Neural Networks and Reservoir Computing

Modeling the rhythmic complexity of professional drumming with an oscillation-driven reservoir computer

This paper uses an oscillation-driven reservoir computer to model professional drumming rhythms, showing it can replicate timing and amplitude patterns of human drumming.

Living Bacterial Reservoir Computers for Information Processing and Sensing

This paper demonstrates using E. coli as a living reservoir computer to classify COVID-19 plasma samples and perform nonlinear computations through growth responses.

Neuromorphic hierarchical modular reservoirs

This paper introduces hierarchical modular networks as reservoirs to study computational advantages of brain-like modularity, showing enhanced memory, multitasking, and temporal dynamics.

Morphologically Tunable Mycelium Chips for Physical Reservoir Computing

The paper introduces biodegradable mycelium-based chips for physical reservoir computing, demonstrating their ability to perform machine learning tasks with sustainability and cost advantages.

Theory of Temporal Pattern Learning in Echo State Networks

The paper develops a quantitative theory to explain learning in echo state networks with stable dynamics and weak feedback, showing that learning can be described by nonlinear interactions of master modes.

Linearizing and forecasting: a reservoir computing route to digital twins of the brain

This paper uses reservoir computing with noisy linear recurrent neural networks to create digital twins of brain activity from resting-state fMRI data, enabling prediction and decomposition of BOLD signals into low-dimensional latent states.

A Mean-Field Approach to Criticality in Spiking Neural Networks for Reservoir Computing

This paper develops a mean-field framework to configure spiking neural networks in the critical regime for reservoir computing, showing improved performance and computational capacity near criticality.

Decreased spinal inhibition leads to undiversified locomotor patterns

This study investigates how the balance of excitation and inhibition in neural networks affects the learning of human locomotor patterns using reservoir computing.

Selective consistency of recurrent neural networks induced by plasticity as a mechanism of unsupervised perceptual learning

This paper shows that a plastic neural network can acquire selective consistency to repeated stimuli through self-organization, even without performance improvement.

Brain Connectivity meets Reservoir Computing

This study explores how real brain connectivity patterns can inform artificial neural network architectures, finding that biologically inspired networks perform as well as classical ones with sufficient randomness and diversity in connections.

Effects of Connectivity on Narrative Temporal Processing in Structured Reservoir Computing

The paper uses structured reservoir computing to model how white matter connectivity influences narrative processing speed in different cortical areas.

Learning function from structure in neuromorphic networks

This paper uses human brain connectomes to train neuromorphic networks, showing that critical dynamics and modular organization enhance cognitive task performance.

Local homeostatic regulation of the spectral radius of echo-state networks

This paper introduces two local synaptic scaling rules for echo-state networks to control the spectral radius of recurrent synaptic weights, showing that flow control outperforms variance control in maintaining network performance under varying input conditions.

Chunking sequence information by mutually predicting recurrent neural networks

This paper introduces a class of unsupervised neural networks that identify repeated patterns in sequence data through mutual prediction between reservoir computing modules.