Dynamically reconfigurable silicon array of generalized integrate-and-fire neurons

In this paper we present a highly scalable, dynamically reconfigurable, energy efficient silicon neuron model for large scale neural networks. This model is a simplification of the generalized linear integrate-and-fire neuron model. The presented model is capable of reproducing 9 of the 20 prominent biologically relevant neuron behaviors. The circuits are designed for a 0.5 μm process and occupy an area of 1029 μm2, while only consuming an average power of 0.38 nW at 1 kHz.