  By recording multiple cells simultaneously, electrophysiologists have found
evidence for repeating spatiotemporal spike patterns, which can carry
information. How this information is extracted 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 the optimal parameters to do so
are, in particular the membrane time constant $\tau$. Using a leaky
integrate-and-fire (LIF) neuron with instantaneous synapses and homogeneous
Poisson input, we were able to compute 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 much longer. This is somewhat counter
intuitive 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 spike-timing-dependent potentiation and
homeostatic rate-based depression, and repeatedly exposed it 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, 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.

Neural and Evolutionary Computing;Neurons and Cognition