Exploration of Reservoir Computing and Artificial Neural Network Architectures as Saliency Detectors

Abstract

This study explores the performance of reservoir-type and conventional artificial neural networks as saliency detectors, inspired by Li Zhaoping's hypothesis regarding the V1's role as a saliency detector. A comparative analysis evaluates various models on a simple input image time series task, focusing on their effectiveness in training and out-of-sample validation data.

Results indicate that while reservoir models exhibit higher training data loss compared to non-reservoir networks, they outperform other models on new data. Modifications to the reservoir structure show promise in improving both loss and Intersection over Union (IoU) score performance on the training and out-of-sample validation data. However, the study does not find that imposing a more visual cortex-like structure in the reservoir enhances its performance.

Further analysis suggests that while reservoir models offer advantages such as cost-effectiveness and robustness for changes in input data, they may lack the precision of more trainable models like CNNs. Limitations to the thesis include the scope of hyperparameter exploration and the lack of overfitting mitigation techniques like the use of dropout.

Future research should focus on refining all models to measure the potential performance on both training and out-of-sample data. This includes exploring ensemble methods and improving weight initialization techniques to enhance model precision and adaptability, leading to advancements in various real-world applications.