STDP allows close-to-optimal spatiotemporal spike pattern detection by single coincidence detector neurons

By recording multiple cells simultaneously, electrophysiologists have found evidence for repeating spatiotemporal spike patterns. In sensory systems in particular, repeating a sensory sequence typically elicits a reproducible spike pattern, which carries information about the sensory sequence. How this information is readout by downstream neurons is unclear. In this theoretical paper, we investigate to what extent a single cell could detect a given spike pattern and what are the optimal parameters to do so, in particular the membrane time constant $\tau$. Using a leaky integrate-and-fire (LIF) neuron with instantaneous synapses and homogeneous Poisson inputs, we computed this optimum analytically. Our results indicate that a relatively small $\tau$ (at most a few tens of ms) is usually optimal, even when the pattern is longer. This is somewhat surprising as the resulting detector ignores most of the pattern, due to its fast memory decay. Next, we wondered if spike-timing-dependent plasticity (STDP) could enable a neuron to reach the theoretical optimum. We simulated a LIF neuron equipped with additive STDP, and repeatedly exposed to a given input spike pattern. As in previous studies, the LIF progressively became selective to the repeating pattern with no supervision, even when the pattern was embedded in Poisson activity. Here we show that, using certain STDP parameters, the resulting pattern detector can be optimal. Taken together, these results may explain how humans can learn repeating visual or auditory sequences. Long sequences could be recognized thanks to coincidence detectors working at a much shorter timescale. This is consistent with the fact that recognition is still possible if a sound sequence is compressed of played backward, or scrambled using 10ms bins. Coincidence detection is a simple yet powerful mechanism, which could be the main function of neurons in the brain.