Integrated Intrinsic and Dedicated Representations of Time: A Computational Study Involving Robotic Agents

2015 ◽  
Vol 3 (3-4) ◽  
pp. 246-268 ◽  
Author(s):  
Michail Maniadakis ◽  
Panos Trahanias
Keyword(s):  
Computational Models ◽  
Computational Study ◽  
A Priori ◽  
Design Procedure ◽  
Careful Examination ◽  
Time Representation ◽  
Simple Neural Network ◽  
Representation Scheme ◽  
Processing Mechanisms ◽  
The Brain

The computational modeling of cognitive processes provides a systematic means to study hidden and particularly complex aspects of brain functionality. Given our rather limited understanding of how the brain deals with the notion of time, the implementation of computational models addressing duration processing can be particularly informative for studying possible time representations in our brain. In the present work we adopt a connectionist modeling approach to study how time experiencing and time processing may be encoded in a simple neural network trained to accomplish time-based robotic tasks. A particularly interesting characteristic of the present study is the implementation of a single computational model to accomplish not only one but three different behavioral tasks that assume diverse manipulation of time intervals. This setup enables a multifaceted exploration of duration-processing mechanisms, revealing a rather plausible hypothesis of how our brain deals with time. The model is implemented through an evolutionary design procedure, making a very limited set of a priori assumptions regarding its internal structure and machinery. Artificial evolution facilitates the unconstrained self-organization of time representation and processing mechanisms in the brain of simulated robotic agents. Careful examination of the artificial brains has shown that the implemented mechanisms incorporate characteristics from both the ‘intrinsic’ time representation scheme and the ‘dedicated’ time representation scheme. Even though these two schemes are widely considered as contradictory, the present study shows that it is possible to effectively integrate them in the same cognitive system. This provides a new view on the possible representation of time in the brain, and paves the way for new and more comprehensive theories to address interval timing.

scholarly journals Experimental and computational study on motor control and recovery after stroke: towards a constructive loop between experimental and virtual embodied neuroscience

2020 ◽  
Author(s):  
Anna Letizia Allegra Mascaro ◽  
Egidio Falotico ◽  
Spase Petkoski ◽  
Maria Pasquini ◽  
Lorenzo Vannucci ◽  
...  
Keyword(s):  
Experimental Data ◽  
Motor Control ◽  
Computational Models ◽  
Computational Study ◽  
Motor Rehabilitation ◽  
Modeling Framework ◽  
First Results ◽  
Traditional Approaches ◽  
The Brain ◽  
Multiple Granularities

ABSTRACTBeing able to replicate real experiments with computational simulations is a unique opportunity to refine and validate models with experimental data and redesign the experiments based on simulations. However, since it is technically demanding to model all components of an experiment, traditional approaches to modeling reduce the experimental setups as much as possible. In this study, our goal is to replicate all the relevant features of an experiment on motor control and motor rehabilitation after stroke. To this aim, we propose an approach that allows continuous integration of new experimental data into a computational modeling framework. First, results show that we could reproduce experimental object displacement with high accuracy via the simulated embodiment in the virtual world by feeding a spinal cord model with experimental registration of the cortical activity. Second, by using computational models of multiple granularities, our preliminary results show the possibility of simulating several features of the brain after stroke, from the local alteration in neuronal activity to long-range connectivity remodeling. Finally, strategies are proposed to merge the two pipelines. We further suggest that additional models could be integrated into the framework thanks to the versatility of the proposed approach, thus allowing many researchers to achieve continuously improved experimental design.

