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Reliability and Redundancy of Neurons: Can We Distinguish Probabilistic, Stochastic, and Noisy Processes?

  • Theodore Holmes Bullock

Summary

The answer to the title question is: with great difficulty in the actual operation of living neurons, although these three properties are distinct and only overlap in their manifestation. The common belief that neurons are generally unreliable in the sense of inconsistent response to the same stimulus is here shown to be a special case and not necessarily due to system noise. The prevalent probabilistic view of neuronal firing remains plausible belief and is virtually untestable. This view models the nervous system with high redundancy of noisy neurons. The arguments usually cited, such as inconsistent response, can not be taken as cogent evidence for it. Several grounds for this conclusion are developed.

Three issues appear in the literature, two of them really irrelevant: (a) arguments that probabilistic methods in data reduction are necessary, (b) assertions that models assuming stochastic noise are valuable, and (c) claims that the nervous system actually works with noisy elements by large scale redundancy. These distinct propositions are usually blurred into one. I take no exception to the first two; the third claim is the issue addressed here.

Of two kinds of uncertainty, Heisenbergian is moot at the neuronal level, and statistical mechanical ( = practical unpredictability), as in a gas, surely exists. The questions are empirical, where and how much it exists; it is already clear that the relative strength of stochastic processes such as arrival times of converging spike trains varies widely among neurons.

Discussion should start by calling unexplained fluctuation “unexplained fluctuation,” not noise in the dictionary sense of undesirable antisignal. Several known causes of stochastic fluctuations are compatible with a highly reliable system. Although varying widely, many neurons are probably low in true noise but they may show a high level of apparent noise from any of several causes. Jitter is demonstrably adaptive in certain situations.

Redundancy in the sense of fully equivalent neurons is probably not as large in scale as commonly thought even of the brains of mammals. Chapter 7 proposes that few classes of fully equivalent neurons exceed 300–500. Overlap of input or output fields is much more general; neurons are commonly partially redundant, partially not. The value of overlap may not be so much for averaging internal noise as for signalling input and output by convergence that achieves sensitive, smooth, high resolution. To the extent that noise cancellation is accomplished, it is likely to depend not merely upon linear averaging or summing but to employ any of several nonlinear processes more robustly noise resistant. We do not assume indeterminate operation, absent good evidence. “Probabilistic” applies appropriately to the methods used for study but not to the actual operation of such a system, at least in a major degree, until adequate study has excluded deterministic or useful fluctuation. Reliability (consistent firing) is demonstrably high in many neurons, implying precision in connections and in dynamic properties. Evidence of this continuously increases, in contrast to evidence that true system noise explains observed fluctuation.

Keywords

Receptive Field Spike Train Optic Lobe Electric Organ Discharge Musca Domestica 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1993

Authors and Affiliations

  • Theodore Holmes Bullock
    • 1
  1. 1.Department of Neurosciences 0201University of California, San DiegoLa JollaUSA

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