Networks of Cognitive Processes: Functional and Anatomical Correlates of Cognition, Emotions, and Social Cognition

2019 ◽  
pp. 157-170
Author(s):  
John D. Medaglia
Keyword(s):  
Neural Networks ◽  
Cognitive Function ◽  
Human Brain ◽  
Cognitive Processes ◽  
Network Science ◽  
Complex Dynamics ◽  
Cognitive Networks ◽  
Cognitive Network ◽  
The World ◽  
Network Neuroscience

Networks of cognitive processes describe some of the key findings emerging from cognitive network neuroscience. Cognition is organized in distinct networks in the human brain. These cognitive networks interact via complex dynamics to process our environments and enact our decisions on the world. Within the emerging subdiscipline known as cognitive network neuroscience, we can connect classical neuroscience approaches to network science. This allows us to consider how major cognitive functions ranging from sensation to cognitive control and emotion are organized in the human brain. Through the lens of network neuroscience, we can enrich our understanding of normal and disordered cognitive function to be manifestations of processes and representations in ordered or disorded neural networks.

scholarly journals Cognitive Network Neuroscience

2015 ◽  
Vol 27 (8) ◽  
pp. 1471-1491 ◽  
Author(s):  
John D. Medaglia ◽  
Mary-Ellen Lynall ◽  
Danielle S. Bassett
Keyword(s):  
Cognitive Neuroscience ◽  
Network Science ◽  
Structural Connectivity ◽  
Cognitive Network ◽  
Network Characteristics ◽  
Time Dynamics ◽  
Neuroimaging Data ◽  
Control Learning ◽  
And Function ◽  
Network Neuroscience

Network science provides theoretical, computational, and empirical tools that can be used to understand the structure and function of the human brain in novel ways using simple concepts and mathematical representations. Network neuroscience is a rapidly growing field that is providing considerable insight into human structural connectivity, functional connectivity while at rest, changes in functional networks over time (dynamics), and how these properties differ in clinical populations. In addition, a number of studies have begun to quantify network characteristics in a variety of cognitive processes and provide a context for understanding cognition from a network perspective. In this review, we outline the contributions of network science to cognitive neuroscience. We describe the methodology of network science as applied to the particular case of neuroimaging data and review its uses in investigating a range of cognitive functions including sensory processing, language, emotion, attention, cognitive control, learning, and memory. In conclusion, we discuss current frontiers and the specific challenges that must be overcome to integrate these complementary disciplines of network science and cognitive neuroscience. Increased communication between cognitive neuroscientists and network scientists could lead to significant discoveries under an emerging scientific intersection known as cognitive network neuroscience.

scholarly journals Cognitive Networks: Interactivity, Intersubjectivity, and Synergy

2018 ◽  
Vol 20 (1) ◽  
pp. 52-78
Author(s):  
Helena Knyazeva
Keyword(s):  
Complex Systems ◽  
Cognitive Science ◽  
Cognitive Processes ◽  
Network Science ◽  
Cognitive Maps ◽  
Cognitive Networks ◽  
Point Of View ◽  
Network Structures ◽  
Cognitive Coherence ◽  
Cognitive Niches

Some properties of cognitive networks are discussed in the article in the context of the modern achievements of the network science. It is the study in network structures and their surprising properties that gives a new impetus to the development of the theory of complex systems (synergetics). The analysis of cognitive processes from the point of view of the network structures that arise in them not only fits with such concepts already existing in cognitive science and epistemology, as cognitive niches, cognitive maps, cognitive coherence, etc.), but also brings some new aspects to the understanding of interactivity, intersubjectivity, synergy in cognition and creative activities, empathy.

scholarly journals Contributions of Modern Network Science to the Cognitive Sciences: Revisiting research spirals of representation and process

2019 ◽  
Author(s):  
Nichol Castro ◽  
Cynthia S. Q. Siew
Keyword(s):  
Network Science ◽  
Human Mind ◽  
Cognitive Networks ◽  
Cognitive Systems ◽  
Mathematical Framework ◽  
Psychological Processes ◽  
Cognitive Network ◽  
Cognitive Sciences ◽  
Graph Theoretic ◽  
Classic Papers

Modelling the structure of cognitive systems is a central goal of the cognitive sciences—a goal that has greatly benefitted from the application of network science approaches. This paper provides an overview of how network science has been applied to the cognitive sciences, with a specific focus on the two research “spirals” of cognitive sciences related to the representation and processes of the human mind. For each spiral, we first review classic papers in the psychological sciences that have drawn on graph-theoretic ideas or frameworks before the advent of modern network science approaches. We then discuss how current research in these areas have been shaped by modern network science, which provide the mathematical framework and methodological tools for psychologists to (i) represent cognitive network structure, and (ii) investigate and model the psychological processes that occur in these cognitive networks. Finally, we briefly comment on the future of, and the challenges facing, cognitive network science.