A combined experimental and computational study of mechanical properties after balloon kyphoplasty

2021 ◽  
pp. 095441192110139
Author(s):  
Philip Purcell ◽  
Fiona McEvoy ◽  
Stephen Tiernan ◽  
Derek Sweeney ◽  
Seamus Morris
Keyword(s):  
Computational Models ◽  
Computational Study ◽  
Case Reports ◽  
Experimental Tests ◽  
Balloon Kyphoplasty ◽  
Fatigue Tests ◽  
Vertebral Compression Fractures ◽  
Baseline Case ◽  
Strength And Stiffness ◽  
Stiffness Loss

Vertebral compression fractures rank among the most frequent injuries to the musculoskeletal system, with more than 1 million fractures per annum worldwide. The past decade has seen a considerable increase in the utilisation of surgical procedures such as balloon kyphoplasty to treat these injuries. While many kyphoplasty studies have examined the risk of damage to adjacent vertebra after treatment, recent case reports have also emerged to indicate the potential for the treated vertebra itself to re-collapse after surgery. The following study presents a combined experimental and computational study of balloon kyphoplasty which aims to establish a methodology capable of evaluating these cases of vertebral re-collapse. Results from both the experimental tests and computational models showed significant increases in strength and stiffness after treatment, by factors ranging from 1.44 to 1.93, respectively. Fatigue tests on treated specimens showed a 37% drop in the rate of stiffness loss compared to the untreated baseline case. Further analysis of the computational models concluded that inhibited PMMA interdigitation at the interface during kyphoplasty could reverse improvements in strength and stiffness that could otherwise be gained by the treatment.

scholarly journals Power Failure of Mitochondria and Oxidative Stress in Neurodegeneration and Its Computational Models

Antioxidants ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 229
Author(s):  
JunHyuk Woo ◽  
Hyesun Cho ◽  
YunHee Seol ◽  
Soon Ho Kim ◽  
Chanhyeok Park ◽  
...  
Keyword(s):  
Oxidative Stress ◽  
Mitochondrial Dysfunction ◽  
Computational Models ◽  
Neuronal Damage ◽  
Living Organism ◽  
The Body ◽  
Mitochondrial Functions ◽  
A Cell ◽  
The Brain ◽  
And Oxidative Stress

The brain needs more energy than other organs in the body. Mitochondria are the generator of vital power in the living organism. Not only do mitochondria sense signals from the outside of a cell, but they also orchestrate the cascade of subcellular events by supplying adenosine-5′-triphosphate (ATP), the biochemical energy. It is known that impaired mitochondrial function and oxidative stress contribute or lead to neuronal damage and degeneration of the brain. This mini-review focuses on addressing how mitochondrial dysfunction and oxidative stress are associated with the pathogenesis of neurodegenerative disorders including Alzheimer’s disease, amyotrophic lateral sclerosis, Huntington’s disease, and Parkinson’s disease. In addition, we discuss state-of-the-art computational models of mitochondrial functions in relation to oxidative stress and neurodegeneration. Together, a better understanding of brain disease-specific mitochondrial dysfunction and oxidative stress can pave the way to developing antioxidant therapeutic strategies to ameliorate neuronal activity and prevent neurodegeneration.

Hydrodynamic Design and Analysis of Horizontal Axis Marine Current Turbines With Lifting Line and Panel Methods

2011 ◽  
Author(s):  
J. Baltazar ◽  
J. Machado ◽  
J. A. C. Falca˜o de Campos
Keyword(s):  
Computational Models ◽  
Design Procedure ◽  
Horizontal Axis ◽  
Complete Analysis ◽  
Speed Ratio ◽  
Integral Boundary ◽  
Power Extraction ◽  
Marine Current ◽  
Lifting Line ◽  
Blade Geometry

This paper presents the computational models used by the authors at MARETEC/IST for hydrodynamic design and analysis of horizontal axis marine current turbines. The models combine a lifting line method for the optimization of the turbine blade geometry and an Integral Boundary Element Method (IBEM) for the hydrodynamic analysis. The classical lifting line optimization is used to determine the optimum blade circulation distribution for maximum power extraction. Blade geometry is determined with simplified cavitation requirements and limitations due to mechanical strength. The application of the design procedure is illustrated for a two-bladed 300 kW marine current turbine with a diameter of 11 meters. The effects of design tip-speed-ratio and the influence of blade section foils on power and cavitation inception are discussed. A more complete analysis may be carried out with an IBEM in steady and unsteady flow conditions. The IBEM has been extended to include wake alignment. The results are compared with experimental performance data available in the literature.

