Publications
119 results found
Bhowmik D, Nikiforou K, Shanahan M, et al., 2016, A RESERVOIR COMPUTING MODEL OF EPISODIC MEMORY, International Joint Conference on Neural Networks (IJCNN), Publisher: IEEE, Pages: 5202-5209, ISSN: 2161-4393
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- Citations: 1
Shanahan MP, Hellyer P, Sharp DJ, et al., 2015, Cognitive flexibility through metastable neural dynamics is disrupted by damage to the structural connectome, Journal of Neuroscience, Vol: 35, Pages: 9050-9063, ISSN: 0270-6474
Current theory proposes that healthy neural dynamics operate in a metastable regime, where brain regions interact to simultaneously maximize integration and segregation. Metastability may confer important behavioral properties, such as cognitive flexibility. It is increasingly recognized that neural dynamics are constrained by the underlying structural connections between brain regions. An important challenge is, therefore, to relate structural connectivity, neural dynamics, and behavior. Traumatic brain injury (TBI) is a pre-eminent structural disconnection disorder whereby traumatic axonal injury damages large-scale connectivity, producing characteristic cognitive impairments, including slowed information processing speed and reduced cognitive flexibility, that may be a result of disrupted metastable dynamics. Therefore, TBI provides an experimental and theoretical model to examine how metastable dynamics relate to structural connectivity and cognition. Here, we use complementary empirical and computational approaches to investigate how metastability arises from the healthy structural connectome and relates to cognitive performance. We found reduced metastability in large-scale neural dynamics after TBI, measured with resting-state functional MRI. This reduction in metastability was associated with damage to the connectome, measured using diffusion MRI. Furthermore, decreased metastability was associated with reduced cognitive flexibility and information processing. A computational model, defined by empirically derived connectivity data, demonstrates how behaviorally relevant changes in neural dynamics result from structural disconnection. Our findings suggest how metastable dynamics are important for normal brain function and contingent on the structure of the human connectome.
Váša F, Shanahan M, Hellyer P, et al., 2015, Effects of lesions on synchrony and metastability in cortical networks, Neuroimage, Vol: 118, Pages: 456-467, ISSN: 1095-9572
At the macroscopic scale, the human brain can be described as a complex network of white matter tracts integrating grey matter assemblies — the human connectome. The structure of the connectome, which is often described using graph theoretic approaches, can be used to model macroscopic brain function at low computational cost. Here, we use the Kuramoto model of coupled oscillators with time-delays, calibrated with respect to empirical functional MRI data, to study the relation between the structure of the connectome and two aspects of functional brain dynamics — synchrony, a measure of general coherence, and metastability, a measure of dynamical flexibility. Specifically, we investigate the relationship between the local structure of the connectome, quantified using graph theory, and the synchrony and metastability of the model's dynamics. By removing individual nodes and all of their connections from the model, we study the effect of lesions on both global and local dynamics. Of the nine nodal graph-theoretical properties tested, two were able to predict effects of node lesion on the global dynamics. The removal of nodes with high eigenvector centrality leads to decreases in global synchrony and increases in global metastability, as does the removal of hub nodes joining topologically segregated network modules. At the level of local dynamics in the neighbourhood of the lesioned node, structural properties of the lesioned nodes hold more predictive power, as five nodal graph theoretical measures are related to changes in local dynamics following node lesions. We discuss these results in the context of empirical studies of stroke and functional brain dynamics.
