Cites a lot of good papers on neural synchrony. Should look into these.
Neurons with "rebound spiking" - latency of spike depends on length of inhibition. When inhibition is released, spikes will be fast if neuron was inhibited for a long time, may not spike if short inhibition. The RF of these neurons is any stimuli which elicits a response - e.g. inhibition for more than 200 ms.
Synchrony Receptive Field (SRF): a stimuli which elicits synchronous firing in two neurons. Requires heterogeneous properties across neurons (otherwise, SRF would be all stimuli).
Synchrony detection depends on the time-constant - d' = w/s * (1 - exp(-d/t)), where w is synaptic weight, s is standard deviation of noise, d is delay between spikes and t is the neuron's time constant. Essentially the coincidence sensitivity are related to the neural time-constant. A low FA rate is important to get a high Hit rate with coincident spikes.
For learning need homeostatic mechanisms to keep a low FA rate - ala Turrigianno. Multiplicative scaling to maintain stable firing rate, F. Weights are learned via STDP, STDP time-constant is same as membrane time constant. Only uses LTP component of STDP, no LTD - relies on homeostasis for depression.
Precise spike-timing is not important, but relative spike-timing is. The system can decodes relative spike-timing, and is able to maintain fairly precise relative spike timing.
Synchrony coding has neurons fire when they are close to a hyperplane, as opposed to rate coding where they fire on one side of a hyperplane. There is robustness to variability with a synchrony code that a rate-like code does not handle.
He goes on to show a lot more examples of specific systems that could be using a synchrony code and how they might work. Olfaction, binaural hearing, and some vision.
This is an interesting paper, adding in the gamma oscillation would allow a synchrony code to propagate. Also, having dendrites do the coincidence detection, and then combine for an output would be powerful.