Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Neural Networks and Natural Intelligence
Neural Networks and Natural Intelligence
Self-Organizing Maps
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While fixed point dynamics is still the predominant regime used for information processing, recent brain observations and computational results suggest more and more the importance/necessity to include and rely on more complex dynamics. Independently, since their introduction sixty years ago, cell assemblies are still a powerful substrate for brain information processing. Here, the first part of this paper aims to conciliate these two evidences by investigating the possibility to encode content addressable information in pre-encoded cell assemblies characterized by complex dynamics. As an expected outcome, after stimulus offset, the information is maintained in the attractor of the cell assembly. As a less expected outcome, when the system is fed with ambiguous stimuli, it will continuously iterate across the possible attractors (instead to settle down to a specific one). In the second part of the paper, based on biologically plausible mechanisms, a novel unsupervised algorithm for online cell assemblies creation is proposed. The procedure involves simultaneously, a fast hebbian/anti-hebbian learning of the network's recurrent connections for the creation of new cell assemblies, and a slower feedback signal which stabilizes the cell assemblies by learning the feedforward input connections. Results show that the obtained cell assemblies exhibit similar behavior as the pre-encoded ones. Finally, we propose that this model could be working for the cognitive map formation of multiple place fields in the CA3 network when the rat is facing a new environment.