This paper presents a deterministic and adaptive spike model derived from radial basis functions
and a leaky integrate-and-fire sampler developed for training spiking neural networks without direct
weight manipulation. Several algorithms have been proposed for training spiking neural networks
through biologically-plausible learning mechanisms, such as spike-timing-dependent synaptic plasticity
and Hebbian plasticity. These algorithms typically rely on the ability to update the synaptic strengths,
or weights, directly, through a weight update rule in which the weight increment can be decided
and implemented based on the training equations. However, in several potential applications of
adaptive spiking neural networks, including neuroprosthetic devices and CMOS/memristor nanoscale
neuromorphic chips, the weights cannot be manipulated directly and, instead, tend to change over time
by virtue of the pre- and postsynaptic neural activity. This paper presents an indirect learning method
that induces changes in the synaptic weights by modulating spike-timing-dependent plasticity by means
of controlled input spike trains. In place of the weights, the algorithm manipulates the input spike trains
used to stimulate the input neurons by determining a sequence of spike timings that minimize a desired
objective function and, indirectly, induce the desired synaptic plasticity in the network.
Stable learning of functional maps in self-organizing spiking neural networks with continuous synaptic plasticity