scholarly journals Inferring brain-computational mechanisms with models of activity measurements

2016 ◽  
Vol 371 (1705) ◽  
pp. 20160278 ◽  
Author(s):  
Nikolaus Kriegeskorte ◽  
Jörn Diedrichsen
Keyword(s):  
Computational Models ◽  
Brain Activity ◽  
Visual Object ◽  
Visual Object Recognition ◽  
Summary Statistics ◽  
Activity Data ◽  
Individual Measurement ◽  
Activity Measurements ◽  
The Brain ◽  
The Way

High-resolution functional imaging is providing increasingly rich measurements of brain activity in animals and humans. A major challenge is to leverage such data to gain insight into the brain's computational mechanisms. The first step is to define candidate brain-computational models (BCMs) that can perform the behavioural task in question. We would then like to infer which of the candidate BCMs best accounts for measured brain-activity data. Here we describe a method that complements each BCM by a measurement model (MM), which simulates the way the brain-activity measurements reflect neuronal activity (e.g. local averaging in functional magnetic resonance imaging (fMRI) voxels or sparse sampling in array recordings). The resulting generative model (BCM-MM) produces simulated measurements. To avoid having to fit the MM to predict each individual measurement channel of the brain-activity data, we compare the measured and predicted data at the level of summary statistics. We describe a novel particular implementation of this approach, called probabilistic representational similarity analysis (pRSA) with MMs, which uses representational dissimilarity matrices (RDMs) as the summary statistics. We validate this method by simulations of fMRI measurements (locally averaging voxels) based on a deep convolutional neural network for visual object recognition. Results indicate that the way the measurements sample the activity patterns strongly affects the apparent representational dissimilarities. However, modelling of the measurement process can account for these effects, and different BCMs remain distinguishable even under substantial noise. The pRSA method enables us to perform Bayesian inference on the set of BCMs and to recognize the data-generating model in each case. This article is part of the themed issue ‘Interpreting BOLD: a dialogue between cognitive and cellular neuroscience’.

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’.

Perspectives on Cognitive Informatics and Cognitive Computing

2010 ◽  
Vol 4 (1) ◽  
pp. 1-29 ◽  
Author(s):  
Yingxu Wang ◽  
George Baciu ◽  
Yiyu Yao ◽  
Witold Kinsner ◽  
Keith Chan ◽  
...  
Keyword(s):  
Computational Linguistics ◽  
Computational Intelligence ◽  
Cognitive Computing ◽  
Cognitive Informatics ◽  
Intelligent Computing ◽  
Internal Information ◽  
Processing Mechanisms ◽  
The Brain ◽  
Science Information ◽  
Intelligence Science

Cognitive informatics is a transdisciplinary enquiry of computer science, information sciences, cognitive science, and intelligence science that investigates the internal information processing mechanisms and processes of the brain and natural intelligence, as well as their engineering applications in cognitive computing. Cognitive computing is an emerging paradigm of intelligent computing methodologies and systems based on cognitive informatics that implements computational intelligence by autonomous inferences and perceptions mimicking the mechanisms of the brain. This article presents a set of collective perspectives on cognitive informatics and cognitive computing, as well as their applications in abstract intelligence, computational intelligence, computational linguistics, knowledge representation, symbiotic computing, granular computing, semantic computing, machine learning, and social computing.

scholarly journals Distributed Code for Semantic Relations Predicts Neural Similarity

2019 ◽  
Author(s):  
Jeffrey N. Chiang ◽  
Yujia Peng ◽  
Hongjing Lu ◽  
Keith J. Holyoak ◽  
Martin M. Monti
Keyword(s):  
Computational Models ◽  
Semantic Network ◽  
Building Blocks ◽  
Semantic Relations ◽  
Human Cognition ◽  
Sequential Presentation ◽  
Specific Content ◽  
Distributed Representations ◽  
Relational Similarity ◽  
The Brain

