Population coding of stimulus orientation by striate cortical cells
Biological Cybernetics
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Synchronisation, binding, and the role of correlated firing in fast information transmission
Emergent neural computational architectures based on neuroscience
Synchronisation, Binding, and the Role of Correlated Firing in Fast Information Transmission
Emergent Neural Computational Architectures Based on Neuroscience - Towards Neuroscience-Inspired Computing
Computational models for neuroscience
Decoding a Temporal Population Code
Neural Computation
Local and Global Gating of Synaptic Plasticity
Neural Computation
Parameter extraction from population codes: A critical assessment
Neural Computation
Temporal segmentation in a neural dynamic system
Neural Computation
Messages of oscillatory correlograms: A spike train model
Neural Computation
Synchrony State Generation in Artificial Neural Networks with Stochastic Synapses
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Model this! seven empirical phenomena missing in the models of cortical oscillatory dynamics
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
CuBIC: cumulant based inference of higher-order correlations in massively parallel spike trains
Journal of Computational Neuroscience
Oscillatory synchronization model of attention to moving objects
Neural Networks
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Recent work suggests that synchronization of neuronal activity could serve to define functionally relevant relationships between spatially distributed cortical neurons. At present, it is not known to what extent this hypothesis is compatible with the widely supported notion of coarse coding, which assumes that features of a stimulus are represented by the graded responses of a population of optimally and suboptimally activated cells. To resolve this issue we investigated the temporal relationship between responses of optimally and suboptimally stimulated neurons in area 17 of cat visual cortex. We find that optimally and suboptimally activated cells can synchronize their responses with a precision of a few milliseconds. However, there are consistent and systematic deviations of the phase relations from zero phase lag. Systematic variation of the orientation of visual stimuli shows that optimally driven neurons tend to lead over suboptimally activated cells. The observed phase lag depends linearly on the stimulus orientation and is, in addition, proportional to the difference between the preferred orientations of the recorded cells. Similar effects occur when testing the influence of the movement direction and the spatial frequency of visual stimuli. These results suggest that binding by synchrony can be used to define assemblies of neurons representing a coarse-coded stimulus. Furthermore, they allow a quantitative test of neuronal network models designed to reproduce physiological results on stimulus-specific synchronization.