How Can Single Sensory Neurons Predict Behavior?

Pitkow, Xaq; Liu, Sheng; Angelaki, Dora E.; DeAngelis, Gregory C.; Pouget, Alexandre

doi:10.1016/j.neuron.2015.06.033

Cited by 136 publications

(250 citation statements)

References 40 publications

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“…When correlated noise is present, as is the case in the VN/CN (13,35), population thresholds may not decrease with the square root of the number of neurons and predictions based on the square root law could be dramatically inaccurate. Moreover, for a fixed neural pool size, population thresholds can also depend substantially on whether the decoder has full knowledge of the structure of noise correlations or not (42,45,46). Some noise correlations can be removed by an optimal decoder, but there is no guarantee that the brain is optimal and accounts for all correlations that can be removed.…”

Section: Discussionmentioning

confidence: 99%

“…These contemporary theoretical studies have important implications for our findings. Specifically, the fact that some sensory fibers are nearly as sensitive as behavior would imply either information-limiting correlations or massively suboptimal decoding to account for behavior (42,45,46). We consider it more likely that the total information encoded by the otolith afferent population is constrained by information-limiting correlations (40), in which case it is inappropriate to use the square root law to predict behavioral thresholds as previous studies have done (26,37).…”

Section: Discussionmentioning

confidence: 99%

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Neuronal thresholds and choice-related activity of otolith afferent fibers during heading perception

Dickman

DeAngelis

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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How activity of sensory neurons leads to perceptual decisions remains a challenge to understand. Correlations between choices and single neuron firing rates have been found early in vestibular processing, in the brainstem and cerebellum. To investigate the origins of choice-related activity, we have recorded from otolith afferent fibers while animals performed a fine heading discrimination task. We find that afferent fibers have similar discrimination thresholds as central cells, and the most sensitive fibers have thresholds that are only twofold or threefold greater than perceptual thresholds. Unlike brainstem and cerebellar nuclei neurons, spike counts from afferent fibers do not exhibit trial-by-trial correlations with perceptual decisions. This finding may reflect the fact that otolith afferent responses are poorly suited for driving heading perception because they fail to discriminate self-motion from changes in orientation relative to gravity. Alternatively, if choice probabilities reflect top-down inference signals, they are not relayed to the vestibular periphery.neuronal threshold | choice probability | psychophysical threshold | otolith afferent | heading discrimination T he neural basis of perception holds a long-standing fascination for neuroscientists. How do the properties of single neurons and populations of neurons relate to, and account for, sensory perception? In all sensory systems, information about the world is first translated into neural activity by peripheral receptor neurons, and then transformed by multiple stages of subcortical and cortical processing into a perceptual decision. How and where does perception emerge from multiple neural representations that appear to be at least partially redundant? These questions have been addressed often in sensory and multisensory cortex (e.g., in refs. 1-3 for vestibular perception), but much less is known about how the activity of sensory afferents relates to perceptual sensitivity and perceptual decisions.One way to assess a potential role of sensory neurons in a perceptual task is to compare neuronal and perceptual sensitivity, measured simultaneously in the same subject (4). Although this comparison has been done many times for cortical neurons (1, 5-7), little is known about how the sensitivity of peripheral afferents compares with behavior apart from microneurography studies of tactile afferents in humans (8, 9). To our knowledge, the present study provides the first direct comparison of afferent neuronal sensitivity and perceptual sensitivity, measured simultaneously in experimental animals. Results of such comparisons have important implications for understanding how population encoding and decoding may constrain and shape the information that guides behavior.Another way that neuroscientists have explored the functional links between sensory neurons and perception is by measuring the trial-by-trial correlations between neural activity and perceptual decisions, which are typically quantified as "choice probabilities" (CPs) (10). The "bottom-...

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Neuronal thresholds and choice-related activity of otolith afferent fibers during heading perception

Dickman

DeAngelis

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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“…9). Also a recently developed formalism of relating choice probabilities to stimulus preferences gives support to the scenario of saturated information and optimal readout as opposed to nonsaturated information and suboptimal readout (59). The above arguments address the case of computationally simple tasks like orientation discrimination; for hard tasks like object recognition under naturalistic viewing conditions the readout of V1 is likely suboptimal due to extensive computational approximations of downstream circuitry (60).…”

Section: Computation-induced Noise Correlations Contain a Tiny Amount Ofmentioning

confidence: 99%

Origin of information-limiting noise correlations

Kanitscheider

Coen-Cagli

Pouget

2015

Proc. Natl. Acad. Sci. U.S.A.

