Dynamic spike threshold and nonlinear dendritic computation for coincidence detection in neuromorphic circuits

We present an electronic cortical neuron incorporating dynamic spike threshold and active dendritic properties. The circuit is simulated using a carbon nanotube field-effect transistor SPICE model. We demonstrate that our neuron has lower spike threshold for coincident synaptic inputs; however when the synaptic inputs are not in synchrony, it requires larger depolarization to evoke the neuron to fire. We also demonstrate that a dendritic spike is key to precisely-timed input-output transformation, produces reliable firing and results in more resilience to input jitter within an individual neuron.