Challenges of Complex Systems in Cognitive and Complex Systems

2010 ◽  
pp. 367-384
Author(s):  
Klaus Mainzer
Keyword(s):  
Neural Networks ◽  
Complex Systems ◽  
Large Scale ◽  
Computational Models ◽  
Self Control ◽  
Self Organization ◽  
Embodied Mind ◽  
Future Technology ◽  
Large Scale Networks ◽  
The Brain

After an introduction (1) the article analyzes complex systems and the evolution of the embodied mind (2), complex systems and the innovation of embodied robotics (3), and finally discusses challenges of handling a world with increasing complexity: Large-scale networks have the same universal properties in evolution and technology (4). Considering the evolution of the embodied mind (2), we start with an introduction of complex systems and nonlinear dynamics (2.1), apply this approach to neural self-organization (2.2), distinguish degrees of complexity of the brain (2.3), explain the emergence of cognitive states by complex systems dynamics (2.4), and discuss criteria for modeling the brain as complex nonlinear system (2.5). The innovation of embodied robotics (3) is a challenge of complex systems and future technology. We start with the distinction of symbolic and embodied AI (3.1). Embodied robotics is inspired by the evolution of life. Modern systems biology integrates the molecular, organic, human, and ecological levels of life with computational models of complex systems (3.2). Embodied robots are explained as dynamical systems (3.3). Self-organization of complex systems needs self-control of technical systems (3.4). Cellular neural networks (CNN) are an example of self-organizing complex systems offering new avenues for neurobionics (3.5). In general, technical neural networks support different kinds of learning robots (3.6). Embodied robotics aims at the development of cognitive and conscious robots (3.7).

scholarly journals Cerebral cartography and connectomics

2015 ◽  
Vol 370 (1668) ◽  
pp. 20140173 ◽  
Author(s):  
Olaf Sporns
Keyword(s):  
Large Scale ◽  
Computational Models ◽  
Network Science ◽  
Temporal Dynamics ◽  
Functional Organization ◽  
Brain Connectivity ◽  
Hierarchical Organization ◽  
Large Scale Networks ◽  
Definition Of ◽  
The Brain

Cerebral cartography and connectomics pursue similar goals in attempting to create maps that can inform our understanding of the structural and functional organization of the cortex. Connectome maps explicitly aim at representing the brain as a complex network, a collection of nodes and their interconnecting edges. This article reflects on some of the challenges that currently arise in the intersection of cerebral cartography and connectomics. Principal challenges concern the temporal dynamics of functional brain connectivity, the definition of areal parcellations and their hierarchical organization into large-scale networks, the extension of whole-brain connectivity to cellular-scale networks, and the mapping of structure/function relations in empirical recordings and computational models. Successfully addressing these challenges will require extensions of methods and tools from network science to the mapping and analysis of human brain connectivity data. The emerging view that the brain is more than a collection of areas, but is fundamentally operating as a complex networked system, will continue to drive the creation of ever more detailed and multi-modal network maps as tools for on-going exploration and discovery in human connectomics.

Bistability of somatic pattern memories: stochastic outcomes in bioelectric circuits underlying regeneration

2021 ◽  
Vol 376 (1821) ◽  
pp. 20190765 ◽  
Author(s):  
Giovanni Pezzulo ◽  
Joshua LaPalme ◽  
Fallon Durant ◽  
Michael Levin
Keyword(s):  
Large Scale ◽  
Computational Models ◽  
State Of The Art ◽  
Memory Representation ◽  
Theme Issue ◽  
Evolutionary Innovation ◽  
New Interpretation ◽  
The Brain

