Adapting the Biological SSVEP Response to Artificial Neural Networks

• URL: http://arxiv.org/abs/2411.10084v1
• Date: Fri, 15 Nov 2024 10:02:48 GMT
• Title: Adapting the Biological SSVEP Response to Artificial Neural Networks
• Authors: Emirhan Böge, Yasemin Gunindi, Erchan Aptoula, Nihan Alp, Huseyin Ozkan,
• Abstract summary: This paper introduces a novel approach to neuron significance assessment inspired by frequency tagging, a technique from neuroscience. Experiments conducted with a convolutional neural network for image classification reveal notable harmonics and intermodulations in neuron-specific responses under part-based frequency tagging. The proposed method holds promise for applications in network pruning, and model interpretability, contributing to the advancement of explainable artificial intelligence.
• Score: 5.4712259563296755
• License:
• Abstract: Neuron importance assessment is crucial for understanding the inner workings of artificial neural networks (ANNs) and improving their interpretability and efficiency. This paper introduces a novel approach to neuron significance assessment inspired by frequency tagging, a technique from neuroscience. By applying sinusoidal contrast modulation to image inputs and analyzing resulting neuron activations, this method enables fine-grained analysis of a network's decision-making processes. Experiments conducted with a convolutional neural network for image classification reveal notable harmonics and intermodulations in neuron-specific responses under part-based frequency tagging. These findings suggest that ANNs exhibit behavior akin to biological brains in tuning to flickering frequencies, thereby opening avenues for neuron/filter importance assessment through frequency tagging. The proposed method holds promise for applications in network pruning, and model interpretability, contributing to the advancement of explainable artificial intelligence and addressing the lack of transparency in neural networks. Future research directions include developing novel loss functions to encourage biologically plausible behavior in ANNs.

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