Fountas Z, Shanahan M, 2015, GPU-based Fast Parameter Optimization for Phenomenological Spiking Neural Models, International Joint Conference on Neural Networks (IJCNN), Publisher: IEEE, ISSN: 2161-4393
Bhowmik D, Shanahan M, 2015, STDP Produces Well Behaved Oscillations and Synchrony, 4th International Conference on Cognitive Neurodynamics (ICCN), Publisher: SPRINGER, Pages: 241-252
Teixeira FPP, Shanahan M, 2015, Local and Global Criticality within Oscillating Networks of Spiking Neurons, International Joint Conference on Neural Networks (IJCNN), Publisher: IEEE, ISSN: 2161-4393
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- Citations: 1
Carhart-Harris RL, Leech R, Hellyer PJ, et al., 2014, The entropic brain: a theory of conscious states informed by neuroimaging research with psychedelic drugs, Frontiers in Human Neuroscience, Vol: 8, Pages: 1-22, ISSN: 1662-5161
Entropy is a dimensionless quantity that is used for measuring uncertainty about the state of a system but it can also imply physical qualities, where high entropy is synonymous with high disorder. Entropy is applied here in the context of states of consciousness and their associated neurodynamics, with a particular focus on the psychedelic state. The psychedelic state is considered an exemplar of a primitive or primary state of consciousness that preceded the development of modern, adult, human, normal waking consciousness. Based on neuroimaging data with psilocybin, a classic psychedelic drug, it is argued that the defining feature of “primary states” is elevated entropy in certain aspects of brain function, such as the repertoire of functional connectivity motifs that form and fragment across time. Indeed, since there is a greater repertoire of connectivity motifs in the psychedelic state than in normal waking consciousness, this implies that primary states may exhibit “criticality,” i.e., the property of being poised at a “critical” point in a transition zone between order and disorder where certain phenomena such as power-law scaling appear. Moreover, if primary states are critical, then this suggests that entropy is suppressed in normal waking consciousness, meaning that the brain operates just below criticality. It is argued that this entropy suppression furnishes normal waking consciousness with a constrained quality and associated metacognitive functions, including reality-testing and self-awareness. It is also proposed that entry into primary states depends on a collapse of the normally highly organized activity within the default-mode network (DMN) and a decoupling between the DMN and the medial temporal lobes (which are normally significantly coupled). These hypotheses can be tested by examining brain activity and associated cognition in other candidate primary states such as rapid eye movement (REM) sleep and early ps
Hellyer PJ, Shanahan MP, Scott G, et al., 2014, The control of global brain dynamics: opposing actions of frontoparietal control and default mode networks on attention, Journal of Neuroscience, Vol: 34, Pages: 451-461, ISSN: 1529-2401
Understanding how dynamic changes in brain activity control behavior is a major challenge of cognitive neuroscience. Here, we consider the brain as a complex dynamic system and define two measures of brain dynamics: the synchrony of brain activity, measured by the spatial coherence of the BOLD signal across regions of the brain; and metastability, which we define as the extent to which synchrony varies over time. We investigate the relationship among brain network activity, metastability, and cognitive state in humans, testing the hypothesis that global metastability is “tuned” by network interactions. We study the following two conditions: (1) an attentionally demanding choice reaction time task (CRT); and (2) an unconstrained “rest” state. Functional MRI demonstrated increased synchrony, and decreased metastability was associated with increased activity within the frontoparietal control/dorsal attention network (FPCN/DAN) activity and decreased default mode network (DMN) activity during the CRT compared with rest. Using a computational model of neural dynamics that is constrained by white matter structure to test whether simulated changes in FPCN/DAN and DMN activity produce similar effects, we demonstate that activation of the FPCN/DAN increases global synchrony and decreases metastability. DMN activation had the opposite effects. These results suggest that the balance of activity in the FPCN/DAN and DMN might control global metastability, providing a mechanistic explanation of how attentional state is shifted between an unfocused/exploratory mode characterized by high metastability, and a focused/constrained mode characterized by low metastability.
Shanahan M, 2014, Review of "consciousness and robot sentience" by Pentti Haikonen, International Journal of Machine Consciousness, Vol: 6, Pages: 63-65, ISSN: 1793-8430
Fountas Z, Shanahan M, 2014, Phase Offset Between Slow Oscillatory Cortical Inputs Influences Competition in a Model of the Basal Ganglia, International Joint Conference on Neural Networks (IJCNN), Publisher: IEEE, Pages: 2407-2414, ISSN: 2161-4393
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- Citations: 4
Teixeira FPP, Shanahan M, 2014, Does Plasticity Promote Criticality?, International Joint Conference on Neural Networks (IJCNN), Publisher: IEEE, Pages: 2383-2390, ISSN: 2161-4393
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- Citations: 3
Shanahan M, Bingman VP, Shimizu T, et al., 2013, Large-scale network organization in the avian forebrain: a connectivity matrix and theoretical analysis, FRONTIERS IN COMPUTATIONAL NEUROSCIENCE, Vol: 7
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- Citations: 156
Fidjeland AK, Gamez D, Shanahan MP, et al., 2013, Three Tools for the Real-Time Simulation of Embodied Spiking Neural Networks Using GPUs, NEUROINFORMATICS, Vol: 11, Pages: 267-290, ISSN: 1539-2791
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- Citations: 4
Bhowmik D, Shanahan M, 2013, Metastability and Inter-Band Frequency Modulation in Networks of Oscillating Spiking Neuron Populations, PLOS ONE, Vol: 8, ISSN: 1932-6203
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- Citations: 19
Fountas Z, Shanahan M, 2013, A cognitive neural architecture as a robot controller, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol: 8064 LNAI, Pages: 371-373, ISSN: 0302-9743
This work proposes a biologically plausible cognitive architecture implemented in spiking neurons, which is based on well- established models of neuronal global workspace, action selection in the basal ganglia and corticothalamic circuits and can be used to control agents in virtual or physical environments. The aim of this system is the investigation of a number of aspects of cognition using real embodied systems, such as the ability of the brain to globally access and process information concurrently, as well as the ability to simulate potential future scenarios and use these predictions to drive action selection. © 2013 Springer-Verlag Berlin Heidelberg.
