Fopefolu Folowosele

TL;DR: The most common building blocks and techniques used to implement these circuits, and an overview of a wide range of neuromorphic silicon neurons, which implement different computational models, ranging from biophysically realistic and conductance-based Hodgkin–Huxley models to bi-dimensional generalized adaptive integrate and fire models.

TL;DR: This silicon neuron is presented which is based on a modified version of the Mihalas-Niebur neural model and has low complexity and reliable matching and can thus be easily integrated into more complex neuromorphic systems.

TL;DR: Results from the first circuit implementation of the Mihalas-Niebur neural model with six spiking patterns observed in biological regular-spiking, fast- spiking and low-threshold spiking inhibitory neurons are presented.

TL;DR: The 0.5 μm complementary metal-oxide-semiconductor (CMOS) implementation of the Mihalaş-Niebur neuron model is presented, a generalized model of the leaky integrate-and-fire neuron with adaptive threshold that is able to produce most of the known spiking and bursting patterns that have been observed in biology.

TL;DR: This paper provides a spike-based implementation of the HMAX model, demonstrating its ability to perform biologically-plausible MAX computations as well as classify basic shapes.