Delayed Synapses: An LSM Model for Studying Aspects of Temporal Context in Memory
Spiking neural networks are promising candidates for representing aspects of cognitive context in human memory. We extended the liquid state machine model with time-delayed connections from liquid neurons to the readout unit to better capture context phenomena. We performed experiments in the area of spoken language recognition for studying two aspects of context dependency: influence of memory and temporal context. For the experiments, we derived a test data set from the well-known Brody-Hopfield test set to which we added varying degrees of Gaussian noise. We studied the influence of temporal context with a further specially designed test set. We found that the temporal context encoded in the pattern to be recognized was recognized better with our delayed synapses than without. Our experiments shed light on how context serves to integrate information and to increase robustness in human signal processing.
KeywordsSpiking neural networks (SNN) liquid state machine (LSM) context-dependency memory acquirements fading memory (FM)
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