation theory, the theory of anticipatory behavioral control, and concept development; artificial intelligence and machine learning theories, including reinforcement learning and generative artificial neural networks; and theories from theoretical and computational neuroscience, including predictive coding and free energy-based inference. In the light of such (...) a potential unification, it is discussed how abstract cognitive, conceptualized knowledge and understanding may be learned from actively gathered sensorimotor experiences. The unification rests on the free energy-based inference principle, which essentially implies that the brain builds a predictive, generative model of its environment. Neural activity-oriented inference causes the continuous adaptation of the currently active predictive encodings. Neural structure-oriented inference causes the longer term adaptation of the developing generative model as a whole. Finally, active inference strives for maintaining internal homeostasis, causing goal-directed motor behavior. To learn abstract, hierarchical encodings, however, it is proposed that free energy-based inference needs to be enhanced with structural priors, which bias cognitive development towards the formation of particular, behaviorally suitable encoding structures. As a result, it is hypothesized how abstract concepts can develop from, and thus how they are structured by and grounded in, sensorimotor experiences. Moreover, it is sketched-out how symbol-like thought can be generated by a temporarily active set of predictive encodings, which constitute a distributed neural attractor in the form of an interactive free-energy minimum. The activated, interactive network attractor essentially characterizes the semantics of a concept or a concept composition, such as an actual or imagined situation in our environment. Temporal successions of attractors then encode unfolding semantics, which may be generated by a behavioral or mental interaction with an actual or imagined situation in our environment. Implications, further predictions, possible verification and falsifications, as well as potential enhancements into a fully spelled-out unified theory of cognition are discussed at the end of the paper. (shrink)

Until quite recently, almost no philosophers trained in the continental tradition saw anything of value in realism. The situation in analytic philosophy was always different, but in continental philosophy realism was usually treated as a pseudo-problem. That is no longer the case. In this provocative new book, two leading philosophers examine the remarkable rise of realism in the continental tradition. While exploring the similarities and differences in their own positions, they also consider the work of others and assess rival trends (...) in contemporary philosophy. They begin by discussing the relation between realism and materialism, which DeLanda links closely but which Harman tries to separate. Part Two covers the many different meanings of realism, with the two authors working together to develop an expanded definition of the term. Part Three features a spirited exchange on the respective virtues and drawbacks of DeLanda's realism of attractors and singularities and Harman's object-oriented theory. Part Four shifts to the question of the knowability of the real, as the authors discuss whether scientific knowledge does full justice to reality. In Part Five, they shift the focus to space, time, and science more generally, and here Harman offers a defence of actor-network theory despite its obvious anti-realist elements. Lively, accessible and engaging, this book is the best attempt so far to clarify the different paths for realism in continental philosophy. It will be of great value to students and scholars of continental philosophy and to anyone interested in the cutting-edge debates in philosophy and critical theory today. (shrink)

This study attempts to describe the notion of the ‘self’ using dynamical systems language based on the results of our robot learning experiments. A neural network model consisting of multiple modules is proposed, in which the interactive dynamics between the bottom-up perception and the top-down prediction are investigated. Our experiments with a real mobile robot showed that the incremental learning of the robot switches spontaneously between steady and unsteady phases. In the steady phase, the top-down prediction for the bottom-up perception (...) works well when coherence is achieved between the internal and the environmental dynamics. In the unsteady phase, conflicts arise between the bottom-up perception and the top-down prediction; the coherence is lost, and a chaotic attractor is observed in the internal neural dynamics. By investigating possible analogies between this result and the phenomenological literature on the ‘self', we draw the conclusions that the structure of the ‘self’ corresponds to the ‘open dynamic structure’ which is characterized by co-existence of stability in terms of goal-directedness and instability caused by embodiment; the open dynamic structure causes the system's spontaneous transition to the unsteady phase where the ‘self’ becomes aware. (shrink)

A key question in studying consciousness is how neural operations in the brain can identify streams of sensory input as belonging to distinct modalities, which contributes to the representation of qualitatively different experiences. The basis for identification of modalities is proposed to be constituted by self-organized comparative operations across a network of unimodal and multimodal sensory areas. However, such network interactions alone cannot answer the question how sensory feature detectors collectively account for an integrated, yet phenomenally differentiated experiential content. This (...) problem turns out to be different from, although related to, the binding problem. It is proposed that the neural correlate of an enriched, multimodal experience is constituted by the attractor state of a dynamic associative network. Within this network, unimodal and multimodal sensory maps continuously interact to influence each other’s attractor state, so that a feature change in one modality results in a fast re-coding of feature information in another modality. In this scheme, feature detection is coded by firing-rate, whereas firing phase codes relational aspects. (shrink)

Connectionist models offer concretemechanisms for cognitive processes. When these modelsmimic the performance of human subjects theycan offer insights into the computationswhich might underlie human cognition. We illustratethis with the performance of a recurrentconnectionist network which produces the meaningof words in response to their spellingpattern. It mimics a paradoxical pattern oferrors produced by people trying to read degradedwords. The reason why the network produces thesurprising error pattern lies in the nature ofthe attractors which it develops as it learns tomap spelling patterns (...) to semantics. The keyrole of attractor structure in the successfulsimulation suggests that the normal adult semanticreading route may involve attractor dynamics, andthus the paradoxical error pattern isexplained. (shrink)

Connectionist models offer concretemechanisms for cognitive processes. When these modelsmimic the performance of human subjects theycan offer insights into the computationswhich might underlie human cognition. We illustratethis with the performance of a recurrentconnectionist network which produces the meaningof words in response to their spellingpattern. It mimics a paradoxical pattern oferrors produced by people trying to read degradedwords. The reason why the network produces thesurprising error pattern lies in the nature ofthe attractors which it develops as it learns tomap spelling patterns (...) to semantics. The keyrole of attractor structure in the successfulsimulation suggests that the normal adult semanticreading route may involve attractor dynamics, andthus the paradoxical error pattern isexplained. (shrink)