Noise in the nervous system

Abstract

Noise--random disturbances of signals--poses a fundamental problem for information processing and affects all aspects of nervous-system function. However, the nature, amount and impact of noise in the nervous system have only recently been addressed in a quantitative manner. Experimental and computational methods have shown that multiple noise sources contribute to cellular and behavioural trial-to-trial variability. We review the sources of noise in the nervous system, from the molecular to the behavioural level, and show how noise contributes to trial-to-trial variability. We highlight how noise affects neuronal networks and the principles the nervous system applies to counter detrimental effects of noise, and briefly discuss noise's potential benefits.

Figure 1. Overvie of the behavioural loop and the stages at which noise is present in the nervous system

a | Sources of sensory noise include the transduction of signals. This is exemplified here by a photoreceptor and its signal-amplification cascade. b | Sources of cellular noise include the ion channels of excitable membranes, synaptic transmission and network interactions (see BOX 2). c | Sources of motor noise include motor neurons and muscle. In the behavioural task shown (catching a ball), the nervous system has to act in the presence of noise in sensing, information processing and movement.

Figure 2. Examples amples of cellular noise

a | Channel noise as a source of trial-to-trial variability in action potential (AP) propagation. Stochastic simulations of the response of a 0.2 μm diameter CNS axon (comparable with a cerebellar parallel fibre) in response to repeated identical current stimuli and initial conditions are shown. The only source of variability is the stochastic opening and closing of a million individually simulated ion channels. Spike trains were triggered by a time-varying current stimulus (top plot). Spike raster plots for each measurement site are shown, from the soma (second-from-top plot) down to the most distal part (the axon; bottom plot). In each raster plot, the precise timing of spikes is marked by dots, which are stacked over each other for each repeated trial (there were 60 trials). The shift of the overall spike pattern across rows reflects the average propagation speed of the APs. The raster plot of the somatic measurement reflects spike-time variability from AP initiation. Owing to channel noise, the spike-time variability rapidl increases the further the AP propagates, and it eventually reaches millisecond orders. b | In vitro paired patch-clamp recordings demonstrate the trial-to-trial variability of synaptic transmission in rat somatosensory cortex slices. Six consecutive postsynaptic responses (black traces) to an identical presynaptic-stimulation pattern (top trace) are shown, along with the ensemble mean response (grey trace) from over 50 trials. Part a modified from REF. . Part b modified, with permission, from REEF. 77 © (2006) American Physical Society.