Predicting Perceptual Decisions Using Visual Cortical Population Responses and Choice History

Jasper, Anna I.; Tanabe, Seiji; Kohn, Adam

doi:10.1523/jneurosci.0035-19.2019

Cited by 29 publications

(29 citation statements)

References 76 publications

(131 reference statements)

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“…The fact that we can weakly predict false alarms from visual cortical activity (Fig. 6f,~52%, significantly greater than the chance level of 50%, p < 0.05 for both monkeys, one sample t-test) is consistent with previous work that reported choice probabilities (~55%) for neurons in MT, V1, V2, and V4 [72,22,41]. However, the observed decoding accuracies are not directly comparable to previously-reported choice probabilities because most previous work computes choice probabilities for a single neuron (here, we use a population of neurons), considers neural activity taken over large time windows (e.g., 1 second compared to 175 ms used here), and uses two-alternative forced choice tasks rather than our sequential task that required long periods of fixation.…”

Section: Models Of Perceptual Decision-makingsupporting

confidence: 91%

Slow drift of neural activity as a signature of impulsivity in macaque visual and prefrontal cortex

Cowley

Snyder

Acar

et al. 2020

Preprint

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An animal's decision depends not only on incoming sensory evidence but also on its fluctuating internal state. This internal state is a product of cognitive factors, such as fatigue, motivation, and arousal, but it is unclear how these factors influence the neural processes that encode the sensory stimulus and form a decision. We discovered that, over the timescale of tens of minutes during a perceptual decision-making task, animals slowly shifted their likelihood of reporting stimulus changes. They did this unprompted by task conditions. We recorded neural population activity from visual area V4 as well as prefrontal cortex, and found that the activity of both areas slowly drifted together with the behavioral fluctuations. We reasoned that such slow fluctuations in behavior could either be due to slow changes in how the sensory stimulus is processed or due to a process that acts independently of sensory processing. By analyzing the recorded activity in conjunction with models of perceptual decision-making, we found evidence for the slow drift in neural activity acting as an impulsivity signal, overriding sensory evidence to dictate the final decision. Overall, this work uncovers an internal state embedded in the population activity across multiple brain areas, hidden from typical trial-averaged analyses and revealed only when considering the passage of time within each experimental session. Knowledge of this cognitive factor was critical in elucidating how sensory signals and the internal state together contribute to the decision-making process.

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Section: Models Of Perceptual Decision-makingsupporting

confidence: 91%

Slow drift of neural activity as a signature of impulsivity in macaque visual and prefrontal cortex

Cowley

Snyder

Acar

et al. 2020

Preprint

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“…This mismatch might arise from shorter stimulus presentation, not tailoring the stimuli to match the neuron's tuning (as done in Britten et al (1992)), recording from lower-level visual areas (V1 vs. V4 or MT) with smaller receptive fields, as well as increased recording noise with calcium imaging as compared to electrophysiological recordings. These lower discrimination thresholds predict increasingly small choice correlations, in line with recent reports from area V1 of monkeys, where fewer than 7% of V1 neurons were found to feature significant choice correlations (Jasper, Tanabe, & Kohn, 2019). In general, the estimated asymptotic information predicted direction discrimination thresholds compatible with previous behavioral reports in mice (Abdolrahmani et al, 2019;Glickfeld et al, 2013), but the use of different stimuli in these experiments precludes a direct quantitative comparison.…”

Section: Discussionsupporting

confidence: 88%

Scaling of information in large neural populations reveals signatures of information-limiting correlations