Nervous systems’ computational abilities are an evolutionary innovation, specializing and speed-optimizing ancient biophysical dynamics. Bioelectric signalling originated in cells' communication with the outside world and with each other, enabling cooperation towards adaptive construction and repair of multicellular bodies. Here, we review the emerging field of developmental bioelectricity, which links the field of basal cognition to state-of-the-art questions in regenerative medicine, synthetic bioengineering and even artificial intelligence. One of the predictions of this view is that regeneration and regulative development can restore correct large-scale anatomies from diverse starting states because, like the brain, they exploit bioelectric encoding of distributed goal states—in this case, pattern memories. We propose a new interpretation of recent stochastic regenerative phenotypes in planaria, by appealing to computational models of memory representation and processing in the brain. Moreover, we discuss novel findings showing that bioelectric changes induced in planaria can be stored in tissue for over a week, thus revealing that somatic bioelectric circuits in vivo can implement a long-term, re-writable memory medium. A consideration of the mechanisms, evolution and functionality of basal cognition makes novel predictions and provides an integrative perspective on the evolution, physiology and biomedicine of information processing in vivo . This article is part of the theme issue ‘Basal cognition: multicellularity, neurons and the cognitive lens’.

scholarly journals Brain network dynamics are hierarchically organized in time

2017 ◽  
Vol 114 (48) ◽  
pp. 12827-12832 ◽  
Author(s):  
Diego Vidaurre ◽  
Stephen M. Smith ◽  
Mark W. Woolrich
Keyword(s):  
Large Scale ◽  
Temporal Dynamics ◽  
Brain Network ◽  
Specific Measure ◽  
Brain Areas ◽  
Neuronal Populations ◽  
Subject Specific ◽  
Large Scale Networks ◽  
Network Patterns ◽  
The Brain

The brain recruits neuronal populations in a temporally coordinated manner in task and at rest. However, the extent to which large-scale networks exhibit their own organized temporal dynamics is unclear. We use an approach designed to find repeating network patterns in whole-brain resting fMRI data, where networks are defined as graphs of interacting brain areas. We find that the transitions between networks are nonrandom, with certain networks more likely to occur after others. Further, this nonrandom sequencing is itself hierarchically organized, revealing two distinct sets of networks, or metastates, that the brain has a tendency to cycle within. One metastate is associated with sensory and motor regions, and the other involves areas related to higher order cognition. Moreover, we find that the proportion of time that a subject spends in each brain network and metastate is a consistent subject-specific measure, is heritable, and shows a significant relationship with cognitive traits.

scholarly journals Attention Networks and the Intrinsic Network Structure of the Human Brain

2021 ◽  
Author(s):  
Sebastian Markett ◽  
David Nothdurfter ◽  
Antonia Focsa ◽  
Martin Reuter ◽  
Philippe Jawinski
Keyword(s):  
Network Structure ◽  
Network Theory ◽  
Large Scale ◽  
Brain Connectivity ◽  
Resting State Fmri ◽  
Relevant Information ◽  
Attention Network ◽  
Attention Networks ◽  
Large Scale Networks ◽  
The Brain

Attention network theory states that attention is not a unified construct but consists of three independent systems that are supported by separable distributed networks: an alerting network to deploy attentional resources in anticipation of upcoming events, an orienting network to direct attention to a cued location, and a control network to select relevant information at the expense of concurrently available information. Ample behavioral and neuroimaging evidence supports the dissociation of the three attention domains. The strong assumption that each attentional system is realized through a separable network, however, raises the question how these networks relate to the intrinsic network structure of the brain. Our understanding of brain networks has advanced majorly in the past years due to the increasing focus on brain connectivity. It is well established that the brain is intrinsically organized into several large-scale networks whose modular structure persists across task states. Existing proposals on how the presumed attention networks relate to intrinsic networks rely mostly on anecdotal and partly contradictory arguments. We addressed this issue by mapping different attention networks with highest spatial precision at the level of cifti-grayordinates. Resulting group maps were compared to the group-level topology of 23 intrinsic networks which we reconstructed from the same participants' resting state fMRI data. We found that all attention domains recruited multiple and partly overlapping intrinsic networks and converged in the dorsal fronto-parietal and midcingulo-insular network. While we observed a preference of each attentional domain for its own set of intrinsic networks, implicated networks did not match well to those proposed in the literature. Our results indicate a necessary refinement of the attention network theory.

