1,115,661 research outputs found
Complex Neuro-Cognitive Systems
Cognitive functions such as a perception, thinking and acting are based on the working of the brain, one of the most complex systems we know. The traditional scientific methodology, however, has proved to be not sufficient to understand the relation between brain and cognition. The aim of this paper is to review an alternative methodology – nonlinear dynamical analysis – and to demonstrate its benefit\ud
for cognitive neuroscience in cases when the usual reductionist method fails
A Preliminar Evidence of Quantum Like Behavior in Measurements of Mental States
Experimental results presented in this paper supports the hypothesis on
quantum-like statistical behaviour of cognitive systems (at least human
beings). Our quantum-like approach gives the possibility to represent mental
states by Hilbert space vectors (complex amplitudes). Such a representation
induces huge reduction of information about a mental state. We realize an
approach that has no direct relation with reductionist quantum models and we
are not interested in statistical behavior of micro systems forming the macro
system of the brain. We describe only probabilistic features of cognitive
measurements. Our quantum-like approach describes statistics of measurements of
cognitive systems with the aim to ascertain if cognitive systems behave as
quantum-like systems where here quantum-like cognitive behavior means that
cognitive systems result to be very sensitive to changes of the context with
regard to the complex of the mental conditions
Consciosusness in Cognitive Architectures. A Principled Analysis of RCS, Soar and ACT-R
This report analyses the aplicability of the principles of consciousness developed in the ASys project to three of the most relevant cognitive architectures. This is done in relation to their aplicability to build integrated control systems and studying their support for general mechanisms of real-time consciousness.\ud
To analyse these architectures the ASys Framework is employed. This is a conceptual framework based on an extension for cognitive autonomous systems of the General Systems Theory (GST).\ud
A general qualitative evaluation criteria for cognitive architectures is established based upon: a) requirements for a cognitive architecture, b) the theoretical framework based on the GST and c) core design principles for integrated cognitive conscious control systems
Vision systems with the human in the loop
The emerging cognitive vision paradigm deals with vision systems that apply machine learning and automatic reasoning in order to learn from what they perceive. Cognitive vision systems can rate the relevance and consistency of newly acquired knowledge, they can adapt to their environment and thus will exhibit high robustness. This contribution presents vision systems that aim at flexibility and robustness. One is tailored for content-based image retrieval, the others are cognitive vision systems that constitute prototypes of visual active memories which evaluate, gather, and integrate contextual knowledge for visual analysis. All three systems are designed to interact with human users. After we will have discussed adaptive content-based image retrieval and object and action recognition in an office environment, the issue of assessing cognitive systems will be raised. Experiences from psychologically evaluated human-machine interactions will be reported and the promising potential of psychologically-based usability experiments will be stressed
System-of-Systems Complexity
The global availability of communication services makes it possible to
interconnect independently developed systems, called constituent systems, to
provide new synergistic services and more efficient economic processes. The
characteristics of these new Systems-of-Systems are qualitatively different
from the classic monolithic systems. In the first part of this presentation we
elaborate on these differences, particularly with respect to the autonomy of
the constituent systems, to dependability, continuous evolution, and emergence.
In the second part we look at a SoS from the point of view of cognitive
complexity. Cognitive complexity is seen as a relation between a model of an
SoS and the observer. In order to understand the behavior of a large SoS we
have to generate models of adequate simplicity, i.e, of a cognitive complexity
that can be handled by the limited capabilities of the human mind. We will
discuss the importance of properly specifying and placing the relied-upon
message interfaces between the constituent systems that form an open SoS and
discuss simplification strategies that help to reduce the cognitive complexity.Comment: In Proceedings AiSoS 2013, arXiv:1311.319
Joint Channel Estimation and Pilot Allocation in Underlay Cognitive MISO Networks
Cognitive radios have been proposed as agile technologies to boost the
spectrum utilization. This paper tackles the problem of channel estimation and
its impact on downlink transmissions in an underlay cognitive radio scenario.
We consider primary and cognitive base stations, each equipped with multiple
antennas and serving multiple users. Primary networks often suffer from the
cognitive interference, which can be mitigated by deploying beamforming at the
cognitive systems to spatially direct the transmissions away from the primary
receivers. The accuracy of the estimated channel state information (CSI) plays
an important role in designing accurate beamformers that can regulate the
amount of interference. However, channel estimate is affected by interference.
Therefore, we propose different channel estimation and pilot allocation
techniques to deal with the channel estimation at the cognitive systems, and to
reduce the impact of contamination at the primary and cognitive systems. In an
effort to tackle the contamination problem in primary and cognitive systems, we
exploit the information embedded in the covariance matrices to successfully
separate the channel estimate from other users' channels in correlated
cognitive single input multiple input (SIMO) channels. A minimum mean square
error (MMSE) framework is proposed by utilizing the second order statistics to
separate the overlapping spatial paths that create the interference. We
validate our algorithms by simulation and compare them to the state of the art
techniques.Comment: 6 pages, 2 figures, invited paper to IWCMC 201
Catecholamines and cognition after traumatic brain injury
Cognitive problems are one of the main causes of ongoing disability after traumatic brain injury. The heterogeneity of the injuries sustained and the variability of the resulting cognitive deficits makes treating these problems difficult. Identifying the underlying pathology allows a targeted treatment approach aimed at cognitive enhancement. For example, damage to neuromodulatory neurotransmitter systems is common after traumatic brain injury and is an important cause of cognitive impairment. Here, we discuss the evidence implicating disruption of the catecholamines (dopamine and noradrenaline) and review the efficacy of catecholaminergic drugs in treating post-traumatic brain injury cognitive impairments. The response to these therapies is often variable, a likely consequence of the heterogeneous patterns of injury as well as a non-linear relationship between catecholamine levels and cognitive functions. This individual variability means that measuring the structure and function of a person’s catecholaminergic systems is likely to allow more refined therapy. Advanced structural and molecular imaging techniques offer the potential to identify disruption to the catecholaminergic systems and to provide a direct measure of catecholamine levels. In addition, measures of structural and functional connectivity can be used to identify common patterns of injury and to measure the functioning of brain ‘networks’ that are important for normal cognitive functioning. As the catecholamine systems modulate these cognitive networks, these measures could potentially be used to stratify treatment selection and monitor response to treatment in a more sophisticated manner
Controllability of structural brain networks.
Cognitive function is driven by dynamic interactions between large-scale neural circuits or networks, enabling behaviour. However, fundamental principles constraining these dynamic network processes have remained elusive. Here we use tools from control and network theories to offer a mechanistic explanation for how the brain moves between cognitive states drawn from the network organization of white matter microstructure. Our results suggest that densely connected areas, particularly in the default mode system, facilitate the movement of the brain to many easily reachable states. Weakly connected areas, particularly in cognitive control systems, facilitate the movement of the brain to difficult-to-reach states. Areas located on the boundary between network communities, particularly in attentional control systems, facilitate the integration or segregation of diverse cognitive systems. Our results suggest that structural network differences between cognitive circuits dictate their distinct roles in controlling trajectories of brain network function
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