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Reduced representation of neural networks
Title
Reduced representation of neural networks.
Creator
Stefanescu, Roxana A., Charles E. Schmidt College of Science, Department of Physics
Abstract/Description
Experimental and computational investigations addressing how various neural functions are achieved in the brain converged in recent years to a unified idea that the neural activity underlying most of the cognitive functions is distributed over large scale networks comprising various cortical and subcortical areas. Modeling approaches represent these areas and their connections using diverse models of neurocomputational units engaged in graph-like or neural field-like structures. Regardless of...
Show moreExperimental and computational investigations addressing how various neural functions are achieved in the brain converged in recent years to a unified idea that the neural activity underlying most of the cognitive functions is distributed over large scale networks comprising various cortical and subcortical areas. Modeling approaches represent these areas and their connections using diverse models of neurocomputational units engaged in graph-like or neural field-like structures. Regardless of the manner of network implementation, simulations of large scale networks have encountered significant difficulties mainly due to the time delay introduced by the long range connections. To decrease the computational effort, it is common to assume severe approximations to simplify the descriptions of the neural dynamics associated with the system's units. In this dissertation we propose an alternative framework allowing the prevention of such strong assumptions while efficiently representing th e dynamics of a complex neural network. First, we consider the dynamics of small scale networks of globally coupled non-identical excitatory and inhibitory neurons, which could realistically instantiate a neurocomputational unit. We identify the most significant dynamical features the neural population exhibits in different parametric configuration, including multi-cluster dynamics, multi-scale synchronization and oscillator death. Then, using mode decomposition techniques, we construct analytically low dimensional representations of the network dynamics and show that these reduced systems capture the dynamical features of the entire neural population. The cases of linear and synaptic coupling are discussed in detail. In chapter 5, we extend this approach for spatially extended neural networks., We consider the dynamical behavior of a neural field-like network, which incorporates many biologically realistic characteristics such as heterogeneous local and global connectivity as well as dispersion in the neural membrane excitability. We show that in this case as well, we can construct a reduced representation, which may capture well the dynamical features of the full system. The method outlined in this dissertation provides a consistent way to represent complex dynamical features of various neural networks in a computationally efficient manner.
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Date Issued
2009
PURL
http://purl.flvc.org/FAU/369387
Subject Headings
Molecular neurobiology, Neural networks (Neurobiology), Brain, Mathematical models, Cognitive neuroscience, Recognition (Psychology)
Format
Document (PDF)
A COMPARISON OF TASK RELEVANT NODE IDENTIFICATION TECHNIQUES AND THEIR IMPACT ON NETWORK INFERENCES: GROUP-AGGREGATED, SUBJECT-SPECIFIC, AND VOXEL WISE APPROACHES
Title
A COMPARISON OF TASK RELEVANT NODE IDENTIFICATION TECHNIQUES AND THEIR IMPACT ON NETWORK INFERENCES: GROUP-AGGREGATED, SUBJECT-SPECIFIC, AND VOXEL WISE APPROACHES.
Creator
Falco, Dimitri, Bressler, Steven L., Florida Atlantic University, Center for Complex Systems and Brain Sciences, Charles E. Schmidt College of Science
Abstract/Description
The dissertation discusses various node identification techniques as well as their downstream effects on network characteristics using task-activated fMRI data from two working memory paradigms: a verbal n-back task and a visual n-back task. The three node identification techniques examined within this work include: a group-aggregated approach, a subject-specific approach, and a voxel wise approach. The first chapters highlight crucial differences between group-aggregated and subject-specific...
Show moreThe dissertation discusses various node identification techniques as well as their downstream effects on network characteristics using task-activated fMRI data from two working memory paradigms: a verbal n-back task and a visual n-back task. The three node identification techniques examined within this work include: a group-aggregated approach, a subject-specific approach, and a voxel wise approach. The first chapters highlight crucial differences between group-aggregated and subject-specific methods of isolating nodes prior to undirected functional connectivity analysis. Results show that the two techniques yield significantly different network interactions and local network characteristics, despite having their network nodes restricted to the same anatomical regions. Prior to the introduction of the third technique, a chapter is dedicated to explaining the differences between a priori approaches (like the previously introduced group-aggregated and subject-specific techniques) and no a priori approaches (like the voxel wise approach). The chapter also discusses two ways to aggregate signal for node representation within a network: using the signal from a single voxel or aggregating signal across a group of neighboring voxels. Subsequently, a chapter is dedicated to introducing a novel processing pipeline which uses a data driven voxel wise approach to identify network nodes. The novel pipeline defines nodes using spatial temporal features generated by a deep learning algorithm and is validated by an analysis showing that the isolated nodes are condition and subject specific. The dissertation concludes by summarizing the main takeaways from each of the three analyses as well as highlighting the advantages and disadvantages of each of the three node identification techniques.
