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Neuromorphic silicon neuron circuits »

Giacomo Indiveri, Bernabé Linares-Barranco, Tara Julia Hamilton, André van Schaik, Ralph Etienne-Cummings, Tobi Delbruck, Shih-Chii Liu, Piotr Dudek, Philipp Häfliger, Sylvie Renaud, Johannes Schemmel, Gert Cauwenberghs, John Arthur, Kai Hynna, Fopefolu Folowosele, Sylvain Saighi, Teresa Serrano-Gotarredona, Jayawan Wijekoon, Yingxue Wang, and Kwabena Boahen

In this paper we describe the most common building blocks and techniques used to implement spiking neuron circuits, and present an overview of a wide range of neuromorphic silicon neurons, which implement different computational models. We compare the different design methodologies used for each silicon neuron design described, and demonstrate their features with experimental results.

On spike-timing-dependent-plasticity, memristive devices, and building a self-learning visual cortex »

Carlos Zamarreño-Ramos, Luis A. Camuñas-Mesa, Jose A. Pérez-Carrasco, Timothée Masquelier, Teresa Serrano-Gotarredona, and Bernabé Linares-Barranco

In this paper we are linking one type of memristor nanotechnology devices to the biological synaptic update rule known as spike-time-dependent-plasticity (STDP) found in real biological synapses. This allows neuromorphic engineers to develop circuit architectures that use this type of memristors to artificially emulate parts of the visual cortex.

  Neural
Using neuron dynamics for realistic synaptic learning »

Christian Mayr, Johannes Partzsch, Marko Noack, and Rene Schueffny

Co-developing neuromorphic integrated circuit learning models and derivations significantly increases biological accuracy and reduces circuit complexity.
 
Adaptive sound localization with a silicon cochlea pair »

Vincent Yue-Sek Chan, Craig T. Jin, and André van Schaik

A neuromorphic sound localization system is presented, based on the extraction of interaural time difference from a far-field source and employing two microphones and a pair of silicon cochleae with address event interface for front-end processing.

Analyzing spike-timing-dependent plasticity in recurrent neuronal networks »

Matthieu Gilson, Anthony Burkitt, David Grayden, Doreen Thomas, and Leo van Hemmen

A mathematical framework is being used to investigate the learning dynamics induced by a class of biologically realistic synaptic plasticity rules in recurrently connected neuronal networks.
 

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