The design and measurement of a time-domain analog spiking neuron is described. The proposed neuron leverages time-domain processing using voltage-controlled oscillators (VCOs) and a time-domain comparator to integrate the input spike and trigger the output spike. A novel leaky circuit uses a phase-locked loop (PLL) to drive the phase difference between the two VCOs toward zero. A weighted capacitive digital-to-analog converter (CDAC) synapse merges the input spikes and phase-frequency detector (PFD) outputs to generate the VCO control voltage. The neuron is implemented in a 28-nm CMOS technology and operates under a subthreshold supply voltage of 0.35 V. Occupying 154 μm2, measurement shows a maximum spike rate of 5.5 MHz and energy consumption of 159 fJ/spike.

We present a transfer learning method inspired by modulatory neurotransmitter mechanisms in biological brains and explore applications for neuromorphic hardware. In this method, the pre-trained weights of an artificial neural network are held constant and a new, similar task is learned by manipulating the firing sensitivity of each neuron via a supplemental bias input. We refer to this as neuromodulatory tuning (NT). We demonstrate empirically that neuromodulatory tuning produces results comparable with traditional fine-tuning (TFT) methods in the domain of image recognition in both feed-forward deep learning and spiking neural network architectures. In our tests, NT reduced the number of parameters to be trained by four orders of magnitude as compared with traditional fine-tuning methods. We further demonstrate that neuromodulatory tuning can be implemented in analog hardware as a current source with a variable supply voltage. Our analog neuron design implements the leaky integrate-and-fire model with three bi-directional binary-scaled current sources comprising the synapse. Signals approximating modulatory neurotransmitter mechanisms are applied via adjustable power domains associated with each synapse. We validate the feasibility of the circuit design using high-fidelity simulation tools and propose an efficient implementation of neuromodulatory tuning using integrated analog circuits that consume significantly less power than digital hardware (GPU/CPU).