Construcción social de la tecnología (SCOT)

It is a curious fact that neurons, which are highly reliable when studied in isolation in a Petri dish (Mainen & Sejnowski, 1995), become erratic in their responses when embedded in neural circuits (Schiller et al., 1976; Dean, 1981). Given the same repeated stimulus, a neuron in vitro will generate nearly identical spike trains, while its in vivo counterpart will produce different responses each time. This variability, or “noise”, presents a challenge to both experimenters wishing to map neural response properties, and to the brain, which has to encode information in these responses. These challenges are closely related, as are their solutions. In both cases, there is a particular part of the response that is of interest, which is often called the “signal”. This signal is the portion of the neural response which depends deterministically on external input, and which therefore carries information about this input. For low-level visual neurons, this deterministic coupling is typically characterized by a tuning curve, which relates variations along basic stimulus dimensions (such as contrast or orientation) to changes in a neuron’s average response.

Noise obscures the signal, limiting the information that a neural response can convey. From a neural coding perspective, when noise is independent between neurons, this information loss is ameliorated by redundancy in the neural code, allowing noise to be “averaged out” across neurons that are tuned to the same stimulus property. Experimenters deal with noise in a similar way, by averaging responses of the same neuron to repeated presentations of the same stimulus. When noise is shared between neurons, however, this strategy breaks down, as correlated noise cannot be averaged out. Moreover, if noise is shared preferentially between neurons with similar tuning properties, then the noise that remains after averaging may be indistinguishable from a tuning response, causing ambiguity in the neural code (an effect illustrated in Fig.

2.1; see also Abbott & Dayan, 1999; Averbeck & Lee, 2006; Moreno-Bote et al., 2014;

Shadlen & Newsome, 1998; Zohary, Shadlen, & Newsome, 1994). In order to develop a comprehensive model of cortical information processing, it is thus essential to understand the influence of noise correlations. This study therefore investigates the correlational structure of noise in human visual cortex.

Response Pref. stim. Pr ef . stim. 0noiser -1 1 Pref. stim. Pr ef . stim.

Independent noise Correlated noise

Preferred stimulus Preferred stimulus

Figure 2.1: Illustration of the effects of independent and (tuning-dependent) correlated noise on neural coding. Shown are the responses of a population of neurons to a constant stimulus, the orientation of which is indicated by the orange dotted line. Neurons are sorted along the x-axis based on their preferred stimulus. Orange solid curves indicate the average response of each neuron (determined by its tuning preference) to the presented stimulus. Green dots are the responses of the neurons on a single trial. Insets depict the matrix of noise correlations ( ) between neurons, as a function of their preferred stimulus. In the left panel, noise is independent, and thus the noise correlation matrix is diagonal. Noise goes in all directions regardless of neural tuning preference, and thus the central tendency of the population’s response (the population resultant vector, green dotted lines) is not pulled in any particular direction in stimulus space. In the right panel, noise is positively correlated between neurons with similar tuning preferences, and anti-correlated between neurons that prefer very different (orthogonal) stimuli. The noise therefore contains structure that is related to the preferred stimulus, which shifts the central tendency of the population’s response in the stimulus dimension. Thus, even though the overall amount of noise is similar between the two panels, tuning-dependent correlated noise is much more harmful to the amount of stimulus information contained in the population’s response.

In macaque visual cortex, noise correlations are known to be related to the distance between neurons, and, interestingly, to the similarity between their tuning curves (Zohary et al., 1994; Bair, Zohary, & Newsome, 2001; Kohn & Smith, 2005; Smith & Kohn, 2008). Do noise correlations in human visual cortex, which we currently know little about, show a similar pattern? To address this question, we measured responses in human visual areas to simple oriented stimuli, using functional magnetic resonance imaging (fMRI). We then related the degree of shared noise in these responses to the distance between voxels within the brain, as well as their orientation tuning properties. Crucially, these tuning properties have to be estimated from the same measured responses that we suspect may contain correlated noise. We reasoned that if voxels incur shared noise, which affects the similarity of their responses, this might also influence the estimated similarity of their tuning properties. Could standard analyses therefore be biased to reveal links between tuning similarity and noise correlations that

THE STRUC TURE OF CORRELATED RESPONSE VARIABILIT Y IN HUMAN VISUAL COR TEX

do not actually exist? Through mathematical derivations, verified on simulated data, we show that standard estimation methods are indeed subject to such a bias, which has hitherto been overlooked. Next, we outline a proposed solution to this bias, which involves cross-correlating tuning curves estimated on independent data partitions. By simulating fMRI data for which the true voxel tuning preferences are known, we confirm that this solution avoids the bias in estimated tuning similarity.

Equipped with this unbiased method, we then return to our initial question regarding noise correlations in human visual cortex. Interestingly, we find that both our unbiased and the standard methodology indicate that noise correlations depend on both distance and tuning similarity. In contrast, the standard, biased approach appears to greatly overestimate the dependence on tuning. Our results provide important information on the structure of noise correlations in human visual cortex, while simultaneously presenting a cautionary tale regarding the estimation of neural tuning properties on data with shared noise.