Direction-Selective Circuits Shape Noise to Ensure a Precise Population Code
Authors: Fred Rieke
Joel Zylberberg
Maxwell H Turner
Eric Shea-Brown
Jon Cafaro
Journal: Neuron (2016)
Discipline: NEUROSCIENCE & BEHAVIOR
Keywords: Neural responses; noise correlations; stimulus dependence; ON-OFF direction-selective retinal ganglion cells; circuit mechanisms
Affiliations: Department of Applied Mathematics, University of Washington, Seattle, Washington 98195, USA., Department of Physiology and Biophysics, University of Washington, Seattle, Washington 98195, USA., Department of Neurobiology, Duke University, Durham, North Carolina 27708, USA., Howard Hughes Medical Institute, University of Washington, Seattle, Washington 98195, USA.

Abstract:  Neural responses are noisy, and circuit structure can correlate this noise across neurons. Theoretical studies show that noise correlations can have diverse effects on population coding, but these studies rarely explore stimulus dependence of noise correlations. Here, we show that noise correlations in responses of ON-OFF direction-selective retinal ganglion cells are strongly stimulus dependent, and we uncover the circuit mechanisms producing this stimulus dependence. A population model based on these mechanistic studies shows that stimulus-dependent noise correlations improve the encoding of motion direction 2-fold compared to independent noise. This work demonstrates a mechanism by which a neural circuit effectively shapes its signal and noise in concert, minimizing corruption of signal by noise. Finally, we generalize our findings beyond direction coding in the retina and show that stimulus-dependent correlations will generally enhance information coding in populations of diversely tuned neurons. 

