Thalamo-cortical interactions modeled by weakly connected oscillators: could the brain use FM radio principles?

Hoppensteadt, Frank C.; Izhikevich, Eugene M.

doi:10.1016/s0303-2647(98)00053-7

Cited by 125 publications

(96 citation statements)

References 16 publications

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“…A detailed interpretation of the cognitive significance of this observation is necessarily speculative in the absence of certainty concerning the nature of the neural code. However, it has been argued that frequency may code the channel of communication in neural circuits, and phase modulations may transmit information in specific channels (46). On this assumption, the existence of a scale-invariant small-world topology for brain functional networks might serve as a mechanism for integrating information transmitted in frequency-specific circuits.…”

Section: Temporal Binding Theories Of Cognitionmentioning

confidence: 99%

Adaptive reconfiguration of fractal small-world human brain functional networks

Bassett

Meyer‐Lindenberg

Achard

et al. 2006

Proc. Natl. Acad. Sci. U.S.A.

750

554

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Brain function depends on adaptive self-organization of largescale neural assemblies, but little is known about quantitative network parameters governing these processes in humans. Here, we describe the topology and synchronizability of frequencyspecific brain functional networks using wavelet decomposition of magnetoencephalographic time series, followed by construction and analysis of undirected graphs. Magnetoencephalographic data were acquired from 22 subjects, half of whom performed a fingertapping task, whereas the other half were studied at rest. We found that brain functional networks were characterized by smallworld properties at all six wavelet scales considered, corresponding approximately to classical ␦ (low and high), , ␣, ␤, and ␥ frequency bands. Global topological parameters (path length, clustering) were conserved across scales, most consistently in the frequency range 2-37 Hz, implying a scale-invariant or fractal small-world organization. Dynamical analysis showed that networks were located close to the threshold of order/disorder transition in all frequency bands. The highest-frequency ␥ network had greater synchronizability, greater clustering of connections, and shorter path length than networks in the scaling regime of (lower) frequencies. Behavioral state did not strongly influence global topology or synchronizability; however, motor task performance was associated with emergence of long-range connections in both ␤ and ␥ networks. Long-range connectivity, e.g., between frontal and parietal cortex, at high frequencies during a motor task may facilitate sensorimotor binding. Human brain functional networks demonstrate a fractal small-world architecture that supports critical dynamics and task-related spatial reconfiguration while preserving global topological parameters. magnetoencephalography ͉ wavelet ͉ graph theory ͉ connectivity ͉ binding C oherent or correlated oscillation of large-scale, distributed neural networks is widely regarded as an important physiological substrate for motor, perceptual and cognitive representations in the brain (1, 2). The topological description of brain networks promises quantitative insight into functionally relevant parameters because their topology strongly influences their dynamic properties such as speed and specialization of information processing, learning, and robustness against pathological attack by disease (3).The topology of networks can range from entirely random to fully ordered (a lattice). In this spectrum, small-world topology is characteristic of complex networks that demonstrate both clustered or cliquish interconnectivity within groups of nodes sharing many nearest neighbors in common (like regular lattices), and a short path length between any two nodes in the network (like random graphs) (3). This is an attractive configuration, in principle, for the anatomical and functional architecture of the brain, because small-world networks are known to optimize information transfer, increase the rate of learning, and support both segregated and...

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Section: Temporal Binding Theories Of Cognitionmentioning

confidence: 99%

Adaptive reconfiguration of fractal small-world human brain functional networks

Bassett

Meyer‐Lindenberg

Achard

et al. 2006

Proc. Natl. Acad. Sci. U.S.A.

