Introduction to the theory of neural computation
Introduction to the theory of neural computation
Image segmentation based on oscillatory correlation
Neural Computation
Weakly connected neural networks
Weakly connected neural networks
Phase equations for relaxation oscillators
SIAM Journal on Applied Mathematics
Guest Editorial Special Issue on Temporal Coding for Neural Information Processing
IEEE Transactions on Neural Networks
Locally excitatory globally inhibitory oscillator networks
IEEE Transactions on Neural Networks
Temporal coding: Assembly formation through constructive interference
Neural Computation
Assemblies as Phase-Locked Pattern Sets That Collectively Win the Competition for Coherence
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
Temporal coding: competition for coherence and new perspectives on assembly formation
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
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Temporal coding is considered with an oscillatory network model that generalizes the Cohen-Grossberg-Hopfield model. It is assumed that the frequency of oscillating units increases with stronger and more coherent input. We refer to this mechanism as acceleration. In the context of Hebbian memory, synchronization and acceleration take complementary roles, and their combined effect on the storage of patterns is profound. Acceleration implies the desynchronization that is needed for self-organized segmention of two overlapping patterns. The superposition problem is thereby solved even without including competition couplings. With respect to brain dynamics, we point to analogies with oscillation spindles in the gamma range and responses to perceptual rivalries.