Characterization of the neuronal and network dynamics of liquid state machines

Neural Information Processing and Computations of Two-Input Synapses

(2022) Soon Ho Kim et al.   NEURAL COMPUTATION

Sampling effects and measurement overlap can bias the inference of neuronal avalanches

(2022) Joao Pinheiro Neto et al.   PLoS Computational Biology

Self-Organized Criticality in the Brain

(2021) Dietmar Plenz et al.   Frontiers in Physics

Characterization of dynamics and information processing of integrate-and-fire neuron models

(2021) JunHyuk Woo et al.   Journal of Physics A-Mathematical and Theoretical

Input-to-State Representation in Linear Reservoirs Dynamics

(2021) Pietro Verzelli et al.   IEEE Transactions on Neural Networks and Learning Systems

Edge of Chaos and Avalanches in Neural Networks with Heavy-Tailed Synaptic Weight Distribution

(2020) Łukasz Kuśmierz et al.   PHYSICAL REVIEW LETTERS

Do reservoir computers work best at the edge of chaos?

(2020) T. L. Carroll   CHAOS

Memory and forecasting capacities of nonlinear recurrent networks

(2020) Lukas Gonon et al.   PHYSICA D-NONLINEAR PHENOMENA

Analysis of Liquid Ensembles for Enhancing the Performance and Accuracy of Liquid State Machines

(2019) Parami Wijesinghe et al.   Frontiers in Neuroscience

Anytime multipurpose emotion recognition from EEG data using a Liquid State Machine based framework

(2018) Obada Al Zoubi et al.   ARTIFICIAL INTELLIGENCE IN MEDICINE

Unsupervised heart-rate estimation in wearables with Liquid states and a probabilistic readout

(2018) Anup Das et al.   NEURAL NETWORKS

Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach

(2018) Jaideep Pathak et al.   PHYSICAL REVIEW LETTERS

Deep learning in spiking neural networks

(2018) Amirhossein Tavanaei et al.   NEURAL NETWORKS

High-Density Liquid-State Machine Circuitry for Time-Series Forecasting

(2016) Josep L. Rosselló et al.   International Journal of Neural Systems

Memory and Information Processing in Neuromorphic Systems

(2015) Giacomo Indiveri et al.   PROCEEDINGS OF THE IEEE

Unsupervised learning of digit recognition using spike-timing-dependent plasticity

(2015) Peter U. Diehl et al.   Frontiers in Computational Neuroscience

A Digital Liquid State Machine With Biologically Inspired Learning and Its Application to Speech Recognition

(2015) Yong Zhang et al.   IEEE Transactions on Neural Networks and Learning Systems

Electrocardiogram Classification Using Reservoir Computing With Logistic Regression

(2015) Miguel Angel Escalona-Moran et al.   IEEE Journal of Biomedical and Health Informatics

Neuronal Avalanches Differ from Wakefulness to Deep Sleep – Evidence from Intracranial Depth Recordings in Humans

(2013) Viola Priesemann et al.   PLoS Computational Biology

Information Processing Capacity of Dynamical Systems

(2012) Joni Dambre et al.   Scientific Reports

Information processing in echo state networks at the edge of chaos

(2011) Joschka Boedecker et al.   THEORY IN BIOSCIENCES

Emergent complex neural dynamics

(2010) Dante R. Chialvo   Nature Physics

Self-Organized Criticality in Developing Neuronal Networks

(2010) Christian Tetzlaff et al.   PLoS Computational Biology

Broadband Criticality of Human Brain Network Synchronization

(2009) Manfred G. Kitzbichler et al.   PLoS Computational Biology

Minimal Hodgkin–Huxley type models for different classes of cortical and thalamic neurons

(2008) Martin Pospischil et al.   BIOLOGICAL CYBERNETICS

Short-term stock price prediction based on echo state networks

(2008) Xiaowei Lin et al.   EXPERT SYSTEMS WITH APPLICATIONS

Neuronal avalanches organize as nested theta- and beta/gamma-oscillations during development of cortical layer 2/3

(2008) E. D. Gireesh et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA