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As an applied mathematician, I have a growing interest in the mechanisms for uncertainty representation and computation in the human brain. In fact, I recently compiled a list of papers on this subject. But, so far I haven't found many papers which answer a fundamental sub-question:

What sources of randomness does the brain use for sampling?

Dan Goodman, a neuroscientist, suggested that I have a look into neuronal noise. The neuronal variability perspective is quite popular among computational neuroscientists and 'Bayesian inference with probabilistic population codes'(2006) is an example of a publication which demonstrates its use:

At first sight, it would seem that the high variability in the responses of cortical neurons would make it difficult to implement such optimal statistical inference in cortical circuits. We argue that, in fact, this variability implies that populations of neurons automatically represent probability distributions over the stimulus, a type of code we call probabilistic population codes. Moreover, we demonstrate that the Poissonlike variability observed in cortex reduces a broad class of Bayesian inference to simple linear combinations of populations of neural activity. These results hold for arbitrary probability distributions over the stimulus, for tuning curves of arbitrary shape and for realistic neuronal variability.

However, a recent paper published in 2018 [3] presents compelling counter-arguments against the utility of neuronal noise for sampling in the brain.

In particular, the authors make the following case in their abstract:

Since the precise statistical properties of neural activity are important in this context, many models assume an ad-hoc source of well-behaved, explicit noise, either on the input or on the output side of single neuron dynamics, most often assuming an independent Poisson process in either case. However, these assumptions are somewhat problematic: neighboring neurons tend to share receptive fields, rendering both their input and their output correlated; at the same time, neurons are known to behave largely deterministically, as a function of their membrane potential and conductance.

While I find the arguments in [3] compelling due to their fundamental nature, this makes me wonder whether there are other fundamental contributions to this problem that I ignore.

Note: Cian O'Donnell recently shared his PhD thesis with me on Twitter [4]. The neural noise vs. no neural noise variant of this question is still very much an open problem.

References:

  1. Wei Ji Ma, J. Beck, P. Latham & A. Pouget. Bayesian inference with probabilistic population codes. Nature Neuroscience. 2006.
  2. Andre Longtin. Neuronal noise. Scholarpedia. 2013.
  3. D. Dold et al. Stochasticity from function - why the Bayesian brain may need no noise. Arxiv. 2018.
  4. R. Cannon , C. O'Donnell , M. Nolan . Stochastic Ion Channel Gating in Dendritic Neurons: Morphology Dependence and Probabilistic Synaptic Activation of Dendritic Spikes. PLOS. 2010.
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    $\begingroup$ I kind of think the noise vs no noise people are talking past each other sometimes, though that's more of an opinion than something I can cite. Basically there is enough "other stuff" going on in the brain at any one time, we aren't input output machines the way experiments presume us to be (nor are mice or flies for that matter), so everything else going on at any point in time is "noise." $\endgroup$ – Bryan Krause May 14 at 14:48
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Not sure if I am getting your question right. You are asking for noise that has an inherently information processing function? I would add an other aspect of noises

Noise as an emerging property

  1. On a large scale, when massive amounts of neurons are synchronising, then you can detect Noise with Electrodes, like in the case of EEG. EEG follows an 1/f² or 1/f³ law (brownian noise, red noise, pink noise, depending on the literature). This principle can be found anywhere in the world. It states that higher frequencies / faster oscillating signals have increasingly smaller amplitudes, similar to smaller and larger waves on the sea and so on. In this case is noise a byproduct of other (stochastic) processes.

  2. On a small scale, which would probably be something you would measure by using 'clamping'-technique on single neurons. The firing rate of a neuron (a neuron chemically charging, until it reaches a threshold, which turns into a all-or-nothing case) also follows some 'systematic randomness' on which probabilistic, stochastic and noisy properties might be useful to describe these processes. In this case is noise a property that emerges from natural stochastic single-neuron firing processes.

Noise emerging from signals related to certain anatomic sources

  1. In how far I remember (I do not have a good resource on that) the hippocampus, which is highly relevant for memories, has a strong randomization aspect inherently in its functioning. This might be my closest guess to your question?!
  2. Another very distinct 'stochastic' process created by the Thalamus, a largely dominant, pace-giving rhythm within the brain.

Ressource: Buszaki Rhythms of the Brain is a nice read on various of these topics

On noise as an error process

Maybe I got your question a little wrong, but thinking of the brain that in the run of evolution turned out to be highly efficient and energy saving, does not want to have any noise that does not fulfill any purpose. (But as I understand, your question was related to the functional aspect of noise.)

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