A spiking neural network includes a plurality of neurons implemented in respective circuits. Each neuron produces a continuous-valued membrane potential according to a Growth Transform bounded by an extrinsic energy constraint. The continuous-valued membrane potential is defined as a function of spiking current received from another neuron in the plurality of neurons, and a received electrical current stimulus. The spiking neural network includes a network energy function representing network energy consumed by the plurality of neurons and a neuromorphic framework. The neuromorphic framework minimizes network energy consumed by the plurality of neurons to determine the extrinsic energy constraint, models synaptic connections among the plurality of neurons as respective transconductances that regulate magnitude of spiking currents received from each of the plurality of neurons by each other of the plurality of neurons, and encodes the received electrical current stimulus in corresponding continuous-valued membrane potentials of the plurality of neurons.
METHOD FOR DESIGNING SCALABLE AND ENERGY-EFFICIENT ANALOG NEUROMORPHIC PROCESSORS