DELTRON: Neuromorphic architectures for delay based learning

We present a neuromorphic spiking neural network, the DELTRON, that can remember and store patterns by changing the delays of every connection as opposed to modifying the weights. The advantage of this architecture over traditional weight based ones is simpler hardware implementation without multipliers or digital-analog converters (DACs). The name is derived due to similarity in the learning rule with an earlier architecture called Tempotron. We present simulations of memory capacity of the DELTRON for different random spatio-temporal spike patterns and also present SPICE simulation results of the core circuits involved in a reconfigurable mixed signal implementation of this architecture.