104,474 research outputs found
Robotic Interval Timing based on Active Oscillations
AbstractInterval timing is crucially involved in many of the daily activities of humans and animals. However, the cognitive mechanisms enabling the encoding and processing of time in the brain remain largely unknown. In the present work, we follow a self- organized modeling approach to study unconventional representations of time in neural network based cognitive system. A particularly interesting feature of our study regards the implementation of a single computational model to accomplish two different robotic behavioral tasks, which assume diverse manipulation of time intervals. The examination of the implemented cognitive system revealed that it is possible to integrate the two main theoretical models of time representation existing today - the dedicated and intrinsic representations - into a new theory that effectively combines their key characteristics
Self-organization without conservation: true or just apparent scale-invariance?
The existence of true scale-invariance in slowly driven models of
self-organized criticality without a conservation law, as forest-fires or
earthquake automata, is scrutinized in this paper. By using three different
levels of description - (i) a simple mean field, (ii) a more detailed
mean-field description in terms of a (self-organized) branching processes, and
(iii) a full stochastic representation in terms of a Langevin equation-, it is
shown on general grounds that non-conserving dynamics does not lead to bona
fide criticality. Contrarily to conserving systems, a parameter, which we term
"re-charging" rate (e.g. the tree-growth rate in forest-fire models), needs to
be fine-tuned in non-conserving systems to obtain criticality. In the infinite
size limit, such a fine-tuning of the loading rate is easy to achieve, as it
emerges by imposing a second separation of time-scales but, for any finite
size, a precise tuning is required to achieve criticality and a coherent
finite-size scaling picture. Using the approaches above, we shed light on the
common mechanisms by which "apparent criticality" is observed in non-conserving
systems, and explain in detail (both qualitatively and quantitatively) the
difference with respect to true criticality obtained in conserving systems. We
propose to call this self-organized quasi-criticality (SOqC). Some of the
reported results are already known and some of them are new. We hope the
unified framework presented here helps to elucidate the confusing and
contradictory literature in this field. In a second accompanying paper, we
shall discuss the implications of the general results obtained here for models
of neural avalanches in Neuroscience for which self-organized scale-invariance
in the absence of conservation has been claimed.Comment: 40 pages, 7 figures
Perspective: network-guided pattern formation of neural dynamics
The understanding of neural activity patterns is fundamentally linked to an
understanding of how the brain's network architecture shapes dynamical
processes. Established approaches rely mostly on deviations of a given network
from certain classes of random graphs. Hypotheses about the supposed role of
prominent topological features (for instance, the roles of modularity, network
motifs, or hierarchical network organization) are derived from these
deviations. An alternative strategy could be to study deviations of network
architectures from regular graphs (rings, lattices) and consider the
implications of such deviations for self-organized dynamic patterns on the
network. Following this strategy, we draw on the theory of spatiotemporal
pattern formation and propose a novel perspective for analyzing dynamics on
networks, by evaluating how the self-organized dynamics are confined by network
architecture to a small set of permissible collective states. In particular, we
discuss the role of prominent topological features of brain connectivity, such
as hubs, modules and hierarchy, in shaping activity patterns. We illustrate the
notion of network-guided pattern formation with numerical simulations and
outline how it can facilitate the understanding of neural dynamics
Seven properties of self-organization in the human brain
The principle of self-organization has acquired a fundamental significance in the newly emerging field of computational philosophy. Self-organizing systems have been described in various domains in science and philosophy including physics, neuroscience, biology and medicine, ecology, and sociology. While system architecture and their general purpose may depend on domain-specific concepts and definitions, there are (at least) seven key properties of self-organization clearly identified in brain systems: 1) modular connectivity, 2) unsupervised learning, 3) adaptive ability, 4) functional resiliency, 5) functional plasticity, 6) from-local-to-global functional organization, and 7) dynamic system growth. These are defined here in the light of insight from neurobiology, cognitive neuroscience and Adaptive Resonance Theory (ART), and physics to show that self-organization achieves stability and functional plasticity while minimizing structural system complexity. A specific example informed by empirical research is discussed to illustrate how modularity, adaptive learning, and dynamic network growth enable stable yet plastic somatosensory representation for human grip force control. Implications for the design of “strong” artificial intelligence in robotics are brought forward
Perspectives on the Neuroscience of Cognition and Consciousness
The origin and current use of the concepts of computation, representation and information in Neuroscience are examined and conceptual flaws are identified which vitiate their usefulness for addressing problems of the neural basis of Cognition and Consciousness. In contrast, a convergence of views is presented to support the characterization of the Nervous System as a complex dynamical system operating in the metastable regime, and capable of evolving to configurations and transitions in phase space with potential relevance for Cognition and Consciousness
The Complementary Brain: From Brain Dynamics To Conscious Experiences
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
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