Abstract
Repeating spatiotemporal spike patterns exist and carry information. How this information is extracted by downstream neurons is unclear. Here we theoretically 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 τ. Using a leaky integrate-and-fire (LIF) neuron with homogeneous Poisson input, we computed this optimum analytically. We found that a relatively small τ (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 equipped with additive STDP, 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 is optimal. These mechanisms may explain how humans learn repeating sensory 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 10-ms bins. Coincidence detection is a simple yet powerful mechanism, which could be the main function of neurons in the brain.
Highlights
Electrophysiologists report the existence of repeating spike sequence involving multiple cells, called “spatiotemporal spike patterns”, with precision in the millisecond range, both in vitro and in vivo, lasting from a few tens of ms to several seconds [Tiesinga et al, 2008]
The spike patterns contain information about the stimulus. How this information is extracted by downstream neurons is unclear. Can it be done by neurons only one synapse away from the recorded neurons? Or are multiple integration steps needed? Can it be done by simple coincidence detector neurons, or should other temporal features, such as spike ranks, be taken into account? Here we wondered how far we can go with the simplest scenario: the readout is done by simple coincidence detector neurons only one synapse away from the neurons involved in the repeating pattern
We only modeled the Long Term Potentiation part of spike-timing-dependent plasticity (STDP), ignoring its Long Term Depression (LTD) term
Summary
Electrophysiologists report the existence of repeating spike sequence involving multiple cells, called “spatiotemporal spike patterns”, with precision in the millisecond range, both in vitro and in vivo, lasting from a few tens of ms to several seconds [Tiesinga et al, 2008]. The spike patterns contain information about the stimulus How this information is extracted by downstream neurons is unclear. We wondered how far we can go with the simplest scenario: the readout is done by simple coincidence detector neurons only one synapse away from the neurons involved in the repeating pattern. We demonstrate that this approach can lead to very robust pattern detectors, provided that the membrane time constants are relatively short, possibly much shorter than the pattern duration. An unsupervised learning mechanism must be at work It could be the so called spike-timingdependent plasticity (STDP). The potential with Poisson input (noise period), and σnoise its standard deviation (see Figure 1)
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