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Brain building 101: advances in large-scale neural simulation »

Chris Eliasmith

The construction of the world's largest functional brain model and large-scale, real-time hardware simulations rely on the same mathematical and neuromorphic methods.
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.

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.
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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