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neuron noise
This shows how noise affects the transmission of signals when non-spiking neurons are propagating the signal.

Neuronal Noise is a broad category that refer to the random electrical fluctuations within neuronal networks that is not associated with encoding a behavior. These fluctuations can be from one to two orders of magnitude [1]. Most noise is sub-threshold, but some noise can be in the form of an action potential. For an example, the brain contains pacemaker-like cells that produce action potentials in the absence of neural input. Some suggestions indicate that these pacemaker cells might result in our biological clock [2].

Neuronal noise begins at the microscopic level with atomic collisions and agitation[3]. Neural noise on the postsynaptic membrane has been evident in the early processing of sight, smell, and hearing. The exact reason for the noise relating to computation processing isn’t quite understood, but there are many great theories. Initially, the thought of noise in complex computer circuit or neural circuits is thought to slow down [4] or negatively affect the processing power, but recent research has suggested that neuronal noise is beneficial to non-linear neural network to an optimal value [5]. One theory, by Anderson and colleagues, suggests that noise produced in the visual cortex helps linearize, or smoothing the threshold of action potentials [6]. Another theory suggests that stochastic noise in a non-linear network shows that there is a positive relationship between the interconnectivity and noise-like activity [7]. Some investigators have shown experimentally and in models that neuronal noise is a possible mechanism to facilitate neuronal processing.[8][9] The presence of neuronal noise (or more specifically synaptic noise) confers to neurons more sensitivity to a broader range of inputs, it can equalize the efficacy of synaptic inputs located at different positions on the neuron, and it can also enable finer temporal discrimination (see details in the "High-conductance state" article in Scholarpedia). There are many theories to why noise is apparent in the neuronal networks, but many neurologists are unclear why they exist.

Types of Noise

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  • Ions exist inside and outside of the neuron and are subject to many bodily conditions. One major source of background noise arising from ions or molecules in response to the third law of thermodynamics. The body temperature is above 0° C; therefore, the molecules have kinetic energy, or motion. Also the constant "leaking of ions across the membrane lead to small fluctuations in the membrane potential. But, most of these fluctuations in the membrane potential are smoothed out by membrane capacitance.
  • Synapses are another major source of neural noise. More than often, there is random exocytosis of vesicles containing neurotransmitters, which eventually bind to the postsynaptic membrane leading to a spontaneous action potential by graded potentials in the postsynaptic neuron[10] It is considered the largest-amplitude noise source in the cerebral cortex. [3]

Sources of Noise

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There exist several sources of response variability for neurons and neural networks:[11]

  • Thermal noise: Johnson–Nyquist noise occurs due to the thermal motions of ions and other charge carriers, producing voltage fluctuations proportional to temperature.
    • Third law of Thermodynamics-kinetic moles move with increasing heat
  • Ionic conductance noise: ion channels in the membrane undergo spontaneous changes in conformation between different states, and can open (or close) due to thermal fluctuations.
  • Ion pump noise: Membrane ion pumps produce fluctuating potentials. Sodium
  • Ion channel shot noise: The number of ions that migrate through an open ion channel is discrete and random. In synapses, the number of calcium ions that enters the postsynaptic side after a spike is on the order of 250 ions,[11] potentially making potentiation processes noisy.
  • Synaptic release noise: The number of vesicles released by a synapse is random, and further influenced by the firing history of the pre- and post-synaptic neuron. In addition vesicles are occasionally released without incoming spikes.
  • Synaptic bombardment: The large number of incoming spikes add a fluctuating amount of charge to the cell, which depends on the structure of the incoming spike trains and affects the cell's excitability.[12] Noise can be produced by on a larger scale due to fluctuations in CO2, which lead to variations in blood flow.[13]
  • Chaos: Chaotic dynamics can occur in single cells (due to periodic inputs or bursting due to intrinsic currents [14]). Simple networks of neurons can also exhibit chaotic dynamics.[15] Even if the chaos is deterministic, it can amplify noise from the other sources to macroscopic levels due to sensitive dependence on initial conditions.
  • Connectivity noise: Neurons have inhomogeneous and somewhat random connectivity, as well as individual variation in cellular parameters.

Recording Methods

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Global Recording

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  • EEG-
    • Desynchronized EEG

The external noise paradigm is used to test the presence or absence of neural noise. According to this paradigm, external noise should multiplicatively increase the amount of internal noise in the central nervous system. Researchers add visual or auditory external noise to a stimuli, and measure how it affects reaction time or the subject's performance. If performance is more inconsistent than without the noise, the subject has internal noise.


Local Recording

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  • Patch Clamps
  • Intercellular/extracellular recording of single neuron

Future Clinical Applications

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References

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  1. ^ Jacobson, G. A., et al. (2005). "Subthreshold voltage noise of rat neocortical pyramidal neurones." J Physiol 564(Pt 1): 145-160.
  2. ^ Mazzoni, E. O., et al. (2005). "Circadian Pacemaker Neurons Transmit and Modulate Visual Information to Control a Rapid Behavioral Response." Neuron 45(2): 293-300.
  3. ^ a b Destexhe, A. (2012). Neuronal noise. New York: Springer.
  4. ^ McDonnell, Mark D., and Lawrence M. Ward. "The Benefits Of Noise In Neural Systems: Bridging Theory And Experiment." Nature Reviews Neuroscience 12.7 (2011): 415-426. Academic Search Complete. Web. 19 Nov. 2012.
  5. ^ Parnas, B. R. (1996). "Noise and neuronal populations conspire to encode simple waveforms reliably." IEEE Trans Biomed Eng 43(3): 313-318.
  6. ^ Anderson, J. S., et al. (2000). "The contribution of noise to contrast invariance of orientation tuning in cat visual cortex." Science 290(5498): 1968-1972.
  7. ^ Serletis, D., et al. (2011). "Complexity in neuronal noise depends on network interconnectivity." Ann Biomed Eng 39(6): 1768-1778.
  8. ^ http://www.rochester.edu/news/show.php?id=2683
  9. ^ Ma, W.J., Beck, J., Latham, P. and Pouget, A., Bayesian inference with probabilistic population codes. Nature Neuroscience. 9(11), 1432-1438. 2006. http://www.bcs.rochester.edu/people/alex/pub/articles/MaBeckLathamPougetNN06.pdf
  10. ^ Fatt, P. and B. Katz (1952). "Spontaneous subthreshold activity at motor nerve endings." J Physiol 117(1): 109-128.
  11. ^ a b Christoph Koch, Biophysics of Computation. Oxford University Press, New York, 1999
  12. ^ N Ho and A Destexhe, Synaptic background activity enhances the responsiveness of neocortical pyramidal neurons. J. Neurophysiol. 84, 1488 (2000)
  13. ^ Birn, R. M. (2012). The role of physiological noise in resting-state functional connectivity. [Review]. Neuroimage, 62(2), 864-870. doi: 10.1016/j.neuroimage.2012.01.016
  14. ^ A Longtin, Autonomous stochastic resonance in bursting neurons. Phys. Rev. E. 55, 868 (1997)
  15. ^ Li, C., Yu, J., & Liao, X. (2001). Chaos in a three‐neuron hysteresis hopfield‐type neural networks. Physics Letters A, 285, 368-372.
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