Kafashan

Jaffe

Chettih

et al. 2020

Preprint

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How is information distributed across large neuronal populations within a given brain area? One possibility is that information is distributed roughly evenly across neurons, so that total information scales linearly with the number of recorded neurons. Alternatively, the neural code might be highly redundant, meaning that total information saturates. Here we investigated how information about the direction of a moving visual stimulus is distributed across hundreds of simultaneously recorded neurons in mouse primary visual cortex (V1). We found that information scales sublinearly, due to the presence of correlated noise in these populations. Using recent theoretical advances, we compartmentalized noise correlations into informationlimiting and nonlimiting components, and then extrapolated to predict how information grows when neural populations are even larger. We predict that tens of thousands of neurons are required to encode 95% of the information about visual stimulus direction, a number much smaller than the number of neurons in V1. Overall, these findings suggest that the brain uses a widely distributed, but nonetheless redundant code that supports recovering most information from smaller subpopulations. Figure 1. Information scaling in large neural populations, and the impact of noise correlations on information.a. The information that a population of neurons can encode about some stimulus value is always a non-decreasing function of the population size. Information might on average increase with every added neuron (unbounded scaling; red) if the information is evenly distributed across all neurons. In contrast, information can rapidly saturate if information is redundant, and thus it is not strictly limited by population size, but by other factors. In general, it has only been possible to record from a very small subset of neurons of a particular area (grey shaded), from which it is hard to tell the difference between the two scenarios if the sampled population size is too small. b. The encoded information is modulated by noise correlations. This is illustrated using two neurons with different tunings to the stimulus value (top). The amount of information to discriminate between two stimulus values ( " /red and # /blue) depends on the difference in mean population activity (crosses) between stimuli, and the noise correlations (shaded ellipsoids) for either stimulus (bottom, showing joint neural activity of both neurons). The information is largest when the noise is smallest in the direction of the mean population activity difference (black arrow), which leads to the largest separation across the optimal discrimination boundary (grey line). In this example, positive correlations boost information (middle), whereas negative correlations lower it (right), when compared to uncorrelated neurons (left). In general, the impact of noise correlations depends on how they interact with the population's tuning curves.

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“…Our work instead indicates that the shape of the CP(p CR ) patterns cannot be summarized in the average, and this shape may be more informative about the role of the activity-choice covariations, when comparing across cells with different tuning properties, cells from different areas, or across task variations (e.g. Romo and Salinas, 2003;Nienborg and Cumming, 2006;Nienborg et al, 2012;Krug et al, 2016;Sanayei et al, 2018;Shushruth et al, 2018;Jasper et al, 2019;Steinmetz et al, 2019). Our new methods allow individuating and quantifying these CP patterns and hence a better characterization of the covariations between neural activity and choice across neural populations.…”

Section: Discussionmentioning

confidence: 89%

Stimulus dependent relationships between behavioral choice and sensory neural responses

Chicharro

Panzeri

Haefner

2019

Preprint

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Understanding the relationship between trial-to-trial variability in neural responses of sensory areas and behavioral choices is fundamental to elucidate the mechanisms of perceptual decision-making. In two-choice tasks, activity-choice covariations have traditionally been quantified with choice probabilities (CP). It has been so far commonly assumed that choice-related neural signals are separable from stimulus-driven responses, which has led to characterizing activity-choice covariations only with a single CP value estimated combining trials from all stimulus levels. In this work we provide theoretical and experimental evidence for the stimulus dependence of the relationship between neural responses and behavioral choices. We derived a general analytical CP expression for this dependency under the general assumption that a decision threshold converts an internal stimulus estimate into a binary choice. This expression predicts a stereotyped thresholdinduced CP modulation by the stimulus information content. We reanalyzed data from and found evidence of this modulation in the responses of macaque MT cells during a random dot discrimination task. Moreover, we developed new methods of analysis that allowed us to further identify a richer structure of cell-specific CP stimulus dependencies. Finally, we capitalised on this progress to develop new generalized linear models (GLMs) with stimulus-choice interaction terms, which show a higher predictive power and lead to a more precise assessment of how much each neuron is stimulus-or choice-driven, hence allowing a more accurate comparison across areas or cell types. Our work suggests that characterizing the patterns of stimulus dependence of choice-related signals is essential to properly determine how neurons in different areas contribute to linking sensory representations to perceptual decisions.

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Predicting Perceptual Decisions Using Visual Cortical Population Responses and Choice History

Cited by 29 publications

References 76 publications

Slow drift of neural activity as a signature of impulsivity in macaque visual and prefrontal cortex

Slow drift of neural activity as a signature of impulsivity in macaque visual and prefrontal cortex

Scaling of information in large neural populations reveals signatures of information-limiting correlations

Stimulus dependent relationships between behavioral choice and sensory neural responses

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