A framework to enhance deep learning using first-spike times

Researchers at Heidelberg University and University of Bern have recently devised a technique to achieve fast and energy-efficient computing using spiking neuromorphic substrates. This strategy, introduced in a paper published in Nature Machine Intelligence, is a rigorous adaptation of a time-to-first-spike (TTFS) coding scheme, together with a corresponding learning rule implemented on certain networks of artificial neurons. TTFS is a time-coding approach, in which the activity of neurons is inversely proportional to their firing delay.