metareservoirs

A metareservoir is a theoretical concept in reservoir computing, a subfield of artificial intelligence and computational neuroscience. It describes a system that is not only capable of computation but also possesses the ability to dynamically reconfigure its own internal structure and parameters. This reconfigurability allows the metareservoir to adapt its computational capabilities to different tasks or changing environmental conditions.

In traditional reservoir computing, a fixed, randomly connected recurrent neural network, known as the reservoir, serves as the computational substrate. The input data is fed into this reservoir, and its complex dynamics are harnessed to perform computation. However, the structure and parameters of this reservoir are typically set beforehand and remain static. A metareservoir, in contrast, introduces a meta-level control mechanism. This meta-level can adjust aspects of the underlying reservoir, such as its connectivity, neuron properties, or even its size.

The motivation behind the metareservoir concept is to enhance the adaptability and efficiency of reservoir computing systems. By allowing the reservoir to modify itself, it can potentially learn to perform a wider range of tasks or optimize its performance for a specific task more effectively than a static reservoir. This self-reconfiguration could be driven by external feedback, internal performance monitoring, or a learning algorithm operating at the meta-level. Research in this area explores how to design and control such adaptable computational systems.