Your Central Nervous System is Noisy

Overview

• Random fluctuations of signals or ‘noise’ cause a fundamental problem for information processing and affect aspects of the central nervous system function.
• Sensory noise is noise in sensory signals and sensory receptors.
• Cellular noise is produced through stochastic processes such as the opening and closing of ion channels, protein production, the fusing of synaptic vesicles and the diffusion and binding of molecules to receptors.
• Electrical noise and synaptic noise are the two most dominant sources of cellular noise.
• Motor noise is generated when neural signals are converted into mechanical forces in their muscle fibres.
• Sometimes noise is important as a weak signal can be amplified via Stochastic Resonance (SR).
• There are certain principles that the CNS uses to compensate for this noise.
• The principle of averaging is one of two mechanisms the CNS uses to compensate for noise. In this principle, the CNS compensates for noise by averaging over sources of redundant information.
• The principle of prior knowledge is another mechanism the CNS utilises to counter noise. Essentially, the CNS exploits the expected nature of signals and noise.
• Recent evidence has suggested that neurotransmitters such as dopamine (DA) and glutamate (GLU) play a role in neural noise modulation for optimal functioning of the nervous system.

Sources of Noise in the Central Nervous System (CNS)

Noise can be explained as random disturbances of signals that can pose problems for information processing and this affects certain aspects of the CNS (Faisal et al., 2008). Here, I discuss a research paper published by Faisal et al. (2008) which has over 2000 citations currently. Clearly, this paper has a lot of influence in the scientific and research community and hence, it is important for people outside of the research community to know what this research paper is all about. So where does noise come from in the central nervous system?

Faisal et al. (2008) clearly explain the sources of noise in the CNS, and I have outlined them briefly in this section.

Sensory Noise

This noise is noise that gets produced when we experience external sensory stimuli. For example, in the first stage of perception, energy in the sensory stimulus is converted into a chemical signal (e.g. through absorption of photons for vision, or through ligand-binding of odour molecules in olfaction) or a mechanical signal (movement of hair cells in hearing). This conversion of sensory stimuli into chemical/mechanical signals is called signal transduction. The transduction process also amplifies the sensory signal and converts it into an electrical signal. This transduction of signals (especially during the amplification process) can generate noise and therefore, is a source of noise in the CNS. Faisal et al. (2008) also suggested that this noise can lead to trial-to-trial variability. Sensory noise may limit the information that is available to other areas of the CNS (Faisal et al., 2008).

Cellular Noise

In each neuron, noise can accumulate due to the randomness of cellular machinery that processes information (Calvin & Stevens, 1968). At the biochemical and biophysical levels, stochastic processes such as the opening and closing of ion channels, protein production, the fusing of synaptic vesicles and the diffusion and binding of molecules to receptors can produce noise. Faisal et al. (2008) mentioned that noise generated at this molecular level can affect or alter the whole cell. Whilst, these fluctuations are averaged out given the scale of these structures, most neurons are tiny, and can be affected by noise. The two most common sources of cellular noise in the CNS, as proposed by Faisal et al. (2008), are electrical and synaptic noise.

a) Electrical Noise and Action Potentials (AP)

The most dominant source of electrical noise is channel noise- that is, currents produced by the random opening and closing of voltage or ion-gated channels (ion channels are proteins found in the cell membrane). These channel noises can account for variability in the AP threshold at the node of Ranvier. Note: an action potential occurs when a neuron transmits information down an axon, away from the cell body. Moreover, channel noise in the dendrites and the cell body produce noise that is large enough to affect AP timing.

b) Synaptic Noise

Many cells at the neocortex receive intense synaptic bombardment from thousands of synapses which can be referred to as synaptic background noise. Synapses are small gaps between two neurons and information is sent through the synapse to the other neuron. Faisal et al. (2008) mentioned that the classic manifestation of synaptic noise is the spontaneous miniature postsynaptic current (mPCS). These are small currents that occur due to the spontaneous release (without a presynaptic AP) of transmitter (GABA/glutamate) vesicles by presynaptic terminals (Wierenga & Wadman, 1999). In simple terms, synaptic noise is noise generated from biochemical processes that underlie synaptic transmission.

Motor Noise

This noise is generated when neural signals are converted into mechanical forces in their muscle fibres. Faisal et al. (2008) suggested that the architecture of motor neurons and their muscle fibres make conversion noisy. Moreover, the authors mentioned that the brain organises movements to minimise the effects of motor noise on motor variability.

