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T Geisel

Publications and source records attributed to T Geisel.

At least 37 records · Page 2Linked to original sources
PubMed1994

Formation of field discontinuities and islands in visual cortical maps.

The representations of visual hemifields in the extrastriate areas of various species exhibit field discontinuities and islands. We propose that these violations of retinotopy are a developmental consequence of the elongated shape of the respective cortical areas. To substantiate this claim, we investigated a model of activity-driven map formation. In agreement with observations, this model yields maps with field discontinuities if the cortical areas exceed a threshold elongation. Moreover, within the same model island representations in the periphery and the area centralis can also be understood. A multistability of the solutions in the model gives a very simple explanation for the observed interindividual variability of maps in cats. The model leads to a prediction of the radial dependence of the areal magnification factor near field discontinuities, which could be accessible for a high precision mapping experiment.

Animals↗
PubMed1994

Uncovering the synchronization dynamics from correlated neuronal activity quantifies assembly formation.

Synchronous network excitation is believed to play an outstanding role in neuronal information processing. Due to the stochastic nature of the contributing neurons, however, those synchronized states are difficult to detect in electrode recordings. We present a framework and a model for the identification of such network states and of their dynamics in a specific experimental situation. Our approach operationalizes the notion of neuronal groups forming assemblies via synchronization based on experimentally obtained spike trains. The dynamics of such groups is reflected in the sequence of synchronized states, which we describe as a renewal dynamics. We furthermore introduce a rate function which is dependent on the internal network phase that quantifies the activity of neurons contributing to the observed spike train. This constitutes a hidden state model which is formally equivalent to a hidden Markov model, and all its parameters can be accurately determined from the experimental time series using the Baum-Welch algorithm. We apply our method to recordings from the cat visual cortex which exhibit oscillations and synchronizations. The parameters obtained for the hidden state model uncover characteristic properties of the system including synchronization, oscillation, switching, background activity and correlations. In applications involving multielectrode recordings, the extracted models quantify the extent of assembly formation and can be used for a temporally precise localization of system states underlying a specific spike train.

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