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

This is a Tensorflow implementation of the paper "CBGT-Net: A Neuromimetic Architecture for Robust Classification of Streaming Data" published at IEEE ICHMS 2024. (arxiv link)

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.

Approach

Citation

In case you find our work useful, consider citing:

Index

1. Environment Setup
2. Dataset Preparation
3. Training Models
4. Evaluating CBGT Model
5. License

Setup

In order to build a conda environment for running our model, run the following command:

conda env create -f environment.yml

Activate environment using:

conda activate py39

Dataset Preparation

• Download the cifar and mnist patch dataset for this Drive Link.
  Now add this folder to the cbgt_net folder before running any experiments on cbgt-net.

Training

To train CBGT-Net Model, simply run the following command:

python -m cbgt_net.experiment --env=cifar --patch_sz=20 --threshold=2

The --env argument specifies which environment to use - mnist / cifar. The --patch_sz argument specifies the size of patch for each observation image. The --threshold argument specifies the value of threshold for which you want to train this CBGT-Net Model.

To train the supervised baselines run these commands:

python supervised_learning_mnist.py
python supervised_learning_cifar.py

To train the LSTM baselines run these commands:

python lstm_cifar_baseline.py
python lstm_mnist_baseline.py

Evaluation

To evaluate one of the trained models of our CBGT-Net, simply run the following command:

python evaluate_cbgt-net.py --env=cifar --patch_sz=20 --threshold=2

License

Copyright (c) 2024 Shreya Sharma, Dana Hughes, Katia Sycara

For license information, see the license.