Towards binary encoding in Bidirectional Associative Memories
Keywords:Artificial Neural Networks, Associative Memory, Cognition, Binary, Learning
Bidirectional Associative Memories (BAMs) are Artificial Neural Networks frequently utilized in cognitive modeling. While bipolar encoding is commonly used in BAMs for optimal performance, binary encoding presents interesting properties. As such, this study introduces a novel transmission function for binary encoding and compares its performance to the conventional bipolar transmission function. To evaluate, an auto-association learning task and a noisy recall task were implemented. Results revealed that despite longer learning times, binary encoding preserves or enhances the properties observed in binary encoding. Findings are promising from a cognitive perspective, as they open the possibility of building intricate models of human cognition.
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Copyright (c) 2023 Thaddé Rolon-Merette, Damiem Rolon-Merette, Sylvain Chartier
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