Vertical signal flow and oscillations in a three-layer model of the cortex

Fuentes, Ursula; Ritz, Raphael; Gerstner, Wulfram; Hemmen, J. Leo van

doi:10.1007/bf00160808

Cited by 6 publications

(5 citation statements)

References 36 publications

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“…The model of Lumer et al (1997a,b), in contrast, dealt in detail with the input pathways, and reproduced synchronous oscillation as a general property of jitter stimuli introduced to their neural networks. Similar considerations hold for the earlier cited works on feedforward modelling (Schillen and Konig 1994;Fuentes et al 1996;Xing and Gerstein 1996;Juergens and Eckhorn 1997). It would appear that these approachs and ours may be complementary.…”

Section: Discussionsupporting

confidence: 54%

“…This approach is intended to complement other simulations which approximate physiological realism using feedforward networks with inhibitory surrounds, or single and multiple orientation domains (Schillen and Konig 1994;Fuentes et al 1996;Xing and Gerstein 1996;Juergens and Eckhorn 1997). Our object was to ascertain the minimal assumptions needed to reproduce the experimental data.…”

Section: Introductionmentioning

confidence: 98%

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Synchronous oscillation in the cerebral cortex and object coherence: simulation of basic electrophysiological findings

Wright

Bourke

Chapman

2000

Biological Cybernetics

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A lumped continuum model for electrocortical activity was used to simulate several established experimental findings of synchronous oscillation which have not all been previously embodied in a single explanatory model. Moving-bar visual stimuli of different extension, stimuli moving in different directions, the impact of non-specific cortical activation upon synchronous oscillation, and the frequency content of EEG associated with synchrony were considered. The magnitude of zero lag synchrony was primarily accounted for by the properties of the eigenmodes of the travelling local field potential superposition waves generated by inputs to the cortex, largely independent of the oscillation properties and associated spectral content. Approximation of the differences in cross-correlation observed with differently moving bar stimuli, and of the impact of cortical activation, required added assumptions on (a) spatial coherence of afferent volleys arising from parts of a single stimulus object and (b) the presence of low-amplitude diffuse field noise, with enhancement of cortical signal/noise ratio with respect to the spatially coherent inputs, at higher levels of cortical activation. Synchrony appears to be a ubiquitous property of cortex-like delay networks. Precision in the modelling of synchronous oscillation findings will require detailed description of input pathways, cortical connectivity, cortical stability, and aspects of cortical/subcortical interactions.

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Section: Discussionsupporting

confidence: 54%

Section: Introductionmentioning

confidence: 98%

Synchronous oscillation in the cerebral cortex and object coherence: simulation of basic electrophysiological findings

Wright

Bourke

Chapman

2000

Biological Cybernetics

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“…We observed a threshold value of ascending neuromodulation beyond which structured neuronal activity emerged in the form of spontaneous thalamocortical oscillations in the gamma band (20–100 Hz, with a peak of the power spectrum around 40 Hz). This phenomenon was quite robust, since it was observed with two different mechanisms for spontaneous activity, either using intrinsic cellular oscillators or noisy spike generation (for other demonstrations of spontaneous oscillations in simpler network simulations, see [48,49]). This robustness appears to arise from the structure and delays inherent to thalamocortical loops, which function as a filter and selectively enhance gamma-band oscillations even when fed with unstructured noise.…”

Section: Discussionmentioning

confidence: 86%

Ongoing Spontaneous Activity Controls Access to Consciousness: A Neuronal Model for Inattentional Blindness

