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A recipe for creating ideal hybrid memristive-CMOS neuromorphic processing systems

Applied physics letters, 2020-03, Vol.116 (12)

2020

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Tutorial: Concepts for closely mimicking biological learning with memristive devices: Principles to emulate cellular forms of learning

Journal of applied physics, 2018-10, Vol.124 (15)

2018

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A VLSI array of low-power spiking neurons and bistable synapses with spike-timing dependent plasticity

IEEE transaction on neural networks and learning systems, 2006-01, Vol.17 (1), p.211-221

2006

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Spiking Elementary Motion Detector in Neuromorphic Systems

Neural computation, 2018-09, Vol.30 (9), p.2384-2417

2018

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Artificial Bio-Inspired Tactile Receptive Fields for Edge Orientation Classification

2021 IEEE International Symposium on Circuits and Systems (ISCAS), 2021, p.1-5

2021

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A Multichip Pulse-Based Neuromorphic Infrastructure and Its Application to a Model of Orientation Selectivity

IEEE transactions on circuits and systems. 1, Fundamental theory and applications, 2007-05, Vol.54 (5), p.981-993

2007

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A VLSI recurrent network of integrate-and-fire neurons connected by plastic synapses with long-term memory

IEEE transaction on neural networks and learning systems, 2003-09, Vol.14 (5), p.1297-1307

2003

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A Spiking Recurrent Neural Network with Phase Change Memory Synapses for Decision Making

2020 IEEE International Symposium on Circuits and Systems (ISCAS), 2020, p.1-5

2020

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Spike-based learning with a generalized integrate and fire silicon neuron

2010 IEEE International Symposium on Circuits and Systems (ISCAS), 2010, p.1951-1954

2010

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Resistive switching synapses for unsupervised learning in feed-forward and recurrent neural networks

2018 IEEE International Symposium on Circuits and Systems (ISCAS), 2018, p.1-5

2018

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Attractor networks and associative memories with STDP learning in RRAM synapses

2017 IEEE International Electron Devices Meeting (IEDM), 2017, p.11.2.1-11.2.4

2017

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State-dependent sensory processing in networks of VLSI spiking neurons

2010 IEEE International Symposium on Circuits and Systems (ISCAS), 2010, p.2789-2792

2010

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Real-time inference in a VLSI spiking neural network

2012 IEEE International Symposium on Circuits and Systems (ISCAS), 2012, p.2425-2428

2012

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Live demonstration: State-dependent sensory processing in networks of VLSI spiking neurons

2010 IEEE International Symposium on Circuits and Systems (ISCAS), 2010, p.2788-2788

2010

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The feeling of color: A haptic feedback device for the visually disabled

2008 IEEE Biomedical Circuits and Systems Conference, 2008, p.381-384

2008

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Reliable Computation in Noisy Backgrounds Using Real-Time Neuromorphic Hardware

2007 IEEE Biomedical Circuits and Systems Conference, 2007, p.71-74

2007

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Exploiting device mismatch in neuromorphic VLSI systems to implement axonal delays

The 2012 International Joint Conference on Neural Networks (IJCNN), 2012, p.1-6

2012

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Characterizing the Firing Properties of an Adaptive Analog VLSI Neuron

Biologically Inspired Approaches to Advanced Information Technology, 2004, p.189-200

2004

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Modeling orientation selectivity using a neuromorphic multi-chip system

2006 IEEE International Symposium on Circuits and Systems (ISCAS), 2006, p.4 pp.

2006

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Exploring olfactory sensory networks: Simulations and hardware emulation

2010 Biomedical Circuits and Systems Conference (BioCAS), 2010, p.270-273

2010

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Function approximation with uncertainty propagation in a VLSI spiking neural network

The 2012 International Joint Conference on Neural Networks (IJCNN), 2012, p.1-7

2012

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An event-based VLSI network of integrate-and-fire neurons

2004

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An event-based VLSI network of integrate-and-fire neurons

2004 IEEE International Symposium on Circuits and Systems (ISCAS), 2004, Vol.5, p.V-357

2004

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An adaptive silicon synapse

2003 IEEE International Symposium on Circuits and Systems (ISCAS), 2003, Vol.1, p.I-I

2003

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Sample strategies for quantification of hepatic fat fraction mean MRI in healthy cats during body weight gain

PloS one, 2020-11, Vol.15 (11), p.e0241905-e0241905

2020

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A recipe for creating ideal hybrid memristive-CMOS neuromorphic processing systems

Tutorial: Concepts for closely mimicking biological learning with memristive devices: Principles to emulate cellular forms of learning

A VLSI array of low-power spiking neurons and bistable synapses with spike-timing dependent plasticity

Spiking Elementary Motion Detector in Neuromorphic Systems

Artificial Bio-Inspired Tactile Receptive Fields for Edge Orientation Classification

A Multichip Pulse-Based Neuromorphic Infrastructure and Its Application to a Model of Orientation Selectivity

A VLSI recurrent network of integrate-and-fire neurons connected by plastic synapses with long-term memory

A Spiking Recurrent Neural Network with Phase Change Memory Synapses for Decision Making

Spike-based learning with a generalized integrate and fire silicon neuron

Resistive switching synapses for unsupervised learning in feed-forward and recurrent neural networks

Attractor networks and associative memories with STDP learning in RRAM synapses

State-dependent sensory processing in networks of VLSI spiking neurons

Real-time inference in a VLSI spiking neural network

Live demonstration: State-dependent sensory processing in networks of VLSI spiking neurons

The feeling of color: A haptic feedback device for the visually disabled

Reliable Computation in Noisy Backgrounds Using Real-Time Neuromorphic Hardware

Exploiting device mismatch in neuromorphic VLSI systems to implement axonal delays

Characterizing the Firing Properties of an Adaptive Analog VLSI Neuron

Modeling orientation selectivity using a neuromorphic multi-chip system

Exploring olfactory sensory networks: Simulations and hardware emulation

Function approximation with uncertainty propagation in a VLSI spiking neural network

An event-based VLSI network of integrate-and-fire neurons

An event-based VLSI network of integrate-and-fire neurons

An adaptive silicon synapse

Sample strategies for quantification of hepatic fat fraction mean MRI in healthy cats during body weight gain

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