Abstract
HIGHLIGHTS We suggest classifying variability of neuronal responses as follows: false (associated with a lack of knowledge about the influential factors), “genuine harmful” (noise), “genuine neutral” (synonyms, repeats), and “genuine useful” (the basis of neuroplasticity and learning).The genuine neutral variability is considered in terms of the phenomenon of degeneracy.Of particular importance is the genuine useful variability that is considered as a potential basis for neuroplasticity and learning. This type of variability is considered in terms of the neural Darwinism theory.In many cases, neural signals detected under the same external experimental conditions significantly change from trial to trial. The variability phenomenon, which complicates extraction of reproducible results and is ignored in many studies by averaging, has attracted attention of researchers in recent years. In this paper, we classify possible types of variability based on its functional significance and describe features of each type. We describe the key adaptive significance of variability at the neural network level and the degeneracy phenomenon that may be important for learning processes in connection with the principle of neuronal group selection.
Highlights
VARIABILITY OF NEURONAL RESPONSESA fundamental problem in neuroscience is decoding of information contained in the structure and functional activity of the nervous system
In many cases, neural signals detected under the same external experimental conditions significantly change from trial to trial
We suggest classifying variability of neuronal responses as follows: false, “genuine harmful”, “genuine neutral”, and “genuine useful”
Summary
A fundamental problem in neuroscience is decoding of information contained in the structure and functional activity of the nervous system. The spike frequency becomes irregular (variable) when activity of a similar neuron is detected in vivo, including cases where identical stimuli are presented (Masquelier, 2013). Upon stimulation of the same cortical area with the same intensity using skin myographic electrodes, a MEP of a varying amplitude and latency is detected. Studies have shown that variability of the MEP amplitude in TMS is affected by a number of factors, including the position of a skin electrode, muscle topography (Dunnewold et al, 1998), high stimulation intensity (Pitcher et al, 2003), voluntary muscle contraction, readiness for contraction (Darling et al, 2006), gender of a subject (Pitcher et al, 2003), and presenile age (Pitcher et al, 2003). “Two-third of the MEP size variability is caused by the variable number of recruited α-motoneurons and approximately one-third by changing synchronization of motoneuron discharges” (Rösler et al, 2008)
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