2005

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Even in the absence of sensory inputs, cortical and thalamic neurons can show structured patterns of ongoing spontaneous activity, whose origins and functional significance are not well understood. We use computer simulations to explore the conditions under which spontaneous activity emerges from a simplified model of multiple interconnected thalamocortical columns linked by long-range, top-down excitatory axons, and to examine its interactions with stimulus-induced activation. Simulations help characterize two main states of activity. First, spontaneous gamma-band oscillations emerge at a precise threshold controlled by ascending neuromodulator systems. Second, within a spontaneously active network, we observe the sudden “ignition” of one out of many possible coherent states of high-level activity amidst cortical neurons with long-distance projections. During such an ignited state, spontaneous activity can block external sensory processing. We relate those properties to experimental observations on the neural bases of endogenous states of consciousness, and particularly the blocking of access to consciousness that occurs in the psychophysical phenomenon of “inattentional blindness,” in which normal subjects intensely engaged in mental activity fail to notice salient but irrelevant sensory stimuli. Although highly simplified, the generic properties of a minimal network may help clarify some of the basic cerebral phenomena underlying the autonomy of consciousness.

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“…Synchrony per se could reflect, for example, nothing more than receptive field overlap. Likewise, oscillations can arise naturally from the intrinsic properties of neurons [90][91][92] or from the recurrent nature of cortical circuits 59,77,78,[93][94][95] . This is not to say that correlations are not important, but simply that their presence alone is not particularly informative.…”

Section: What Can Correlations Tell Us?mentioning

confidence: 99%

Correlated neuronal activity and the flow of neural information

Salinas¹,

Sejnowski²

2001

Nat Rev Neurosci

1,198

902

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For years we have known that cortical neurons collectively have synchronous or oscillatory patterns of activity, the frequencies and temporal dynamics of which are associated with distinct behavioural states. Although the function of these oscillations has remained obscure, recent experimental and theoretical results indicate that correlated fluctuations might be important for cortical processes, such as attention, that control the flow of information in the brain.Research in systems neuroscience has traditionally focused on how neurons represent the world and on the mechanisms that endow neurons with their response properties or receptive fields. However, an equally important, but less well understood, aspect of brain function is how neurons communicate. For instance, the presence of a red light in the visual field might be irrelevant if in a theatre, but crucial if about to cross a road. The neural representation of the red light might be, at some level, the same in the two situations, but this information is then redirected and prioritized in totally different ways. Little is known about how, depending on the current behavioural requirements, neural signals are routed or assessed in the nervous system. There is evidence that timing is crucial: a recent study 1 showed that whether intracortical microstimulation influences performance in a sensory discrimination task depends on the time at which the microinjected current is delivered relative to the natural stimulus onset. This indicates that even a simple discrimination paradigm is executed according to an internal schedule, such that the information provided by the sensory neurons is effectively transmitted only during a certain time window. So, the temporal dynamics of neuronal interactions seem to be important for the gating processes that control the information that goes through at a given time.On the other hand, networks of neurons show highly complex temporal dynamics. It is well known from electroencephalographic studies 2 that the small voltage signals recorded from the scalp fluctuate at various frequencies, with dominant frequency components shifting according to behaviour. Slow oscillations with a strong 0.75-4-Hz component are associated with certain stages of sleep, whereas oscillations dominated by the 14-40-Hz band are typical of active, awake states 3,4 . Direct measurement of field potentials from the cortex reveals even higherfrequency components in the 40-200-Hz range 5 . At the single-neuron level, collective oscillations in cortical neurons have been documented for several years [6][7][8] . One functional interpretation is that this rhythmic behaviour is, again, related to higher-order sensory representations, but this idea continues to be hotly debated 9,10 . The problem is not that oscillations are not there, but that linking them to behaviour has been difficult.

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Vertical signal flow and oscillations in a three-layer model of the cortex

Cited by 6 publications

References 36 publications

Synchronous oscillation in the cerebral cortex and object coherence: simulation of basic electrophysiological findings

Synchronous oscillation in the cerebral cortex and object coherence: simulation of basic electrophysiological findings

Ongoing Spontaneous Activity Controls Access to Consciousness: A Neuronal Model for Inattentional Blindness

Correlated neuronal activity and the flow of neural information

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