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Date Issued
2020
PURL
http://purl.flvc.org/fau/fd/FA00013553
Subject Headings
Functional magnetic resonance imaging, Brain mapping, Working memory, Neural networks (Neurobiology), Neuroimaging--methods
Format
Document (PDF)
"Stop-It anti-fidgeting device
Title
The "Stop-It anti-fidgeting device.
Creator
Barnard, Scott A., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
Fidgeting and otherwise constant movements in individuals can be beneficial in those who suffer from Attention Deficit/Hyperactivity Disorder or Generalized Anxiety Disorder as well as others. However this constant movement can also be a distraction to others as well as protrude an air of no self confidence. It is the drawbacks from these actions that we wish to address. By developing an intelligent system that can detect these motions and alert the user, it will allow the wearer of the...
Show moreFidgeting and otherwise constant movements in individuals can be beneficial in those who suffer from Attention Deficit/Hyperactivity Disorder or Generalized Anxiety Disorder as well as others. However this constant movement can also be a distraction to others as well as protrude an air of no self confidence. It is the drawbacks from these actions that we wish to address. By developing an intelligent system that can detect these motions and alert the user, it will allow the wearer of the device to self correct. It is in this self control that one may exhibit more confidence or simply reduce the level of irritation experienced by those in the immediate vicinity. We have designed and built a low cost, mobile, lightweight, untethered, wearable prototype device. It will detect these actions and deliver user selectable biofeedback through a light emitting diode, buzzer, vibromotor or an electric shock to allow for self control.
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Date Issued
2009
PURL
http://purl.flvc.org/FAU/368612
Subject Headings
Restless legs syndrome, Treatment, Technological innovations, Agitation (Psychology), Biomedical engineering, Neural networks (Neurobiology)
Format
Document (PDF)
neural correlates of endogenously cued covert visuospatial attentional shifting in the cue-target interval
Title
The neural correlates of endogenously cued covert visuospatial attentional shifting in the cue-target interval: an electroencephalographic study.
Creator
Modestino, Edward Justin., Charles E. Schmidt College of Science, Center for Complex Systems and Brain Sciences
Abstract/Description
This study investigated electroencephalographic differences related to cue (central left- or right-directed arrows) in a covert endogenous visual spatial attention task patterned after that of Hopf and Mangun (2000). This was done with the intent of defining the timing of components in relation to cognitive processes within the cue-target interval. Multiple techniques were employed to do this. Event-related potentials (ERPs) were examined using Independent Component Analysis. This revealed a...
Show moreThis study investigated electroencephalographic differences related to cue (central left- or right-directed arrows) in a covert endogenous visual spatial attention task patterned after that of Hopf and Mangun (2000). This was done with the intent of defining the timing of components in relation to cognitive processes within the cue-target interval. Multiple techniques were employed to do this. Event-related potentials (ERPs) were examined using Independent Component Analysis. This revealed a significant N1, between 100:200 ms post-cue, greater contralateral to the cue. Difference wave ERPs, left minus right cue-locked data, divulged significant early directing attention negativity (EDAN) at 200:400 ms post-cue in the right posterior which reversed polarity in the left posterior. Temporal spectral evolution (TSE) analysis of the alpha band revealed three stages, (1) high bilateral alpha precue to 120 ms post-cue, (2) an event related desynchronization (ERD) from approximately 120 ms: 500 ms post-cue, and (3) an event related synchronization (ERS) rebound, 500: 900 ms post-cue, where alpha amplitude, a measure of activity, was highest contralateral to the ignored hemifield and lower contralateral to the attended hemifield. Using a combination of all of these components and scientific literature in this field, it is possible to plot out the time course of the cognitive events and their neural correlates.
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Date Issued
2009
PURL
http://purl.flvc.org/FAU/369199
Subject Headings
Brain mapping, Neural networks (Neurobiology), Cognitive neuroscience, Recognition (Psychology), Cognition, Research, Methodology
Format
Document (PDF)
Fractal ion-channel behavior generates fractal firing patterns in neuronal models
Title
Fractal ion-channel behavior generates fractal firing patterns in neuronal models.
Creator
Liebovitch, Larry S., Lowen, Steven B., White, John A.
Date Issued
1999-05
PURL
http://purl.flvc.org/fau/165477
Subject Headings
Biophysics--Research, Fractals, Neural networks (Neurobiology)--Mathematical models, Neurons--Mathematical models, Markov processes--Mathematical models, Ion channels--Mathematical models
Format
Document (PDF)