scholarly journals Trips and Neurotransmitters: Discovering Principled Patterns across 6,850 Hallucinogenic Experiences

2021 ◽  
Author(s):  
Galen Ballentine ◽  
Sam Freesun Friedman ◽  
Danilo Bzdok
Keyword(s):  
Large Scale ◽  
Free Form ◽  
Association Cortex ◽  
Receptor Subtypes ◽  
Drug Induced ◽  
Psychological Phenomena ◽  
Input Pole ◽  
Large Scale Networks ◽  
Induced Changes ◽  
The Brain

Psychedelics are thought to alter states of consciousness by disrupting how the higher association cortex governs bottom-up sensory signals. Individual hallucinogenic drugs are usually studied in participants in controlled laboratory settings. Here, we have explored word usage in 6,850 free-form testimonials with 27 drugs through the prism of 40 neurotransmitter receptor subtypes, which were then mapped to 3D coordinates in the brain via their gene transcription levels from invasive tissue probes. Despite the variable subjective nature of hallucinogenic experiences, our pattern-learning approach delineated how drug-induced changes of conscious awareness (e.g., dissolving self-world boundaries or fractal distortion of visual perception) are linked to cortex-wide anatomical distributions of receptor density proxies. The dominant explanatory factor related ego-dissolution-like phenomena to a constellation of 5-HT2A, D2, KOR, and NMDA receptors, anchored especially in the brain's deep hierarchy (epitomized by the associative higher-order cortex) and shallow hierarchy (epitomized by the visual cortex). Additional factors captured psychological phenomena in which emotions (5-HT2A and Imidazoline1) were in tension with auditory (SERT, 5-HT1A) or visual (5-HT2A) sensations. Each discovered receptor-experience factor spanned between a higher-level association pole and a sensory input pole, which may relate to the previously reported collapse of hierarchical order among large-scale networks. Simultaneously considering many psychoactive molecules and thousands of natural language descriptions of drug experiences our framework finds the underlying semantic structure and maps it directly to the brain. These advances could assist in unlocking their wide-ranging potential for medical treatment.

scholarly journals Emergence of border-ownership by large-scale consistency and long-range interactions: Neuro-computational model to reflect global configurations

2021 ◽  
Author(s):  
Naoki Kogo ◽  
Vicky Froyen
Keyword(s):  
Surface Properties ◽  
Large Scale ◽  
Computational Models ◽  
Depth Order ◽  
Global Configuration ◽  
Long Range Interactions ◽  
The Brain ◽  
Computational Processes ◽  
Advanced Version ◽  
Ground Organization

The visual system performs remarkably well to perceive depth order of surfaces without stereo disparity, indicating the importance of figure-ground organization based on pictorial cues. To understand how figure-ground organization emerges, it is essential to investigate how the global configuration of an image is reflected. In the past, many neuro-computational models developed to reproduce figure-ground organization implemented algorithms to give a bias to convex areas. However, in certain conditions, a convex area can be perceived as a hole and a non-convex area as figural. This occurs when the surface properties of the convex area are consistent with the background and, hence, are grouped together in our perception. We argue that large-scale consistency of surface properties is reflected in the border-ownership computation. We developed a model, called DISC2, that first analyzes relationships between two border-ownership signals of all possible combinations in the image. It then enhances signals if they satisfy the following conditions: 1. the two signals fit to a convex configuration, and 2. the surface properties at the locations of the two signals are consistent. The strength of the enhancement decays with distance between the signals. The model gives extremely robust responses to various images with complexities both in shape and depth order. Furthermore, we developed an advanced version of the model ("augmented model") where the global computation above interacts with local computation of curvilinearity, which further enhanced the robust nature of the model. The results suggest the involvement of similar computational processes in the brain for figure-ground organization.

