Temporal precision in the neural code and the timescales of natural vision

Precision in vision In mammalian visual system, spikes evoked by visual stimuli have millisecond-scale timing even though the relevant timescales of visual processing themselves are much slower. It has therefore long been debated whether spike timing itself carries some form of the neural code. Now experiments in the lateral geniculate nucleus of cats, the part of the brain that is the primary processor of visual information, show that spike timing precision is not absolute for all classes of visual stimuli. Rather, the degree of precision is relative to the timescale of the stimulus, and this relatively high level of precision is required to construct an accurate representation of the stimulus. In the mammalian visual system, spikes evoked by visual stimuli have millisecond-scale timing, even though the relevant time scales of visual processing themselves are much slower. In cat lateral geniculate nucleus, spike timing precision is not absolute for all classes of visual stimuli, but is relative to the time scale of the stimulus. Further, it is demonstrated that this relatively high level of precision is required to construct an accurate representation of the stimulus. The timing of action potentials relative to sensory stimuli can be precise down to milliseconds in the visual system 1 , 2 , 3 , 4 , 5 , 6 , 7 , even though the relevant timescales of natural vision are much slower. The existence of such precision contributes to a fundamental debate over the basis of the neural code and, specifically, what timescales are important for neural computation 8 , 9 , 10 . Using recordings in the lateral geniculate nucleus, here we demonstrate that the relevant timescale of neuronal spike trains depends on the frequency content of the visual stimulus, and that ‘relative’, not absolute, precision is maintained both during spatially uniform white-noise visual stimuli and naturalistic movies. Using information-theoretic techniques, we demonstrate a clear role of relative precision, and show that the experimentally observed temporal structure in the neuronal response is necessary to represent accurately the more slowly changing visual world. By establishing a functional role of precision, we link visual neuron function on slow timescales to temporal structure in the response at faster timescales, and uncover a straightforward purpose of fine-timescale features of neuronal spike trains.