Deep Neural Networks Exhibit Self-Organized Criticality and 1/f Noise Patterns Akin to Biological Neural Networks

Temel Kavramlar

Deep neural networks, like their biological counterparts, exhibit 1/f noise patterns in their neuron activations, suggesting the presence of self-organized criticality and optimal information processing.

Özet

The study investigates the presence of 1/f noise, also known as pink noise, in deep neural networks, particularly Long Short-Term Memory (LSTM) networks trained on a natural language processing task. The key findings are:

1. LSTM networks trained on the IMDb movie review dataset exhibit clear 1/f noise patterns in the time series of their neuron activations, similar to the 1/f noise observed in biological neural networks like the human brain.

2. This 1/f noise is not present in the input data itself, indicating that the networks are self-organizing to generate these patterns.

3. As the capacity of the LSTM networks increases beyond what is needed for the task, the 1/f noise pattern starts to break down, with many neurons becoming inactive and the overall activation patterns shifting towards white noise.

4. The study also finds a distinction between the 1/f noise patterns in the "internal" activations (used for regulating the network) versus the "external" activations (the final output), mirroring the differences observed between surface EEG and deep fMRI measurements in the human brain.

These findings suggest that the emergence of 1/f noise in deep neural networks is a signature of optimal, self-organized information processing, akin to what is observed in biological neural networks. The authors propose that the transparency and controllability of artificial neural networks make them valuable tools for further investigating the fundamental origins of 1/f noise and its relationship to healthy neural function.

İstatistikler

The LSTM networks achieved test set accuracies ranging from 87.61% to 89.63% on the sentiment analysis task.

Alıntılar

"When the neural network is at overcapacity, having more than enough neurons to achieve the learning task, the activation patterns deviate from 1/f noise and shifts towards white noise."
"The aggregate results obtained in [17] gives a scaling exponent β = 1.33 ± 0.19, demonstrating 1/f noise."
"In fMRI studies [19, 20], the scaling exponents measured in these studies are smaller than in EEGs, with an average scaling exponent of β = 0.84. This exponent became even smaller when the brain performs tasks, averaging to β = 0.72 across the brain."

Önemli Bilgiler Şuradan Elde Edildi

Self-Organization Towards $1/f$ Noise in Deep Neural Networks

by Nicholas Cho... : arxiv.org 04-02-2024

https://arxiv.org/pdf/2301.08530.pdf

Daha Derin Sorular

How do the 1/f noise patterns in deep neural networks change as the networks are trained on more diverse or complex tasks

The 1/f noise patterns in deep neural networks tend to change as the networks are trained on more diverse or complex tasks. When neural networks are exposed to tasks that require higher levels of abstraction, intricate pattern recognition, or multi-modal data processing, the 1/f noise patterns may exhibit variations. These variations can manifest as shifts in the scaling exponents of the power spectral densities, indicating changes in the network's information processing dynamics. As the complexity of the tasks increases, the neural network may adapt by reorganizing its internal activations to accommodate the new information processing requirements. This adaptation can lead to alterations in the 1/f noise patterns, reflecting the network's ability to self-organize towards optimal learning states for the specific tasks at hand.

What are the potential implications of the distinction between "internal" and "external" 1/f noise patterns in deep neural networks for understanding information processing in the brain

The distinction between "internal" and "external" 1/f noise patterns in deep neural networks can have significant implications for understanding information processing in the brain. The "internal" activations, which regulate the layer output and maintain temporal correlations, exhibit distinct 1/f noise patterns compared to the "external" activations that contain the values of the layer output. This distinction mirrors the differences observed in brain signals measured through EEG and fMRI, where surface and volume measurements show varying exponents in their activations. By studying these internal and external patterns in neural networks, researchers can gain insights into how information is processed and represented within the network layers. Understanding these distinct patterns can provide valuable clues about the hierarchical organization and functional dynamics of neural networks, shedding light on how the brain may encode and process information across different scales and modalities.

Could the insights from 1/f noise in deep neural networks be leveraged to develop novel neural network architectures or training techniques that better mimic the dynamics of biological neural networks

Insights from 1/f noise in deep neural networks offer opportunities to develop novel neural network architectures or training techniques that better mimic the dynamics of biological neural networks. By leveraging the self-organization properties associated with 1/f noise, researchers can design neural network models that exhibit adaptive learning capabilities, robustness to noise, and efficient information processing similar to biological systems. For instance, incorporating mechanisms for self-organized criticality or critical synchronization states in neural network architectures could enhance their learning efficiency and generalization performance. Additionally, by considering the interplay between "internal" and "external" activations in neural networks, novel training techniques could be devised to optimize the network's information flow, enhance its representational capacity, and improve its ability to capture complex patterns in data. Overall, leveraging insights from 1/f noise in deep neural networks holds promise for advancing the development of more biologically inspired and efficient artificial intelligence systems.