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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.

Adaptive, brain-like systems give robots complex behaviors »

Gennady Livitz, Massimiliano Versace, Anatoli Gorchetchnikov, Heather Ames, Jasmin Léveillé, Ben Chandler, Ennio Mingolla, and Zlatko Vasilkoski

Converging advances in memory, parallel computers, and neural network models will soon allow for systems that can support complicated activities in virtual and robotic agents.
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.
MoNETA: A Mind Made from Memristors »

Massimiliano Versace and Ben Chandler

DARPA's new memristor-based approach to AI consists of a chip that mimics how neurons process information.


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