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The ability to discriminate between similar sensory stimuli relies on the amount of information encoded in sensory neuronal populations. Such information can be substantially reduced by correlated trial-to-trial variability. Noise correlations have been measured across a wide range of areas in the brain, but their origin is still far from clear. Here we show analytically and with simulations that optimal computation on inputs with limited information creates patterns of noise correlations that account for a broad range of experimental observations while at same time causing information to saturate in large neural populations. With the example of a network of V1 neurons extracting orientation from a noisy image, we illustrate to our knowledge the first generative model of noise correlations that is consistent both with neurophysiology and with behavioral thresholds, without invoking suboptimal encoding or decoding or internal sources of variability such as stochastic network dynamics or cortical state fluctuations. We further show that when information is limited at the input, both suboptimal connectivity and internal fluctuations could similarly reduce the asymptotic information, but they have qualitatively different effects on correlations leading to specific experimental predictions. Our study indicates that noise at the sensory periphery could have a major effect on cortical representations in widely studied discrimination tasks. It also provides an analytical framework to understand the functional relevance of different sources of experimentally measured correlations.noise correlations | information theory | neural computation | efficient coding | neuronal variability T he response of cortical neurons to an identical stimulus varies from trial to trial. Moreover, this variability tends to be correlated among pairs of nearby neurons. These correlations, known as noise correlations, have been the subject of numerous experimental as well as theoretical studies because they can have a profound impact on behavioral performance (1-7). Indeed, behavioral performance in discrimination tasks is inversely proportional to the Fisher information available in the neural responses, which itself is strongly dependent on the pattern of correlations. In particular, correlations can strongly limit information in the sense that some patterns of correlations can lead information to saturate to a finite value in large populations, in sharp contrast to the case of independent neurons for which information grows proportionally to the number of neurons. However, the saturation is observed for only one type of correlations known as differential correlations. If the correlation pattern slightly deviates from differential correlations, information typically scales with the number of neurons, just like it does for independent neurons (7). These previous results clarify how correlations impact information and consequently behavioral performance but fail to address another fundamental question, namely, Where do noise correlations, and in...

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“…Although we have previously shown that the activity of disparity-selective neurons in V2 is correlated with a macaque monkey's perceptual decision in a "coarse" dis-parity discrimination task (Fig. 1B) (Nienborg and Cumming, 2006, no one has examined this correlation for "fine" tasks that depend on relative disparity (Fig. 1A).…”

Section: Introductionmentioning

confidence: 97%

“…The first stage in the primate cortex that contains a subgroup of neurons selective for relative disparity is visual area V2 (Thomas et al, 2002;Neri et al, 2004). Although we have previously shown that the activity of disparity-selective neurons in V2 is correlated with a macaque monkey's perceptual decision in a "coarse" dis-parity discrimination task (Fig.…”

Section: Introductionmentioning

confidence: 99%

Decision-Related Activity in Macaque V2 for Fine Disparity Discrimination Is Not Compatible with Optimal Linear Readout

2016

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Fine judgments of stereoscopic depth rely mainly on relative judgments of depth (relative binocular disparity) between objects, rather than judgments of the distance to where the eyes are fixating (absolute disparity). In macaques, visual area V2 is the earliest site in the visual processing hierarchy for which neurons selective for relative disparity have been observed (Thomas et al., 2002). Here, we found that, in macaques trained to perform a fine disparity discrimination task, disparity-selective neurons in V2 were highly selective for the task, and their activity correlated with the animals' perceptual decisions (unexplained by the stimulus). This may partially explain similar correlations reported in downstream areas. Although compatible with a perceptual role of these neurons for the task, the interpretation of such decision-related activity is complicated by the effects of interneuronal "noise" correlations between sensory neurons. Recent work has developed simple predictions to differentiate decoding schemes (Pitkow et al., 2015) without needing measures of noise correlations, and found that data from early sensory areas were compatible with optimal linear readout of populations with informationlimiting correlations. In contrast, our data here deviated significantly from these predictions. We additionally tested this prediction for previously reported results of decision-related activity in V2 for a related task, coarse disparity discrimination (Nienborg and Cumming, 2006), thought to rely on absolute disparity. Although these data followed the predicted pattern, they violated the prediction quantitatively. This suggests that optimal linear decoding of sensory signals is not generally a good predictor of behavior in simple perceptual tasks.

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How Can Single Sensory Neurons Predict Behavior?

Cited by 136 publications

References 40 publications

Neuronal thresholds and choice-related activity of otolith afferent fibers during heading perception

Neuronal thresholds and choice-related activity of otolith afferent fibers during heading perception

Origin of information-limiting noise correlations

Decision-Related Activity in Macaque V2 for Fine Disparity Discrimination Is Not Compatible with Optimal Linear Readout

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