750

554

View full text Add to dashboard Cite

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“…''Binding by resonance'' constitutes a general mechanism by which cell assemblies selectively interact in multiple different matching frequencies. In Hoppensteadt and Izhikevich (1998) and Izhikevich (1999) numerical evidence has been provided that weakly coupled oscillators may strongly interact when their characteristic frequencies obey specific resonance relations. This framework is sufficiently general that it might be realized by the communication between local neural networks operating in different frequency ranges (Nunez andSrinivasan, 2006a, 2006b).…”

Section: Local Information Processing Versus Global Dynamicsmentioning

confidence: 99%

Standing Waves as an Explanation for Generic Stationary Correlation Patterns in Noninvasive EEG of Focal Onset Seizures

et al. 2014

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Cerebral electrical activity is highly nonstationary because the brain reacts to ever changing external stimuli and continuously monitors internal control circuits. However, a large amount of energy is spent to maintain remarkably stationary activity patterns and functional inter-relations between different brain regions. Here we examine linear EEG correlations in the peri-ictal transition of focal onset seizures, which are typically understood to be manifestations of dramatically changing inter-relations. Contrary to expectations we find stable correlation patterns with a high similarity across different patients and different frequency bands. This skeleton of spatial correlations may be interpreted as a signature of standing waves of electrical brain activity constituting a dynamical ground state. Such a state could promote the formation of spatiotemporal neuronal assemblies and may be important for the integration of information stemming from different local circuits of the functional brain network.

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“…Hoppensteadt and Izhikevich [19] proposed a h ypothesis that the cortex might employ a principle similar to that in FM radio: Each cortical column is an autonomous oscillator. The frequency of oscillation encodes the channel of communication, while the information is transmitted via phase modulations.…”

Section: Discussionmentioning

confidence: 99%

Synchronized oscillation in a modular neural network composed of columns

Qi²,

et al. 2005

Sci China Ser C

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The columnar organization is a ubiquitous feature in the cerebral cortex. In this study, a neural network model simulating the cortical columns has been constructed. When fed with random pulse input with constant rate, a column generates synchronized oscillations, with a frequency varying from 3 to 43 Hz depending on parameter values. The behavior of the model under periodic stimulation was studied and the input-output relationship was non-linear. When identical columns were sparsely interconnected, the column oscillator could be locked in synchrony. In a network composed of heterogeneous columns, the columns were organized by intrinsic properties and formed partially synchronized assemblies.Keywords: cortical column, synchronized oscillation, Rose-Hindmarsh model. DOI: 10.1360/03yc0240Since it was first reported in 1957 [1] , the columnar organization has been found in somatic sensory cortex, visual cortex, auditory cortex, motor cortex and association cortex of various different species including mice, rat, cats, rabbits, monkeys and humans [2] . These experimental findings strongly supported that the cortical columns are basic anatomical and physiological units of the cortex and suggested that columns might be of fundamental importance for the function of the brain [2,3] .Mathematical models were constructed and simulated to investigate the biological function of the column in the information processing in the brain. Wilson-Cowan model [4] is most frequently used to simulate the electrophysiological activities of the cortical columns in the previous studies. For example, Schuster et al. [5] proposed a columnar model to simulate the synchronized oscillation discovered in visual cortex. Jansen [6,7] presented a mathematical model of coupled cortical columns which produced EEG-like waveform and evoked potentials. Fukai [8] designed a network model in columnar structure to simulate visual pattern retrieval. Some of the column models are phase models which assume a column as an oscillator and simulate mere the phase of oscillating activities [9,10] .There has been few modeling of columns based on spiking single neurons. By replacing the single cell as the functional unit by multiple cells in cortical columns, an attractor network model was constructed and simulated to perform associative memory [11] . Hansel and Sompolinsky [12] constructed a hypercolumn model according to the physiological organization in the orientation columns in visual cortex. The synchronous and chaotic activities in the hypercolumn were studied and the model was used to explore the cortical mechanisms for orientation selectivity.

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Thalamo-cortical interactions modeled by weakly connected oscillators: could the brain use FM radio principles?

Cited by 125 publications

References 16 publications

Adaptive reconfiguration of fractal small-world human brain functional networks

Adaptive reconfiguration of fractal small-world human brain functional networks

Standing Waves as an Explanation for Generic Stationary Correlation Patterns in Noninvasive EEG of Focal Onset Seizures

Synchronized oscillation in a modular neural network composed of columns

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