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The reliability of neurons.

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TLDR
Although unreliability is difficult to establish, to say nothing of measure, evidence that some neurons have a high degree of reliability, in both connections and activity is increasing greatly, and it would contribute to discussion to recognize that neurons perform with adegree of reliability.
Abstract
The prevalent probabilistic view is virtually untestable; it remains a plausible belief. The cases usually cited can not be taken as evidence for it. Several grounds for this conclusion are developed. Three issues are distinguished in an attempt to clarify a murky debate: (a) the utility of probabilistic methods in data reduction, (b) the value of models that assume indeterminacy, and (c) the validity of the inference that the nervous system is largely indeterministic at the neuronal level. No exception is taken to the first two; the second is a private heuristic question. The third is the issue to which the assertion in the first two sentences is addressed. Of the two kinds of uncertainty, statistical mechanical (= practical unpredictability) as in a gas, and Heisenbergian indeterminancy, the first certainly exists, the second is moot at the neuronal level. It would contribute to discussion to recognize that neurons perform with a degree of reliability. Although unreliability is difficult to establish, to say nothing of measure, evidence that some neurons have a high degree of reliability, in both connections and activity is increasing greatly. An example is given from sternarchine electric fish.

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Journal ArticleDOI

Reliability of spike timing in neocortical neurons

TL;DR: Data suggest a low intrinsic noise level in spike generation, which could allow cortical neurons to accurately transform synaptic input into spike sequences, supporting a possible role for spike timing in the processing of cortical information by the neocortex.
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Synaptic Modification by Correlated Activity: Hebb's Postulate Revisited

TL;DR: Spike timing-dependent modifications, together with selective spread of synaptic changes, provide a set of cellular mechanisms that are likely to be important for the development and functioning of neural networks.
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Reproducibility and Variability in Neural Spike Trains

TL;DR: Variability and reproducibility are quantified with ideas from information theory, and measured spike sequences in H1 carry more than twice the amount of information they would if they followed the variance-mean relation seen with constant inputs.
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The structure and precision of retinal spike trains

TL;DR: The reproducibility of retinal responses to repeated visual stimuli, in both tiger salamander and rabbit, is measured to show that the timing of a firing event conveyed several times more visual information than its spike count.
Journal ArticleDOI

Cellular mechanisms contributing to response variability of cortical neurons in vivo.

TL;DR: In this paper, the authors measured the membrane potential (V m) fluctuations and spike activity during brief epochs immediately before and after the onset of an evoked response and found that the response magnitude, expressed as a change in V m relative to baseline, was linearly correlated with the preceding spontaneous V m.
References
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Journal ArticleDOI

Receptive fields, binocular interaction and functional architecture in the cat's visual cortex

TL;DR: This method is used to examine receptive fields of a more complex type and to make additional observations on binocular interaction and this approach is necessary in order to understand the behaviour of individual cells, but it fails to deal with the problem of the relationship of one cell to its neighbours.
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Chemoaffinity in the Orderly Growth of Nerve Fiber Patterns and Connections

TL;DR: The hypothesis, 18-24 in brief, suggested that the patterning of synaptic connections in the nerve centers must be handled instead by the growth mechanism directly, independently of function, and with very strict selectivity governing synaptic formation from the beginning.
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