Trends in Neurosciences
ReviewReliability, synchrony and noise
Introduction
Compared with their artificial analogues, biological systems are often considered quite unreliable. Each press of the space bar on a keyboard has a >99% chance of transmitting the appropriate signal to the computer. By contrast, a moving of a whisker might have only a 15% chance of generating a spike in a corresponding layer 4 neuron in a mouse's somatosensory cortex [1]. If computers were so unreliable, they would be nearly useless devices. Often this unreliability is attributed to the noisiness of biological systems. That is, the behavior of many biological systems is often considered to be stochastic or probabilistic. Indeed, recordings from neurons in vivo and in vitro have shown that total membrane current can be described as being randomly drawn from a Gaussian distribution (Figure 1) and that action potentials can be described as occurring randomly in time according to a Poisson process (see Glossary for a definition) 2, 3, 4. Discussion of neuronal noise has focused on how neurons can overcome or compensate for this noise, for example by averaging across time or across neurons, and still process and transmit information [4]. Here we take a contrasting view and discuss experimental and theoretical results that emphasize how neurons can behave reliably and synchronously not despite of, but because of, noise.
Section snippets
A simple example
Many of the ideas needed for this review can be introduced by considering the simple example of current injection into a neuron during an intracellular recording. If the current is rapidly stepped from zero to a value large enough to fire the cell, the neuron will fire a series of action potentials, namely a spike train. Maintaining this level of current for an extended time will allow the frequency of action potentials to stabilize, resulting in a regular clock-like series of spikes which will
Effects of noise on single spike trains
Adding noise to an input signal changes many features of a neuron's spike train. If the amplitude of the noise is large enough, noise can push a neuron from rest over threshold, making a neuron spike, even if the average value of the input is zero. Thus, adding noise can make an unresponsive neuron responsive. The effectiveness of the noise at generating spikes will vary with the noise amplitude and spectrum [15] (see Glossary). Similarly, for a neuron that is firing at a low rate, the addition
Reliability and synchrony
Assessing the reliability of neuronal spiking is critical to understanding what features of spike trains might be important for coding stimuli. If any feature of the spike train (such as spike times) is not reliably generated when stimuli are repeated, then this feature will not carry information about the stimulus, restricting possible spike time coding schemes [20]. In vivo data have shown that neurons are reliable enough to transmit information on a timescale of ∼10 ms, although this is
Oscillatory synchronization and noise
Local field potentials or EEG recordings often show large oscillatory responses, indicating that the firing of many nearby neurons is periodic and synchronized [37]. These oscillations often are synchronized across multiple recording sites, indicating that this synchronization occurs between distant brain areas. Such short-range and long-range synchronization might be generated by several distinct mechanisms. Local oscillations are often considered to arise owing to features of local circuit
Explanation of noise-induced synchronization
At first glance, correlated noise seems unlikely to support synchronization, but this phenomenon can be understood by considering the following example. Consider the effect of a small input delivered simultaneously to many neurons in the population (Figure 3b). If these neurons are firing regularly they can be modeled as oscillators (see Box 1) [49], and their current state can be described by their phase. In this case, the degree of synchronization in the network can be measured as the
Noise-induced synchrony in context
Synchronization of neuronal oscillations by noise requires that neurons receive partially correlated input. This is likely to be a common feature of brain areas in which there is divergent local connectivity or a strong topographic input. In the cortex, a single interneuron can be connected with tens of thousands of local circuit interneurons. Thus, activity of this single cell will provide correlated input to many neurons in the local circuit. Alternatively, correlated inputs might be stimulus
Conclusions
Noise is usually considered destructive, or disruptive in the processing and transmission of information. In this review, we have discussed many examples demonstrating that noise can be constructive, leading to larger, more patterned and more useful responses. Noise can cause single neurons to fire more regularly within a trial, and more reliably across trials, and correlated noise can cause synchronization of both aperiodic and periodic activity across populations of neurons. This work, and
Acknowledgements
We would like to thank Alison Barth and Matt Angle for reading this manuscript. This work was supported by NIH R01DC005798, R01MH079504 and NSF DMS 0513500.
Glossary
- Coherence resonance
- a phenomenon in which the regularity (see above) of a process is maximal when the input has a nonzero noise amplitude.
- Noise
- a signal that varies as a function of time, the value of which at any given time is drawn randomly from some distribution. Noise can be described by its spectrum and its amplitude. The spectrum of noise describes how rapidly the value of the signal is changing. If fluctuations occur such that the value of the signal at any time is completely independent
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