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Contributions and challenges for network models in cognitive neuroscience

Contributions and challenges for network models in cognitive neuroscience | Papers | Scoop.it

The confluence of new approaches in recording patterns of brain connectivity and quantitative analytic tools from network science has opened new avenues toward understanding the organization and function of brain networks. Descriptive network models of brain structural and functional connectivity have made several important contributions; for example, in the mapping of putative network hubs and network communities. Building on the importance of anatomical and functional interactions, network models have provided insight into the basic structures and mechanisms that enable integrative neural processes. Network models have also been instrumental in understanding the role of structural brain networks in generating spatially and temporally organized brain activity. Despite these contributions, network models are subject to limitations in methodology and interpretation, and they face many challenges as brain connectivity data sets continue to increase in detail and complexity.


Contributions and challenges for network models in cognitive neuroscience
• Olaf Sporns
Nature Neuroscience (2014) http://dx.doi.org/10.1038/nn.3690

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When Two Become One: The Limits of Causality Analysis of Brain Dynamics - Daniel Chicharro, Anders Ledberg

When Two Become One: The Limits of Causality Analysis of Brain Dynamics - Daniel Chicharro, Anders Ledberg | Papers | Scoop.it

Biological systems often consist of multiple interacting subsystems, the brain being a prominent example. To understand the functions of such systems it is important to analyze if and how the subsystems interact and to describe the effect of these interactions. In this work we investigate the extent to which the cause-and-effect framework is applicable to such interacting subsystems. We base our work on a standard notion of causal effects and define a new concept called natural causal effect. This new concept takes into account that when studying interactions in biological systems, one is often not interested in the effect of perturbations that alter the dynamics. The interest is instead in how the causal connections participate in the generation of the observed natural dynamics. We identify the constraints on the structure of the causal connections that determine the existence of natural causal effects. In particular, we show that the influence of the causal connections on the natural dynamics of the system often cannot be analyzed in terms of the causal effect of one subsystem on another. Only when the causing subsystem is autonomous with respect to the rest can this interpretation be made. We note that subsystems in the brain are often bidirectionally connected, which means that interactions rarely should be quantified in terms of cause-and-effect. We furthermore introduce a framework for how natural causal effects can be characterized when they exist. Our work also has important consequences for the interpretation of other approaches commonly applied to study causality in the brain. Specifically, we discuss how the notion of natural causal effects can be combined with Granger causality and Dynamic Causal Modeling (DCM). Our results are generic and the concept of natural causal effects is relevant in all areas where the effects of interactions between subsystems are of interest.

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Forgetting Is Harder for Older Brains: Scientific American

Forgetting Is Harder for Older Brains: Scientific American | Papers | Scoop.it

Kids are wildly better than adults at most types of learning—most famously, new languages. One reason may be that adults' brains are “full,” in a way. Creating memories relies in part on the destruction of old memories, and recent research finds that adults have high levels of a protein that prevents such forgetting.


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FastTFriend's curator insight, June 8, 2013 10:32 AM

More dramatically, their brains could barely weaken their synapses, a process that allows the loss of useless information in favor of more recent data.

Arjen ten Have's curator insight, June 12, 2013 5:43 AM

There is two interesting aspect to this. The fact that learning can be hampered by ehm yeah well, learning and of course that this is a clue about the heuristics of memory storage in human brains.

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On reverse engineering in the cognitive and brain sciences - Springer

On reverse engineering in the cognitive and brain sciences - Springer | Papers | Scoop.it

Various research initiatives try to utilize the operational principles of organisms and brains to develop alternative, biologically inspired computing paradigms and artificial cognitive systems. This article reviews key features of the standard method applied to complexity in the cognitive and brain sciences, i.e. decompositional analysis or reverse engineering. The indisputable complexity of brain and mind raise the issue of whether they can be understood by applying the standard method. Actually, recent findings in the experimental and theoretical fields, question central assumptions and hypotheses made for reverse engineering. Using the modeling relation as analyzed by Robert Rosen, the scientific analysis method itself is made a subject of discussion. It is concluded that the fundamental assumption of cognitive science, i.e. complex cognitive systems can be analyzed, understood and duplicated by reverse engineering, must be abandoned. Implications for investigations of organisms and behavior as well as for engineering artificial cognitive systems are discussed.

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