CBGT-Net: A Neuromimetic Architecture for Robust Classification of Streaming Data

• URL: http://arxiv.org/abs/2403.15974v1
• Date: Sun, 24 Mar 2024 00:46:40 GMT
• Title: CBGT-Net: A Neuromimetic Architecture for Robust Classification of Streaming Data
• Authors: Shreya Sharma, Dana Hughes, Katia Sycara,
• Abstract summary: The CBGT-Net learns to produce an output after a sufficient criteria for evidence is achieved from a stream of observed data. We show that the CBGT-Net provides improved accuracy and robustness compared to models trained to classify from a single patch.
• Score: 0.994853090657971
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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