  Precise spatio-temporal patterns of neuronal action potentials underly e.g.
sensory representations and control of muscle activities. However, it is not
known how the synaptic efficacies in the neuronal networks of the brain adapt
such that they can reliably generate spikes at specific points in time.
Existing activity-dependent plasticity rules like Spike-Timing-Dependent
Plasticity are agnostic to the goal of learning spike times. On the other hand,
the existing formal and supervised learning algorithms perform a temporally
precise comparison of projected activity with the target, but there is no known
biologically plausible implementation of this comparison. Here, we propose a
simple and local unsupervised synaptic plasticity mechanism that is derived
from the requirement of a balanced membrane potential. Since the relevant
signal for synaptic change is the postsynaptic voltage rather than spike times,
we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP).
Combining our plasticity mechanism with spike after-hyperpolarization causes a
sensitivity of synaptic change to pre- and postsynaptic spike times which can
reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as
was found in experiments. In addition, the sensitivity of MPDP to the time
course of the voltage when generating a spike allows MPDP to distinguish
between weak (spurious) and strong (teacher) spikes, which therefore provides a
neuronal basis for the comparison of actual and target activity. For
spatio-temporal input spike patterns our conceptually simple plasticity rule
achieves a surprisingly high storage capacity for spike associations. The
sensitivity of the MPDP to the subthreshold membrane potential during training
allows robust memory retrieval after learning even in the presence of activity
corrupted by noise.