Bhowmik D, Shanahan M, 2013, STDP Produces Robust Oscillatory Architectures That Exhibit Precise Collective Synchronization, 2013 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), ISSN: 2161-4393
Wildie M, Shanahan M, 2012, Metastability and chimera states in modular delay and pulse-coupled oscillator networks, CHAOS, Vol: 22, ISSN: 1054-1500
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- Citations: 76
Shanahan M, 2012, The brain's connective core and its role in animal cognition, Philosophical Transactions of the Royal Society B: Biological Sciences, Vol: 367, Pages: 2704-2714
This paper addresses the question of how the brain of an animal achieves cognitive integration—that is to say how it manages to bring its fullest resources to bear on an ongoing situation. To fully exploit its cognitive resources, whether inherited or acquired through experience, it must be possible for unanticipated coalitions of brain processes to form. This facilitates the novel recombination of the elements of an existing behavioural repertoire, and thereby enables innovation. But in a system comprising massively many anatomically distributed assemblies of neurons, it is far from clear how such open-ended coalition formation is possible. The present paper draws on contemporary findings in brain connectivity and neurodynamics, as well as the literature of artificial intelligence, to outline a possible answer in terms of the brain’smost richly connected and topologically central structures, its so-called connective core.
Hellyer PJ, Shanahan MP, Scott G, et al., 2012, Global network dynamics during task based activity in the brain., Organisation for Human Brain Mapping
Hellyer PJ, Shanahan MP, Scott G, et al., 2012, Global network dynamics during task based activity in the brain., British Association of Cognitive Neuroscience
Bhowmik D, Shanahan M, 2012, How Well Do Oscillator Models Capture the Behaviour of Biological Neurons?, International Joint Conference on Neural Networks (IJCNN), Publisher: IEEE, ISSN: 2161-4393
Wildie M, Shanahan M, 2012, Establishing Communication Between Neuronal Populations Through Competitive Entrainment, Frontiers in Computational Neuroscience, Vol: 5
Wildie M, Shanahan M, 2012, Hierarchical Clustering Identifies Hub Nodes in a Model of Resting-State Brain Activity, IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)/International Joint Conference on Neural Networks (IJCNN)/IEEE Congress on Evolutionary Computation (IEEE-CEC)/IEEE World Congress on Computational Intelligence (IEEE-WCCI), Publisher: IEEE, ISSN: 1098-7576
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- Citations: 2
Shanahan M, Wildie M, 2012, Knotty-Centrality: Finding the Connective Core of a Complex Network, PLoS ONE, Vol: 7
Seed A, Clayton N, Carruthers P, et al., 2011, Planning, memory, and decision making, Pages: 121-147
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- Citations: 5
Scott G, Hellyer PJ, Shanahan M, et al., 2011, From structural networks to functional networks via coupled oscillators, Neuroscience 2011
Shanahan MP, 2011, Serial from Parallel, Unity from Multiplicity: What Emerges from Global Workspace Architecture, 2nd Annual Meeting of the Biologically-Inspired-Cognitive-Architectures-Society (BICA), Publisher: IOS PRESS, Pages: 343-343, ISSN: 0922-6389
Shanahan M, 2010, Embodiment and the Inner Life: Cognition and Consciousness in the Space of Possible Minds, Publisher: Oxford University Press, ISBN: 978-0-19-922655-9
Shanahan M, 2010, Perception as abduction: Turning, sensor data into meaningful representation, COGNITIVE SCIENCE, Vol: 29, Pages: 103-134, ISSN: 0364-0213
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- Citations: 68
Bouganis A, Shanahan M, 2010, Training a spiking neural network to control a 4-DoF robotic arm based on spike timing-dependent plasticity
In this paper, we present a spiking neural network architecture that autonomously learns to control a 4 degree-of-freedom robotic arm after an initial period of motor babbling. Its aim is to provide the joint commands that will move the end-effector in a desired spatial direction, given the joint configuration of the arm. The spiking neurons have been simulated according to Izhikevich's model, which exhibits biologically realistic behaviour and yet is computationally efficient. The architecture is a feed-forward network where the input layers encode the intended movement direction of the end-effector in spatial coordinates, as well as the information that is given by proprioception about the current joint angles of the arm. The motor commands are determined by decoding the firing patterns in the output layers. Both excitatory and inhibitory synapses connect the input and output layers, and their initial weights are set to random values. The network learns to map input stimuli to motor commands during a phase of repetitive action-perception cycles, in which Spike Timing-Dependent Plasticity (STDP) strengthens synapses between neurons that are correlated and weakens synapses between uncorrelated ones. The trained spiking neural network has been successfully tested on a kinematic model of the arm of an iCub humanoid robot. © 2010 IEEE.
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