Noise is Not Always Bad

In one of my previous blog posts, I mentioned that noise can be good for you and I recommend that you have a read at that article. In that post, I introduced the concept of stochastic resonance, where adding noise to a weak signal (which is not detectable), actually enhances its detection, now making it detectable for a system (Moss et al., 2004). This is also true for the noise that is generated in the nervous system. For example, Simmons et al. (2009) mentioned that high neural noise can actually help autistic individuals with enhanced detection of details, however the same high internal noise can produce negative effects for more complex processing such as processing of faces.

How does CNS manage noise?

Although there are positive effects of noise, too much noise can be detrimental for the CNS. Moreover, noise cannot be removed from a signal once it has been added and therefore, there are certain principles that the CNS needs to use to compensate for this noise. Faisal et al. (2008) mentioned two ways in which the CNS manages noise. The authors mentioned that the principle of averaging was one way in which the CNS managed noise. This means that the CNS compensates for noise by averaging over sources of redundant information (the presence of duplicate information in a message). The principle of averaging is applied whenever this redundant information is present across sensory inputs to the CNS (Faisal et al. 2008). Averaging is seen at the initial stage of sensory processing, for example, in vision, visual inputs are averaged over photoreceptors with adjacent or overlapping receptive fields in the eye (Kendel et al., 2009).

The principle of prior knowledge is another way in which CNS counters noise. Essentially, the CNS exploits the expected nature of signals and noise. This process is helpful when dealing with sensory signals in the natural world that are highly structured but are also redundant (Van Steveninck et al., 1996). The CNS may apply this principle in combination with averaging for sensory processing (Faisal et al., 2008).

Recent evidence has suggested that neurotransmitters such as dopamine (DA) and glutamate (GLU) play a role in neural noise modulation for optimal functioning of the nervous system (Hong & Rebec, 2012). The authors suggested that neural noise is highest at both low and high levels of DA and GLU. With aging and neurodegenerative conditions such as Alzheimer’s disease, Huntington’s disease, and Parkinson’s disease, the range over which DA and GLU is decreased leads to inflexibility in brain activity and behaviour. As the capacity to modulate neural noise is restricted, the ability to shift noise from one brain region to another is reduced, which leads to constant levels of noise (signal-noise ratios) across the entire brain (Hong & Rebec, 2012). This constant level of neuronal noise may prevent highs and lows in noise levels and brain activation variability characteristics of a healthy nervous system. Below is an image that explains why constant noise levels can be detrimental.

For a complete review and overview, I recommend you read the following full articles: Noise in the nervous system and Biological sources of inflexibility in brain and behaviour with aging and neurodegenerative diseases.

Latest From Instagram

• Instagram
• LinkedIn
• Facebook
• Mail

References

Andrewes, D. (2015). Neuropsychology: From theory to practice. Psychology Press.

Calvin, W. H., & Stevens, C. F. (1968). Synaptic noise and other sources of randomness in motoneuron interspike intervals. Journal of neurophysiology, 31(4), 574-587.

Simmons, D. R., Robertson, A. E., McKay, L. S., Toal, E., McAleer, P., & Pollick, F. E. (2009). Vision in autism spectrum disorders. Vision research, 49(22), 2705-2739.

Faisal, A. A., Selen, L. P., & Wolpert, D. M. (2008). Noise in the nervous system. Nature reviews neuroscience, 9(4), 292-303.

Hong, S. L., & Rebec, G. V. (2012). Biological sources of inflexibility in brain and behavior with aging and neurodegenerative diseases. Frontiers in systems neuroscience, 6, 77.

Kandel, E. R., Schwartz, J. H., Jessell, T. M., Siegelbaum, S., Hudspeth, A. J., & Mack, S. (Eds.). (2000). Principles of neural science (Vol. 4, pp. 1227-1246). New York: McGraw-hill.

Moss, F., Ward, L. M., & Sannita, W. G. (2004). Stochastic resonance and sensory information processing: a tutorial and review of application. Clinical neurophysiology, 115(2), 267-281.

Van Steveninck, R. D. R., & Laughlin, S. B. (1996). The rate of information transfer at graded-potential synapses. Nature, 379(6566), 642-645.

Wierenga, C. J., & Wadman, W. J. (1999). Miniature inhibitory postsynaptic currents in CA1 pyramidal neurons after kindling epileptogenesis. Journal of neurophysiology, 82(3), 1352-1362.

More from the NeuroBlog