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.

这篇论文描述了 CBGT-Net，这是一个受到哺乳动物大脑中的皮层 - 基底节 - 丘脑（CBGT）回路启发的神经网络模型。与传统的神经网络模型不同，该模型在观察到的数据流中学习在达到足够证据标准后产生输出。我们通过两个图像分类任务对该模型进行评估，其中模型需要根据从图像中提取的一系列小块来预测图像类别。我们证明 CBGT-Net 相比于单个小块进行分类的模型和利用 LSTM 层进行分类的模型，具有更高的准确性和鲁棒性。