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Uncertainty is intrinsic to perception. Neural circuits which process sensory information must therefore also represent the reliability of this information. How they do so is a topic of debate. We propose a model of visual cortex in which average neural response strength encodes stimulus features, while cross-neuron variability in response gain encodes the uncertainty of these features. To test this model, we studied spiking activity of neurons in macaque V1 and V2 elicited by repeated presentations of stimuli whose uncertainty was manipulated in distinct ways. We show that gain variability of individual neurons is tuned to stimulus uncertainty, that this tuning is specific to the features encoded by these neurons and largely invariant to the source of uncertainty. We demonstrate that this behavior naturally arises from known gain-control mechanisms, and illustrate how downstream circuits can jointly decode stimulus features and their uncertainty from sensory population activity., How does the brain represent sensory uncertainty? The authors find that neural gain variability tracks stimulus uncertainty across the visual hierarchy and explain their findings with a simple generalization of canonical models of neural computation.

Many sensory-driven behaviors rely on predictions about future states of the environment. Visual input typically evolves along complex temporal trajectories that are difficult to extrapolate. We test the hypothesis that spatial processing mechanisms in the early visual system facilitate prediction by constructing neural representations that follow straighter temporal trajectories. We recorded V1 population activity in anesthetized macaques while presenting static frames taken from brief video clips, and developed a procedure to measure the curvature of the associated neural population trajectory. We found that V1 populations straighten naturally occurring image sequences, but entangle artificial sequences that contain unnatural temporal transformations. We show that these effects arise in part from computational mechanisms that underlie the stimulus selectivity of V1 cells. Together, our findings reveal that the early visual system uses a set of specialized computations to build representations that can support prediction in the natural environment., Many behaviours depend on predictions about the environment. Here the authors find neural populations in primary visual cortex to straighten the temporal trajectories of natural video clips, facilitating the extrapolation of past observations.

Many behaviors rely on predictions derived from recent visual input, but the temporal evolution of those inputs is generally complex and difficult to extrapolate. We propose that the visual system transforms these inputs to follow straighter temporal trajectories. To test this ‘temporal straightening’ hypothesis, we develop a methodology for estimating the curvature of an internal trajectory from human perceptual judgments. We use this to test three distinct predictions: natural sequences that are highly curved in the space of pixel intensities should be substantially straighter perceptually; in contrast, artificial sequences that are straight in the intensity domain should be more curved perceptually; finally, naturalistic sequences that are straight in the intensity domain should be relatively less curved. Perceptual data validate all three predictions, as do population models of the early visual system, providing evidence that the visual system specifically straightens natural videos, offering a solution for tasks that rely on prediction. The brain predicts future sensory input. The authors hypothesize that the visual system achieves this by straightening the temporal trajectories of natural videos, and they provide evidence using human perceptual experiments and computational modeling.

Uncertainty is intrinsic to perception. Neural circuits which process sensory information must therefore also represent the reliability of this information. How they do so is a topic of debate. We propose a view of visual cortex in which average neural response strength encodes stimulus features, while cross-neuron variability in response gain encodes the uncertainty of these features. To test our theory, we studied spiking activity of neurons in macaque V1 and V2 elicited by repeated presentations of stimuli whose uncertainty was manipulated in distinct ways. We show that gain variability of individual neurons is tuned to stimulus uncertainty, that this tuning is invariant to the source of uncertainty, and that it is specific to the features encoded by these neurons. We demonstrate that this behavior naturally arises from known gain-control mechanisms, and derive how downstream circuits can jointly decode stimulus features and their uncertainty from sensory population activity.

Several studies have reported optimal population decoding of sensory responses in two-alternative visual discrimination tasks. Such decoding involves integrating noisy neural responses into a more reliable representation of the likelihood that the stimuli under consideration evoked the observed responses. Importantly, an ideal observer must be able to evaluate likelihood with high precision and only consider the likelihood of the two relevant stimuli involved in the discrimination task. We report a new perceptual bias suggesting that observers read out the likelihood representation with remarkably low precision when discriminating grating spatial frequencies. Using spectrally filtered noise, we induced an asymmetry in the likelihood function of spatial frequency. This manipulation mainly affects the likelihood of spatial frequencies that are irrelevant to the task at hand. Nevertheless, we find a significant shift in perceived grating frequency, indicating that observers evaluate likelihoods of a broad range of irrelevant frequencies and discard prior knowledge of stimulus alternatives when performing two-alternative discrimination., Author Summary An attractive view on human information processing proposes that inference problems are dealt with in a statistically optimal fashion. This hypothesis can explain aspects of perception, movement planning, cognition and decision making. In the present study, I use a new psychophysical paradigm that reveals surprisingly suboptimal perceptual decision making. Observers discriminate between two sinusoidal gratings of a different spatial frequency. Making use of visual noise, I induce an asymmetry in neural population responses to the gratings and find this asymmetry to effectively bias perceptual decision making. A simple ideal observer model, uninformed about the presence of visual noise but only considering the two grating spatial frequencies relevant to the task at hand, manages to avoid such a bias. I conclude that observers are limited in their ability to make use of prior knowledge of relevant visual features when performing this task. These results are in line with a growing number of findings suggesting that near-optimal decoders, although straightforward to implement and achieving near-maximal performance, consistently overestimate empirical performance in simple perceptual tasks.

Visual object recognition is remarkably accurate and robust, yet its neurophysiological underpinnings are poorly understood. Single cells in brain regions thought to underlie object recognition code for many stimulus aspects, which poses a limit on their invariance. Combining the responses of multiple non-invariant neurons via weighted linear summation, i.e. population-coding, has been suggested to offer an optimal decoding strategy able to achieve invariant object recognition. However, because object identification is essentially parameter optimization in this model, the characteristics of the identification task trained to perform are critically important. If this task does not require invariance, a neural population-code is inherently more selective but less tolerant than the single-neurons constituting the population. Nevertheless, tolerance can be learned –provided that it is trained for–, at the cost of selectivity. We argue that this model is the appropriate null-hypothesis to compare behavioural results with and conclude that it may explain several experimental findings.

Neural mechanisms underlying invariant behaviour such as object recognition are not well understood. For brain regions critical for object recognition, such as inferior temporal cortex (ITC), there is now ample evidence indicating that single cells code for many stimulus aspects, implying that only a moderate degree of invariance is present. However, recent theoretical and empirical work seems to suggest that integrating responses of multiple non-invariant units may produce invariant representations at population level. We provide an explicit test for the hypothesis that a linear read-out mechanism of a pool of units resembling ITC neurons may achieve invariant performance in an identification task. A linear classifier was trained to decode a particular value in a 2-D stimulus space using as input the response pattern across a population of units. Only one dimension was relevant for the task, and the stimulus location on the irrelevant dimension (ID) was kept constant during training. In a series of identification tests, the stimulus location on the relevant dimension (RD) and ID was manipulated, yielding estimates for both the level of sensitivity and tolerance reached by the network. We studied the effects of several single-cell characteristics as well as population characteristics typically considered in the literature, but found little support for the hypothesis. While the classifier averages out effects of idiosyncratic tuning properties and inter-unit variability, its invariance is very much determined by the (hypothetical) ‘average’ neuron. Consequently, even at population level there exists a fundamental trade-off between selectivity and tolerance, and invariant behaviour does not emerge spontaneously. ispartof: Frontiers in Computational Neuroscience vol:3 issue:FEB pages:1-16 ispartof: location:Switzerland status: published