@article {67675, title = {Structured patterns of activity in pulse-coupled oscillator networks with varied connectivity.}, journal = {PLoS One}, volume = {16}, year = {2021}, month = {2021 Aug 11}, pages = {e0256034}, abstract = {

Identifying coordinated activity within complex systems is essential to linking their structure and function. We study collective activity in networks of pulse-coupled oscillators that have variable network connectivity and integrate-and-fire dynamics. Starting from random initial conditions, we see the emergence of three broad classes of behaviors that differ in their collective spiking statistics. In the first class ("temporally-irregular"), all nodes have variable inter-spike intervals, and the resulting firing patterns are irregular. In the second ("temporally-regular"), the network generates a coherent, repeating pattern of activity in which all nodes fire with the same constant inter-spike interval. In the third ("chimeric"), subgroups of coherently-firing nodes coexist with temporally-irregular nodes. Chimera states have previously been observed in networks of oscillators; here, we find that the notions of temporally-regular and chimeric states encompass a much richer set of dynamical patterns than has yet been described. We also find that degree heterogeneity and connection density have a strong effect on the resulting state: in binomial random networks, high degree variance and intermediate connection density tend to produce temporally-irregular dynamics, while low degree variance and high connection density tend to produce temporally-regular dynamics. Chimera states arise with more frequency in networks with intermediate degree variance and either high or low connection densities. Finally, we demonstrate that a normalized compression distance, computed via the Lempel-Ziv complexity of nodal spike trains, can be used to distinguish these three classes of behavior even when the phase relationship between nodes is arbitrary.

}, issn = {1932-6203}, doi = {10.1371/journal.pone.0256034}, author = {Kadhim, Kyra L and Hermundstad, Ann M and Brown, Kevin S} } @article {47704, title = {Structurally-constrained relationships between cognitive states in the human brain.}, journal = {PLoS computational biology}, volume = {10}, year = {2014}, month = {2014 May}, pages = {e1003591}, abstract = {

The anatomical connectivity of the human brain supports diverse patterns of correlated neural activity that are thought to underlie cognitive function. In a manner sensitive to underlying structural brain architecture, we examine the extent to which such patterns of correlated activity systematically vary across cognitive states. Anatomical white matter connectivity is compared with functional correlations in neural activity measured via blood oxygen level dependent (BOLD) signals. Functional connectivity is separately measured at rest, during an attention task, and during a memory task. We assess these structural and functional measures within previously-identified resting-state functional networks, denoted task-positive and task-negative networks, that have been independently shown to be strongly anticorrelated at rest but also involve regions of the brain that routinely increase and decrease in activity during task-driven processes. We find that the density of anatomical connections within and between task-positive and task-negative networks is differentially related to strong, task-dependent correlations in neural activity. The space mapped out by the observed structure-function relationships is used to define a quantitative measure of separation between resting, attention, and memory states. We find that the degree of separation between states is related to both general measures of behavioral performance and relative differences in task-specific measures of attention versus memory performance. These findings suggest that the observed separation between cognitive states reflects underlying organizational principles of human brain structure and function.

}, keywords = {Attention, Brain, Cognition, Computer Simulation, Connectome, Humans, Memory, Models, Anatomic, Models, Neurological, Nerve Net, White Matter}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1003591}, author = {Hermundstad, Ann M and Brown, Kevin S and Bassett, Danielle S and Aminoff, Elissa M and Frithsen, Amy and Johnson, Arianne and Tipper, Christine M and Miller, Michael B and Grafton, Scott T and Carlson, Jean M} } @article {47705, title = {Structural foundations of resting-state and task-based functional connectivity in the human brain.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {110}, year = {2013}, month = {2013 Apr 9}, pages = {6169-74}, abstract = {

Magnetic resonance imaging enables the noninvasive mapping of both anatomical white matter connectivity and dynamic patterns of neural activity in the human brain. We examine the relationship between the structural properties of white matter streamlines (structural connectivity) and the functional properties of correlations in neural activity (functional connectivity) within 84 healthy human subjects both at rest and during the performance of attention- and memory-demanding tasks. We show that structural properties, including the length, number, and spatial location of white matter streamlines, are indicative of and can be inferred from the strength of resting-state and task-based functional correlations between brain regions. These results, which are both representative of the entire set of subjects and consistently observed within individual subjects, uncover robust links between structural and functional connectivity in the human brain.

}, keywords = {Aging, Attention, Brain, Brain Mapping, Cognition, Computational Biology, Diffusion Magnetic Resonance Imaging, Humans, Magnetic Resonance Imaging, Memory, Models, Statistical, Neural Pathways, Software}, issn = {1091-6490}, doi = {10.1073/pnas.1219562110}, author = {Hermundstad, Ann M and Bassett, Danielle S and Brown, Kevin S and Aminoff, Elissa M and Clewett, David and Freeman, Scott and Frithsen, Amy and Johnson, Arianne and Tipper, Christine M and Miller, Michael B and Grafton, Scott T and Carlson, Jean M} } @article {47706, title = {Learning, memory, and the role of neural network architecture.}, journal = {PLoS computational biology}, volume = {7}, year = {2011}, month = {2011 Jun}, pages = {e1002063}, abstract = {

The performance of information processing systems, from artificial neural networks to natural neuronal ensembles, depends heavily on the underlying system architecture. In this study, we compare the performance of parallel and layered network architectures during sequential tasks that require both acquisition and retention of information, thereby identifying tradeoffs between learning and memory processes. During the task of supervised, sequential function approximation, networks produce and adapt representations of external information. Performance is evaluated by statistically analyzing the error in these representations while varying the initial network state, the structure of the external information, and the time given to learn the information. We link performance to complexity in network architecture by characterizing local error landscape curvature. We find that variations in error landscape structure give rise to tradeoffs in performance; these include the ability of the network to maximize accuracy versus minimize inaccuracy and produce specific versus generalizable representations of information. Parallel networks generate smooth error landscapes with deep, narrow minima, enabling them to find highly specific representations given sufficient time. While accurate, however, these representations are difficult to generalize. In contrast, layered networks generate rough error landscapes with a variety of local minima, allowing them to quickly find coarse representations. Although less accurate, these representations are easily adaptable. The presence of measurable performance tradeoffs in both layered and parallel networks has implications for understanding the behavior of a wide variety of natural and artificial learning systems.

}, keywords = {Computational Biology, Humans, Learning, Memory, Models, Neurological, Nerve Net, Neural Networks (Computer)}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1002063}, author = {Hermundstad, Ann M and Brown, Kevin S and Bassett, Danielle S and Carlson, Jean M} }