Abstract
This article presents concurrent associative memories with synaptic delays useful for processing sequences of real vectors. Associative memories with synaptic delays were introduced by the authors for symbolic sequential inputs and demonstrated several advantages over other sequential memories. They were easy to organize and train. It was demonstrated that they were more robust than long short-term memories in recognition of damaged sequences. The associative memories can be applied in combination with deep neural networks to solve such symbol grounding problems, such as speech recognition, and support sequential memories triggered by sensory inputs. Several practical considerations for developed memories were discussed and illustrated. A continuous speech database was used to compare the developed method with LSTM memories. Tests demonstrated that the developed approach is more robust in recognition of speech sequences, particularly when the test sequences are damaged.
Highlights
M EMORIES are essential elements of cognitive systems, providing storing learned knowledge and episodes of its past interactions with the environment [1]
The results are compared with the results obtained with the long short-term memories (LSTMs) network, which is preceded by the deep network, and followed by one dense layer used for classification
This work described an extension of the symbolic form of synaptic delay associative knowledge graph (SDAKG), where the inputs were represented by sequences of symbols instead of vectors of real sensory data
Summary
M EMORIES are essential elements of cognitive systems, providing storing learned knowledge and episodes of its past interactions with the environment [1]. Because these neurons transmit their spikes to the polysynchronous group with various delays, if the difference between these delays corresponds to the differences between input activations, the polysynchronous neurons fire [10] This idea was developed in [11] to create an associative network of pulsing neurons that can store and recognize the sequences of activation of neurons that represent the input symbols. SDAKG is a new type of associative memory in which synaptic connections of the self-organizing neural network include information about time delays between input sequence elements. A new algorithm for time-series recognition tasks by the propagation of signals in the separate paths of synaptic gates that represent classes of sequences was created and described in this article This tool was connected with a convolutional neural network on the input to assess the class membership of frames.
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