Post
2004
Artificial Kuramoto Oscillatory Neurons
Artificial Kuramoto Oscillatory Neurons (AKOrN) differ from traditional artificial neurons by oscillating, rather than just turning on or off. Each neuron is represented by a rotating vector on a sphere, influenced by its connections to other neurons. This behavior is based on the Kuramoto model, which describes how oscillators (like neurons) tend to synchronize, similar to pendulums swinging in unison.
Key points:
Oscillating Neurons: Each AKOrNโs rotation is influenced by its connections, and they try to synchronize or oppose each other.
Synchronization: When neurons synchronize, they "bind," allowing the network to represent complex concepts (e.g., "a blue square toy") by compressing information.
Updating Mechanism: Neurons update their rotations based on connected neurons, input stimuli, and their natural frequency, using a Kuramoto update formula.
Network Structure: AKOrNs can be used in various network layers, with iterative blocks combining Kuramoto layers and feature extraction modules.
Reasoning: This model can perform reasoning tasks, like solving Sudoku puzzles, by adjusting neuron interactions.
Advantages: AKOrNs offer robust feature binding, reasoning capabilities, resistance to adversarial data, and well-calibrated uncertainty estimation.
In summary, AKOrN's oscillatory neurons and synchronization mechanisms enable the network to learn, reason, and handle complex tasks like image classification and object discovery with enhanced robustness and flexibility.
yt
https://www.youtube.com/watch?v=i3fRf6fb9ZM
paper
https://arxiv.org/html/2410.13821v1
Artificial Kuramoto Oscillatory Neurons (AKOrN) differ from traditional artificial neurons by oscillating, rather than just turning on or off. Each neuron is represented by a rotating vector on a sphere, influenced by its connections to other neurons. This behavior is based on the Kuramoto model, which describes how oscillators (like neurons) tend to synchronize, similar to pendulums swinging in unison.
Key points:
Oscillating Neurons: Each AKOrNโs rotation is influenced by its connections, and they try to synchronize or oppose each other.
Synchronization: When neurons synchronize, they "bind," allowing the network to represent complex concepts (e.g., "a blue square toy") by compressing information.
Updating Mechanism: Neurons update their rotations based on connected neurons, input stimuli, and their natural frequency, using a Kuramoto update formula.
Network Structure: AKOrNs can be used in various network layers, with iterative blocks combining Kuramoto layers and feature extraction modules.
Reasoning: This model can perform reasoning tasks, like solving Sudoku puzzles, by adjusting neuron interactions.
Advantages: AKOrNs offer robust feature binding, reasoning capabilities, resistance to adversarial data, and well-calibrated uncertainty estimation.
In summary, AKOrN's oscillatory neurons and synchronization mechanisms enable the network to learn, reason, and handle complex tasks like image classification and object discovery with enhanced robustness and flexibility.
yt
https://www.youtube.com/watch?v=i3fRf6fb9ZM
paper
https://arxiv.org/html/2410.13821v1
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