A Limit to the Speed of Processing in Ultra-Rapid Visual Categorization of Novel Natural Scenes
Journal of Cognitive Neuroscience
Learning Patterns and Pattern Sequences by Self-Organizing Nets of Threshold Elements
IEEE Transactions on Computers
Coding static natural images using spiking event times: do neurons Cooperate?
IEEE Transactions on Neural Networks
A Bidirectional Hetero-Associative Memory for True-Color Patterns
Neural Processing Letters
A New Associative Model with Dynamical Synapses
Neural Processing Letters
A modified sparse distributed memory model for extracting clean patterns from noisy inputs
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Memory capacities for synaptic and structural plasticity
Neural Computation
Neural associative memory with optimal bayesian learning
Neural Computation
Neural associative memories and sparse coding
Neural Networks
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Associative memory in cortical circuits has been held as a major mechanism for content-addressable memory. Hebbian synapses implement associative memory efficiently when storing sparse binary activity patterns. However, in models of sensory processing, representations are graded and not binary. Thus, it has been an unresolved question how sensory computation could exploit cortical associative memory. Here we propose a way how sensory processing could benefit from memory in cortical circuitry. We describe a new collaborative method of rank coding for converting graded stimuli, such as natural images, into sequences of synchronous spike volleys. Such sequences of sparse binary patterns can be efficiently processed in associative memory of the Willshaw type. We evaluate storage capacity and noise tolerance of the proposed system and demonstrate its use in cleanup and fill-in for noisy or occluded visual input.