scholarly journals Post-Stroke Cognitive Impairment: Pathophysiological Insights into Brain Disconnectome from Advanced Neuroimaging Analysis Techniques

2021 ◽  
Vol 23 (3) ◽  
pp. 297-311
Author(s):  
Jae-Sung Lim ◽  
Jae-Joong Lee ◽  
Choong-Wan Woo
Keyword(s):  
Cognitive Impairment ◽  
Large Scale ◽  
Imaging Techniques ◽  
Brain Network ◽  
Clinical Neurology ◽  
Post Stroke ◽  
Large Scale Networks ◽  
High Level ◽  
New Treatments ◽  
The Brain

The neurological symptoms of stroke have traditionally provided the foundation for functional mapping of the brain. However, there are many unresolved aspects in our understanding of cerebral activity, especially regarding high-level cognitive functions. This review provides a comprehensive look at the pathophysiology of post-stroke cognitive impairment in light of recent findings from advanced imaging techniques. Combining network neuroscience and clinical neurology, our research focuses on how changes in brain networks correlate with post-stroke cognitive prognosis. More specifically, we first discuss the general consequences of stroke lesions due to damage of canonical resting-state large-scale networks or changes in the composition of the entire brain. We also review emerging methods, such as lesion-network mapping and gradient analysis, used to study the aforementioned events caused by stroke lesions. Lastly, we examine other patient vulnerabilities, such as superimposed amyloid pathology and blood-brain barrier leakage, which potentially lead to different outcomes for the brain network compositions even in the presence of similar stroke lesions. This knowledge will allow a better understanding of the pathophysiology of post-stroke cognitive impairment and provide a theoretical basis for the development of new treatments, such as neuromodulation.

Research on the Development and Applications of Artificial Neural Networks

2014 ◽  
Vol 556-562 ◽  
pp. 6011-6014 ◽  
Author(s):  
Xiao Guang Li
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Intelligent Control ◽  
Computational Models ◽  
Bayesian Probability ◽  
Control Techniques ◽  
Central Nervous Systems ◽  
Artificial Neural ◽  
The Brain

Intelligent control is a class of control techniques that use various AI computing approaches like neural networks, Bayesian probability, fuzzy logic, machine learning, evolutionary computation and genetic algorithms. In computer science and related fields, artificial neural networks are computational models inspired by animals’ central nervous systems (in particular the brain) that are capable of machine learning and pattern recognition. They are usually presented as systems of interconnected “neurons” that can compute values from inputs by feeding information through the network. Like other machine learning methods, neural networks have been used to solve a wide variety of tasks that are hard to solve using ordinary rule-based programming, including computer vision and speech recognition.

scholarly journals Predicting the Quality of Short Narratives from Social Media

2017 ◽  
Author(s):  
Tong Wang ◽  
Ping Chen ◽  
Boyang Li
Keyword(s):  
Neural Networks ◽  
Social Media ◽  
Large Scale ◽  
Computational Models ◽  
Quality Evaluation ◽  
Future Research ◽  
Automatic Evaluation ◽  
Need To Evaluate ◽  
Difficult Challenge

An important and difficult challenge in building computational models for narratives is the automatic evaluation of narrative quality. Quality evaluation connects narrative understanding and generation as generation systems need to evaluate their own products. To circumvent difficulties in acquiring annotations, we employ upvotes in social media as an approximate measure for story quality. We collected 54,484 answers from a crowd-powered question-and-answer website, Quora, and then used active learning to build a classifier that labeled 28,320 answers as stories. To predict the number of upvotes without the use of social network features, we create neural networks that model textual regions and the interdependence among regions, which serve as strong benchmarks for future research. To our best knowledge, this is the first large-scale study for automatic evaluation of narrative quality.

Sign in / Sign up

Export Citation Format

Share Document