AbstractThe ability to generate and process semantic relations is central to many aspects of human cognition. Theorists have long debated whether such relations are coded as atomistic links in a semantic network, or as distributed patterns over some core set of abstract relations. The form and content of the conceptual and neural representations of semantic relations remains to be empirically established. The present study combined computational modeling and neuroimaging to investigate the representation and comparison of abstract semantic relations in the brain. By using sequential presentation of verbal analogies, we decoupled the neural activity associated with encoding the representation of the first-order semantic relation between words in a pair from that associated with the second-order comparison of two relations. We tested alternative computational models of relational similarity in order to distinguish between rival accounts of how semantic relations are coded and compared in the brain. Analyses of neural similarity patterns supported the hypothesis that semantic relations are coded, in the parietal cortex, as distributed representations over a pool of abstract relations specified in a theory-based taxonomy. These representations, in turn, provide the immediate inputs to the process of analogical comparison, which draws on a broad frontoparietal network. This study sheds light not only on the form of relation representations but also on their specific content.SignificanceRelations provide basic building blocks for language and thought. For the past half century, cognitive scientists exploring human semantic memory have sought to identify the code for relations. In a neuroimaging paradigm, we tested alternative computational models of relation processing that predict patterns of neural similarity during distinct phases of analogical reasoning. The findings allowed us to draw inferences not only about the form of relation representations, but also about their specific content. The core of these distributed representations is based on a relatively small number of abstract relation types specified in a theory-based taxonomy. This study helps to resolve a longstanding debate concerning the nature of the conceptual and neural code for semantic relations in the mind and brain.

scholarly journals Comparing supervised and unsupervised approaches to emotion categorization in the human brain, body, and subjective experience.

2020 ◽  
Author(s):  
Bahar Azari ◽  
Christiana Westlin ◽  
Ajay Satpute ◽  
J. Benjamin Hutchinson ◽  
Philip A. Kragel ◽  
...  
Keyword(s):  
Human Brain ◽  
Subjective Experience ◽  
A Priori ◽  
Ground Truth ◽  
Test Case ◽  
Physical Measurements ◽  
Supervised Classifiers ◽  
Emotional Episodes ◽  
Emotion Labels ◽  
The Brain

Machine learning methods provide powerful tools to map physical measurements to scientific categories. But are such methods suitable for discovering the ground truth about psychological categories? We use the science of emotion as a test case to explore this question. In studies of emotion, researchers use supervised classifiers, guided by emotion labels, to attempt to discover biomarkers in the brain or body for the corresponding emotion categories. This practice relies on the assumption that the labels refer to objective categories that can be discovered. Here, we critically examine this approach across three distinct datasets collected during emotional episodes- measuring the human brain, body, and subjective experience- and compare supervised classification studies with those from unsupervised clustering in which no a priori labels are assigned to the data. We conclude with a set of recommendations to guide researchers towards meaningful, data-driven discoveries in the science of emotion and beyond.

Early Experience Determines How the Senses Will Interact

2007 ◽  
Vol 97 (1) ◽  
pp. 921-926 ◽  
Author(s):  
Mark T. Wallace ◽  
Barry E. Stein
Keyword(s):  
Superior Colliculus ◽  
Multisensory Integration ◽  
A Priori ◽  
Spatial Relationship ◽  
Early Experience ◽  
Auditory Stimuli ◽  
Anomalous Form ◽  
Sensory Environment ◽  
External Events ◽  
The Brain

Multisensory integration refers to the process by which the brain synthesizes information from different senses to enhance sensitivity to external events. In the present experiments, animals were reared in an altered sensory environment in which visual and auditory stimuli were temporally coupled but originated from different locations. Neurons in the superior colliculus developed a seemingly anomalous form of multisensory integration in which spatially disparate visual-auditory stimuli were integrated in the same way that neurons in normally reared animals integrated visual-auditory stimuli from the same location. The data suggest that the principles governing multisensory integration are highly plastic and that there is no a priori spatial relationship between stimuli from different senses that is required for their integration. Rather, these principles appear to be established early in life based on the specific features of an animal's environment to best adapt it to deal with that environment later in life.

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