CBGT-Net: A Neuromimetic Architecture for Robust Classification of Streaming Data
<|reference_start|>CBGT-Net: A Neuromimetic Architecture for Robust Classification of Streaming Data: This paper describes CBGT-Net, a neural network model inspired by the cortico-basal ganglia-thalamic (CBGT) circuits found in mammalian brains. Unlike traditional neural network models, which either generate an output for each provided input, or an output after a fixed sequence of inputs, the CBGT-Net learns to produce an output after a sufficient criteria for evidence is achieved from a stream of observed data. For each observation, the CBGT-Net generates a vector that explicitly represents the amount of evidence the observation provides for each potential decision, accumulates the evidence over time, and generates a decision when the accumulated evidence exceeds a pre-defined threshold. We evaluate the proposed model on two image classification tasks, where models need to predict image categories based on a stream of small patches extracted from the image. We show that the CBGT-Net provides improved accuracy and robustness compared to models trained to classify from a single patch, and models leveraging an LSTM layer to classify from a fixed sequence length of patches.<|reference_end|>
@article{sharma2024cbgt-net:,
    title={CBGT-Net: A Neuromimetic Architecture for Robust Classification of
  Streaming Data},
    author={Shreya Sharma, Dana Hughes, Katia Sycara},
    journal={arXiv preprint arXiv:2403.15974},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.15974},
    primaryClass={cs.NE cs.AI cs.CV cs.LG}
}