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Optimizing Reservoir Computers for Signal Classification.

Thomas L Carroll¹

¹US Naval Research Lab, Washington, DC, United States.

Frontiers in Physiology

|July 5, 2021

Summary

This summary is machine-generated.

Optimizing hyperparameters for reservoir computers, a type of recurrent neural network, can be accelerated by maximizing network entropy. This approach enhances signal classification performance and reduces computational demands.

Keywords:

machine learning neuromorphic neuron nonlinear dynamics reservoir computer

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Area of Science:

Computational neuroscience
Artificial intelligence
Machine learning

Background:

Reservoir computers are recurrent neural networks with fixed connections.
Training involves fitting output signals to a linear model, enabling fast training and analog hardware implementation for high speed and low power.
Hyperparameter optimization is crucial for reservoir computer performance, especially in signal classification.

Purpose of the Study:

To investigate a novel method for optimizing reservoir computer hyperparameters.
To demonstrate that maximizing network entropy leads to optimal signal classification performance.
To reduce the computational cost associated with hyperparameter optimization.

Main Methods:

The study evaluated both spiking and continuous-variable reservoir computers.
Hyperparameter optimization was performed by maximizing the entropy of the reservoir computer's state.
Performance was assessed using signal classification tasks.

Main Results:

Optimal signal classification performance was achieved when reservoir computer hyperparameters maximized network entropy.
Maximizing entropy requires only a single signal realization, significantly reducing computation.
This entropy-based optimization was validated on both spiking and continuous-variable reservoir computers.

Conclusions:

Maximizing reservoir computer entropy is an effective and computationally efficient strategy for hyperparameter optimization.
This method accelerates the optimization process for signal classification tasks.
The findings are applicable to various reservoir computer architectures, including spiking and continuous-variable types.