  A common view in the neuroscience community is that memory is encoded in the
connection strength between neurons. This perception led artificial neural
network models to focus on connection weights as the key variables to modulate
learning. In this paper, we present a prototype for weightless spiking neural
networks that can perform a simple classification task. The memory in this
network is stored in the timing between neurons, rather than the strength of
the connection, and is trained using a Hebbian Spike Timing Dependent
Plasticity (STDP), which modulates the delays of the connection.