Retracted: No

PubPeer comments:
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 This is another one for the u201c[p-hacking-in-principle of neural-tuning-claims file.] u201d This type of method and its results have, of course, no standing in science. The method immunizes assumptions u2013 no matter how uninformed and absurd u2013 from falsification. The paper is nominally about u201cdirection-selective retinal ganglion cells.u201d This term requires a lot of unpacking u2013 its implications are impossibilities u2013 but I ll get to that later. First I want to point out why I say this paper s method constitutes p-hacking. For that we go to Methods u2013 literally, the fine print. And then to the even finer print, the supplemental information. From the main Methods section: u201cTo probe direction selectivity of ooDS cell pairs (Figures1,3,4, and5), stimuli were centered midway between the two somas andu0018 100% contrast bars were swept through their receptive fields...u201d So, the authors record from neurons in the animal s brain while some event is taking place outside (moving bars). They assume that the firing rates of the neuron being recorded from are causally-linked to u201cmoving bar direction,u201d via an increase in (average plus some ad hoc adjustments) firing rate, and thus that fishing for correlations between firing rates and some feature or description of conditions will produce correlations with causal potency u2013 that the correlation between highish firing rates and u201cbar-moving-in-some-particular-directionu201d at the time of recording indicates that the neuron is u201cselectiveu201d for that u201cdirection;u201d that it thus u201csignalsu201d this condition; that in the absence of this condition it would fire less; that variation in firing rates is simply u201cnoise;u201d that, further, such a hypothetical mechanism is capable of explaining all that we see in general. As Mehler Kording (2018) have pointed out with respect to neuroscience (and as philosophers and scientists have known since time-immemorial), massive confounding, internal and external, means that such post hoc correlational tuning claims are worthless. Note also how risk-free the procedure is; regardless of the distal situation, some neurons will always be firing more at the time of presentation of the stimulus, some less. Ad hoc post hoc labelling of the highest flyers as u201cdirection-selectiveu201d is child s play. They could just as validly have used kitties and labelled neurons  kitty-detectors. What is also striking (though unfortunately not surprising for this literature) are the citations offered as the basis for the direction-tuning claim. Because if the claim were well-grounded, the authors could claim that their procedures were not confounded, that we already knew that neural firing rates conform to all of the assumptions being made, that some neurons really do only get excited when a bar is moving left, etc: u201cThere are four subtypes of ooDS cells based on direction tuning, each of which responds preferentially to motion in one of the cardinal directions (Figure 1A; Barlow and Levick, 1965; Oyster and Barlow, 1967).u201d So the functional properties of retinal ganglion cells were fully and unambiguously elucidated in work on rabbits performed 50 years ago, and these conclusions have not been refined or elaborated since. That s impressive. Here s the type of technique Barlow was using: u201cFor the two-spot experiment we initially used two modulator tubes controlled by pulse generators, but we later found that black and white cards moved behind apertures in grey paper provided a more flexible means of delivering the required stimuli. This method also has disadvantages...which we over came by illuminating the apertures from behind with thin Perspex light pipes lit by low-current torch bulbs turned on and off manually.u201d It also sounds like p-hacking to me: u201cWe graded the unit s response from 0 to 6 after listening to several repetitions of these stimuli on the loudspeaker.u201d You d think with all the hot new tech constantly being developed, these stone-age findings might have been revisited and refined...It could be that, as in the case of e.g. Hubel Wiesel (1968), references to simple, early accounts (made even simpler in their later descriptions) are preferred to later, more complicated, ambiguous, confused attempts to build on them, so authors prefer the former. (Read Carandini et al (2005) to see how well early assumptions have fared. I blogged about it [here] ). Again, post hoc methodology doesn t punish false assumptions, so authors are free to choose whatever makes their story seem cleaner and less difficult to sell. The authors make additional, equally unfounded, subsidiary assumptions: u201cTo compute the information content of the model ooDS cell populations (Figure 6) and the larger model populations (Figures 7 and 8), we used the linear Fisher information. This is a fairly standard way to quantify neural population codes (Averbeck, Latham, and Pouget, 2006; Hu et al., 2014; Beck et al.,2011), and it determines the precision with which the neural activities can be decoded to recover the stimulus. Mathematically, the Fisher information uses the vector of tuning curves (where, for a given stimulus angle q, each element of the vector is one neuronu2019s mean firing rate), and the covariance matrix S(q) of the trial-to-trial variability in response to stimulus q.u201d So, u201clinear Fisher informationu201d is a valid way of analyzing neural data because it is...u201cfairly standardu201d ? What does this even mean? (This must be that u201cleaning over backwardu201d that Feynman talked about). u201cFor the data shown in Figure 7, we generated the neural tuning curves via Von Mises functions (as in Ecker et al., 2011; Hu et al., 2014) and assumed Poisson variability (vector of spike count variance equal to vector of mean spike counts given by tuning curves f(q)). The correlation coefficient between cells i and j was assumed to follow the functional form...u201d Are assumptions of normality (von Mises) valid because...Ecker et al? Are assumptions of Poisson variability valid? Is assuming u201cthe functional form...u201d valid? It don t matter when you re post hoc ing it! There s also the little matter of the u201creceptive fieldsu201d mentioned in the first quote. The receptive field concept is highly problematic, and theoretically undefined. Even Kuffler knew this: u201cu2026 not only the areas from which responses can actually be set up by retinal illumination may be included in a definition of the receptive field but also all areas which show a functional connection, by an inhibitory or excitatory effect on a ganglion cell. This may well involve areas which are somewhat remote from a ganglion cell and by themselves do not set up discharges.u201d (From Spillmann et al 2016) JOV). What this means, in effect, and what was understood well before the first retinal ganglion cell was recorded from, is that in perception (and its neural correlates) a description of u201cthe stimulusu201d involves the entire area of stimulation, not just the local area; the firing rate of neuron u201cxu201d will be a function of what was going on here and also what was going on there and there and there, (as well as what went on before) in ways we aren t close to understanding. The simple, local view of u201creceptive field,u201d apparently popularized by Hubel and Wiesel, has been semi-retired with the honorary title of u201cclassical receptive field,u201d in favor of the alternative u201cnon-classical receptive field.u201d Neither has any meaning. All this raises the question of what do Zylberberg et al mean by u201creceptive fieldu201d? How did they measure these hypothetical bounded areas? I don t know u2013 this info isn t provided either in the main text nor in the supplemental info. I ve emailed the lead author to inquire, and will report his response as soon as I receive it. In the meantime, the supplemental text (which I consulted in the vain attempt to answer the receptive field question) is even more revealing of the ad hoc post hoc nature of this article s claims: **u201cDirection selectivity was quantified as follows. For each stimulus angle u03b8, we defined the two-dimensional direction vector d(u03b8) = (cos(u03b8),sin(u03b8)). We then multiplied these direction vectors by the mean firing rate induced in the by that stimulus, to get the vector v(u03b8) = r(u03b8)d(u03b8). The length of this vector is the cellu2019s mean firing rate (for this particular stimulus), and the direction indicates the direction of stimulus motion. We averaged these vectors over stimuli and computed the norm of the result. If the cell fired equally to stimuli in all directions, the horizontal and vertical components of the v(u03b8) vectors would cancel, leading to the average vector having small magnitude. Finally, we scaled the mean vector by the cellu2019s overall average firing rate (averaged over all stimuli). This yields a number between 0 (cell responds equally to stimuli in all directions) and 1 (cell responds only to motion in 1 direction), that we called the Direction Selectivity Index (DSI).u201d** **It should be obvious that the DSI is an arbitrary, artificial number. If the authors had performed statistical tests we could describe it as p-hacking. Their conclusions sans stats are no less arbitrary.** The authors understand neural functioning so well that they can even correct the neurons activities, improving their performance, if you will: u201cOver the course of paired recordings, both spike and conductance recordings are subject to slow, long-term drift that, if left uncorrected, can introduce artifactual noise correlation measurements. To correct for long term drift in cell attached recordings (Fig. 1), we subtracted the window (radius 2 trials) averaged spike count from each trial. By inspection of spike counts across trials and the trial autocorrelation function [Bair et al., 2001], this window-correction method effectively removed long-term drift from reported data.u201d Is there any reason to trust the authors judgment about what aspects of neural activity are functionally relevant or not? Relatedly, from Kuhn and Gollisch (2019):  Yet, for most neurons that are considered as feature-selective, activity can likely also be elicited or modulated by other stimuli. **Direction-selective retinal ganglion cells, for example, respond vigorously to increases or decreases in luminance inside their receptive fields, even without a motion component (Levick, 1967).** Moreover, complex sensory stimuli, such as natural scenes, generally contain multiple stimulus features that vary dynamically in their strength. Under such circumstances, the response of a feature-selective neuron may be ambiguous as to whether it represents the occurrence of the particular sensory feature that is typically used to characterize it. The authors also fit data to linear functions. Why? In response, I will quote Konrad Kording, [speaking] ) in a Waterloo Brain Day lecture: u201cwe use techniques that have been developed for linear systems and we just use them because we can download the code and it s easy.u201d ,