scholarly journals What Do Cognitive Networks Do? Simulations of Spoken Word Recognition Using the Cognitive Network Science Approach

2021 ◽  
Vol 11 (12) ◽  
pp. 1628
Author(s):  
Michael S. Vitevitch ◽  
Gavin J. D. Mullin
Keyword(s):  
Word Recognition ◽  
Network Model ◽  
Language Processing ◽  
Network Science ◽  
Spoken Word Recognition ◽  
Cognitive Networks ◽  
Spoken Word ◽  
Cognitive Network ◽  
Behavioral Studies ◽  
Trace Model

Cognitive network science is an emerging approach that uses the mathematical tools of network science to map the relationships among representations stored in memory to examine how that structure might influence processing. In the present study, we used computer simulations to compare the ability of a well-known model of spoken word recognition, TRACE, to the ability of a cognitive network model with a spreading activation-like process to account for the findings from several previously published behavioral studies of language processing. In all four simulations, the TRACE model failed to retrieve a sufficient number of words to assess if it could replicate the behavioral findings. The cognitive network model successfully replicated the behavioral findings in Simulations 1 and 2. However, in Simulation 3a, the cognitive network did not replicate the behavioral findings, perhaps because an additional mechanism was not implemented in the model. However, in Simulation 3b, when the decay parameter in spreadr was manipulated to model this mechanism the cognitive network model successfully replicated the behavioral findings. The results suggest that models of cognition need to take into account the multi-scale structure that exists among representations in memory, and how that structure can influence processing.

scholarly journals Contributions of modern network science to the cognitive sciences: revisiting research spirals of representation and process

2020 ◽  
Vol 476 (2238) ◽  
pp. 20190825 ◽  
Author(s):  
Nichol Castro ◽  
Cynthia S. Q. Siew
Keyword(s):  
Network Science ◽  
Human Mind ◽  
Cognitive Networks ◽  
Cognitive Systems ◽  
Mathematical Framework ◽  
Psychological Processes ◽  
Cognitive Network ◽  
Cognitive Sciences ◽  
Graph Theoretic ◽  
Classic Papers

Modelling the structure of cognitive systems is a central goal of the cognitive sciences—a goal that has greatly benefitted from the application of network science approaches. This paper provides an overview of how network science has been applied to the cognitive sciences, with a specific focus on the two research ‘spirals’ of cognitive sciences related to the representation and processes of the human mind. For each spiral, we first review classic papers in the psychological sciences that have drawn on graph-theoretic ideas or frameworks before the advent of modern network science approaches. We then discuss how current research in these areas has been shaped by modern network science, which provides the mathematical framework and methodological tools for psychologists to (i) represent cognitive network structure and (ii) investigate and model the psychological processes that occur in these cognitive networks. Finally, we briefly comment on the future of, and the challenges facing, cognitive network science.

Representation of adversarial images in deep neural networks and the human brain

2018 ◽  
Author(s):  
Chi Zhang ◽  
Xiaohan Duan ◽  
Ruyuan Zhang ◽  
Li Tong
Keyword(s):  
Neural Networks ◽  
Human Brain ◽  
Deep Neural Networks

scholarly journals Deep Malaria Parasite Detection in Thin Blood Smear Microscopic Images

2021 ◽  
Vol 11 (5) ◽  
pp. 2284
Author(s):  
Asma Maqsood ◽  
Muhammad Shahid Farid ◽  
Muhammad Hassan Khan ◽  
Marcin Grzegorzek
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Red Blood Cells ◽  
Convolutional Neural Networks ◽  
Blood Cells ◽  
Superior Performance ◽  
Learning Models ◽  
Infected Female ◽  
The World ◽  
Augmentation Techniques

Malaria is a disease activated by a type of microscopic parasite transmitted from infected female mosquito bites to humans. Malaria is a fatal disease that is endemic in many regions of the world. Quick diagnosis of this disease will be very valuable for patients, as traditional methods require tedious work for its detection. Recently, some automated methods have been proposed that exploit hand-crafted feature extraction techniques however, their accuracies are not reliable. Deep learning approaches modernize the world with their superior performance. Convolutional Neural Networks (CNN) are vastly scalable for image classification tasks that extract features through hidden layers of the model without any handcrafting. The detection of malaria-infected red blood cells from segmented microscopic blood images using convolutional neural networks can assist in quick diagnosis, and this will be useful for regions with fewer healthcare experts. The contributions of this paper are two-fold. First, we evaluate the performance of different existing deep learning models for efficient malaria detection. Second, we propose a customized CNN model that outperforms all observed deep learning models. It exploits the bilateral filtering and image augmentation techniques for highlighting features of red blood cells before training the model. Due to image augmentation techniques, the customized CNN model is generalized and avoids over-fitting. All experimental evaluations are performed on the benchmark NIH Malaria Dataset, and the results reveal that the proposed algorithm is 96.82% accurate in detecting malaria from the microscopic blood smears.

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