Temporal Structure of Cortical Information Processing: Cortical Architecture, Oscillations, and Non-Separability of Spatio-Temporal Receptive Field Organization

Dinse, Hubert R.; Krüger, Katharina; Mallot, Hanspeter A.; Best, Johannes

doi:10.1007/978-3-642-84301-3_5

Cited by 25 publications

(14 citation statements)

References 42 publications

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“…Studies by Dinse et al (1991Dinse et al ( , 1997 and Dudkin et al (1978) confirmed the existence of the ''alpha response'' at the cellular level. Later, cross-modality experiments demonstrated that sensory alpha responses are dependent on the modality of sensory stimulation and recording site.…”

Section: Pre-stimulus Eeg Alpha Blocking (Erd) and Alpha Responsementioning

confidence: 83%

Event related desynchronization: use as a neurophysiologic marker is restricted

Başar

Gölbaşı

2014

Cogn Neurodyn

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The present research aims to show that the occurrence of alpha blocking or event-related desynchronization (ERD) strongly depends on the amplitude and also on the phase angle of alpha activity at the stimulus onset. Simple visual stimulation was presented to 17 healthy subjects during EEG recording. An O 2 electrode was used for analysis with a 32 channel EEG sampling system. We used a segmentation of raw data in order to obtain the evoked potential. Prestimulus and poststimulus activities were filtered in the alpha (8-13 Hz) frequency band. Later, four different events (blocked, time-locked, phase-locked, and eliminated) were separately averaged. Phase-locked sweeps were determined by application of inter-trial coherence analysis. The evaluation of the data shows that ''time-locked and phase-locked sweeps'' were the dominating pattern and not ''the blocked pattern'', which occurred only when the prestimulus alpha was high. In the analyses of EEG-EP sweeps, only 22 % of epochs showed (ERD). The ANOVA revealed significant differences between four different alpha responses (F(3,48) = 11.175; p \ 0.001). Furthermore, alpha oscillations in time-locked responses were significantly higher than blocked (p \ 0.0001). The analyses clearly demonstrate that important precaution is needed when using the ERD as a cognitive or pathological marker.

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Section: Pre-stimulus Eeg Alpha Blocking (Erd) and Alpha Responsementioning

confidence: 83%

Event related desynchronization: use as a neurophysiologic marker is restricted

Başar

Gölbaşı

2014

Cogn Neurodyn

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“…Eckhorn et al (1993) have developed an elaborate technique, the RF-cinematogram, to study this characteristic explicitly. An important question regarding the temporal aspects of receptive fields refers to the possible role of feedback in the visual cortex (Dinse et al 1991;Celebrini et al 1993). While feedback seems to be omnipresent in the cortex, we demonstrate that interesting temporal aspects of the receptive field can also emerge in a purely feedforward structure.…”

Section: Introductionmentioning

confidence: 67%

“…Furthermore, our analysis shows that local variations in the spatial and temporal arborisation of the synapses are necessary prerequesites for the emergence of a structure with a temporal and a spatial differentiator next to each other. These variations may be reflected neuroanatom-3 A different point of view regarding this problem has been advocated by Dinse et al (1991).…”

Section: Conclusion and Biological Discussionmentioning

confidence: 99%

“…During recent years, however, there has been growing experimental evidence that the temporal characteristic of the receptive field is of great importance for visual processing as well (Dinse et al 1991;Eckhorn et al 1993). A spatiotemporal receptive field is characterized by the fact that it changes its spatial response in dependence upon the time that elapses between the arrival of the input to the receptive field and its output.…”

Section: Introductionmentioning

confidence: 99%

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Emergence of spatiotemporal receptive fields and its application to motion detection

1994

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Abstract.A model of motion sensitivity as observed in some cells of area V1 of the visual cortex is proposed. Motion sensitivity is achieved by a combination of different spatiotemporal receptive fields, in particular, spatial and temporal differentiators. The receptive fields emerge if a Hebbian learning rule is applied to the network. Similar to a Linsker model the network has a spatially convergent, linear feedforward structure. Additionally, however, delays omnipresent in the brain are incorporated in the model. The emerging spatiotemporal receptive fields are derived explicitly by extending the approach of MacKay and Miller. The response characteristic of the network is calculated in frequency space and shows that the network can be considered as a spacetime filter for motion in one direction. The emergence of different types of receptive field requires certain structural constraints regarding the spatial and temporal arborisation. These requirements can be derived from the theoretical analysis and might be compared with neuroanatomical data. In this way an explicit link between structure and function of the network is established.

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“…This result is reminiscent of the recursive filters used in image processing: complex nonseparable filters requiring three-dimensional convolutions can be realized recursively by simple separable filters requiring just a two-dimensional (spatial) plus a one-dimensional (temporal) convolution at each iteration. A comparison of the spatiotemporal properties of the layered network with neurophysiological findings has been given by Dinse et al (1991).…”

Section: Layered Cortical Areamentioning

confidence: 99%

Population networks: a large-scale framework for modelling cortical neural networks

Mallot

Giannakopoulos

1996

Biological Cybernetics

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Artificial neural networks are usually built on rather few elements such as activation functions, learning rules, and the network topology. When modelling the more complex properties of realistic networks, however, a number of higher-level structural principles become important. In this paper we present a theoretical framework for modelling cortical networks at a high level of abstraction. Based on the notion of a population of neurons, this framework can accommodate the common features of cortical architecture, such as lamination, multiple areas and topographic maps, input segregation, and local variations of the frequency of different cell types (e.g., cytochrome oxidase blobs). The framework is meant primarily for the simulation of activation dynamics; it can also be used to model the neural environment of single cells in a multiscale approach.

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Temporal Structure of Cortical Information Processing: Cortical Architecture, Oscillations, and Non-Separability of Spatio-Temporal Receptive Field Organization

Cited by 25 publications

References 42 publications

Event related desynchronization: use as a neurophysiologic marker is restricted

Event related desynchronization: use as a neurophysiologic marker is restricted

Emergence of spatiotemporal receptive fields and its application to motion detection

Population networks: a large-scale framework for modelling cortical neural networks

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