Time-series prediction with single integrate-and-fire neuron
Applied Soft Computing
Learning with single quadratic integrate-and-fire neuron
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Associative Learning of Integrate-and-Fire Neurons with Memristor-Based Synapses
Neural Processing Letters
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Spatio-temporal processing of spike trains by neuronal networks depends on a variety of mechanisms distributed across synapses, dendrites, and somata. In natural systems, the spike trains and the processing mechanisms cohere though their common physical instantiation. This coherence is lost when the natural system is encoded for simulation on a general purpose computer. By contrast, analog VLSI circuits are, like neurons, inherently related by their real-time physics, and so, could provide a useful substrate for exploring neuronlike event-based processing. Here, we describe a hybrid analog-digital VLSI chip comprising a set of integrate-and-fire neurons and short-term dynamical synapses that can be configured into simple network architectures with some properties of neocortical neuronal circuits. We show that, despite considerable fabrication variance in the properties of individual neurons, the chip offers a viable substrate for exploring real-time spike-based processing in networks of neurons.