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We demonstrate diverse aspects of attractor dynamics of recurrent spiking neural networks implemented in a multi-chip neuromorphic system. Reconfigurable synaptic connectivity within each chip and between chips (the latter is implemented by a previously developed PCI-AER board) allow to arrange different architectures for the 256 spiking neurons and 32k hebbian bistable plastic synapses, distributed on two chips. We report preliminary results on three scenarios: 1. attractor dynamics and robust working memory states, 2. real-time learning of attractor-based neural representations of simple visual stimuli acquired in real time through a neuromorphic sensor, 3. competitive behavior modeling stochastic decision making dynamics.
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