A "spiking" bidirectional associative memory for modeling intermodal priming

Starting from a modular artificial neural system modelling the integration of several perceptive stimuli, this article proposes a new implementation of the central module performing a multimodal associative memory. A Bidirectional Associative Memory (BAM) has been emulated in temporal coding with spiking neurons. Since input patterns are dynamically encoded, the effects of the latency of evocation can be simulated with the "spiking BAM", thus adding temporal properties to the model. For highlighting the contribution of the new module and the relevance for modelling cognitive processes, the "spiking BAM" has been tested in the context of an experimental protocol of cognitive psychology. KEY WORDS Neural Network Models, Cognitive Processes, Spiking Neurons, Intermodal Priming.

• Type: Conference
• Year: 2004
• Where: NCI
• Authors: David Meunier, Hélène Paugam-Moisy
• Keywords: Associative Memory, Cognitive Processes, NCI 2004, Neural Networks, Spiking Bam