| Summary: | This paper presents a new neural network for online topological map building inspired by beta oscillations and hippocampal place cell learning. The memory layer represents the hippocampus, the input layer represents the entorhinal, and the ρ is the orientation system. In this model, multiple-scale entorhinal grid cell activations form the input layer feature patterns, which are categorized by hippocampal place cells (nodes) and act as spatial categories in the memory layer. Top-down attentive matching and mismatch-mediated reset (beta oscillations), which are triggered by the orientation system, overcome the stability-plasticity dilemma and prevent the catastrophic forgetting of place cell maps. In our proposed method, nodes in the topological map represent place cells (robot location), while edges connect nodes and store robot action (i.e., orientation, direction). Our method is based upon a multi-channel Adaptive Resonance Associative Memory (ARAM) network architecture to obtain multiple sensory sources for topological map building. It comprises two layers: input and memory. The input layer collects sensory data and incrementally clusters the obtained information into a set of topological nodes. In the memory layer, the clustered information is used as a topological map where nodes are associated with actions. The advantages of the proposed method are: (1) it does not require high-level cognitive processes and prior knowledge to make it work in a natural environment; and (2) it can process multiple sensory sources simultaneously in continuous space, which is crucial for real-world robot navigation. Thus, we combine our Topological Gaussian ARAM method (TGARAM) with incremental principle component analysis to constitute a basis for topological map building. Lastly, the proposed method was validated using several standardized benchmark datasets.
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