reservoir computing
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Heterogeneity and non-random connectivity in reservoir computing
Reservoir computing is a promising framework to study cortical computation, as it is based on continuous, online processing and the requirements and operating principles are compatible with cortical circuit dynamics. However, the framework has issues that limit its scope as a generic model for cortical processing. The most obvious of these is that, in traditional models, learning is restricted to the output projections and takes place in a fully supervised manner. If such an output layer is interpreted at face value as downstream computation, this is biologically questionable. If it is interpreted merely as a demonstration that the network can accurately represent the information, this immediately raises the question of what would be biologically plausible mechanisms for transmitting the information represented by a reservoir and incorporating it in downstream computations. Another major issue is that we have as yet only modest insight into how the structural and dynamical features of a network influence its computational capacity, which is necessary not only for gaining an understanding of those features in biological brains, but also for exploiting reservoir computing as a neuromorphic application. In this talk, I will first demonstrate a method for quantifying the representational capacity of reservoirs without training them on tasks. Based on this technique, which allows systematic comparison of systems, I then present our recent work towards understanding the roles of heterogeneity and connectivity patterns in enhancing both the computational properties of a network and its ability to reliably transmit to downstream networks. Finally, I will give a brief taster of our current efforts to apply the reservoir computing framework to magnetic systems as an approach to neuromorphic computing.
MAPPING THE HIDDEN GEOMETRY OF RESTING‑STATE BRAIN DYNAMICS WITH RESERVOIR COMPUTING
FENS Forum 2026
COMPARATIVE ANALYSIS OF ARTIFICIAL, SPIKING, AND BIOLOGICAL NEURAL NETWORKS FOR RESERVOIR COMPUTING
FENS Forum 2026
BIOLOGICAL RESERVOIR COMPUTING USING MODULAR HUMAN IPSC-DERIVED NEURONAL NETWORKS
FENS Forum 2026
Sparse neural engagement in connectome-based reservoir computing networks
COSYNE 2025
One-shot learning of paired associations by a reservoir computing model with Hebbian plasticity
COSYNE 2022
Reservoir computing using cultured neuronal networks with modular topology
FENS Forum 2024
One-shot learning of paired associations by a reservoir computing model with Hebbian plasticity
COSYNE 2022
Slow transition to chaos and robust reservoir computing in recurrent neural networks with heavy-tailed distributed synaptic weights
COSYNE 2025
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