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

Information-Theoretic Intrinsic Plasticity for Online Unsupervised Learning in Spiking Neural Networks

(2019) Wenrui Zhang et al.   Frontiers in Neuroscience

Dynamic parallelism for synaptic updating in GPU-accelerated spiking neural network simulations

(2018) Bahadir Kasap et al.   NEUROCOMPUTING

Learning to Recognize Actions From Limited Training Examples Using a Recurrent Spiking Neural Model

(2018) Priyadarshini Panda et al.   Frontiers in Neuroscience

A mean field view of the landscape of two-layer neural networks

(2018) Song Mei et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

SpiLinC: Spiking Liquid-Ensemble Computing for Unsupervised Speech and Image Recognition

(2018) Gopalakrishnan Srinivasan et al.   Frontiers in Neuroscience

A Spiking Neural Network Framework for Robust Sound Classification

(2018) Jibin Wu et al.   Frontiers in Neuroscience

Energy efficient parallel neuromorphic architectures with approximate arithmetic on FPGA

(2017) Qian Wang et al.   NEUROCOMPUTING

Reservoir computing using dynamic memristors for temporal information processing

(2017) Chao Du et al.   Nature Communications

An Online Unsupervised Structural Plasticity Algorithm for Spiking Neural Networks

(2017) Subhrajit Roy et al.   IEEE Transactions on Neural Networks and Learning Systems

Learning to Generate Sequences with Combination of Hebbian and Non-hebbian Plasticity in Recurrent Spiking Neural Networks

(2017) Priyadarshini Panda et al.   Frontiers in Neuroscience

Morphological Properties of Mass–Spring Networks for Optimal Locomotion Learning

(2017) Gabriel Urbain et al.   Frontiers in Neurorobotics

An Online Structural Plasticity Rule for Generating Better Reservoirs

(2016) Subhrajit Roy et al.   NEURAL COMPUTATION

Unsupervised Learning in an Ensemble of Spiking Neural Networks Mediated by ITDP

(2016) Yoonsik Shim et al.   PLoS Computational Biology

Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines

(2016) Emre O. Neftci et al.   Frontiers in Neuroscience

Multiple linear regression

(2015) Martin Krzywinski et al.   NATURE METHODS

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

Liquid computing on and off the edge of chaos with a striatal microcircuit

(2014) Carlos Toledo-Suárez et al.   Frontiers in Computational Neuroscience

The MNIST Database of Handwritten Digit Images for Machine Learning Research [Best of the Web]

(2012) Li Deng   IEEE SIGNAL PROCESSING MAGAZINE

Energy-Efficient Neuron, Synapse and STDP Integrated Circuits

(2012) J. M. Cruz-Albrecht et al.   IEEE Transactions on Biomedical Circuits and Systems

Computer modelling: Brain in a box

(2012) M. Mitchell Waldrop   NATURE

Use of the separation property to derive Liquid State Machines with enhanced classification performance

(2012) Emmanouil Hourdakis et al.   NEUROCOMPUTING

Ephaptic coupling of cortical neurons

(2011) Costas A Anastassiou et al.   NATURE NEUROSCIENCE

Distributed Fading Memory for Stimulus Properties in the Primary Visual Cortex

(2009) Danko Nikolić et al.   PLOS BIOLOGY

Generalized Linear Discriminant Analysis: A Unified Framework and Efficient Model Selection

(2008) Shuiwang Ji et al.   IEEE TRANSACTIONS ON NEURAL NETWORKS

Brian: a simulator for spiking neural networks in Python

(2008) Dan Goodman   Frontiers in Neuroinformatics