The brain maintains internal models of its environment to interpret sensory inputs and prepare actions. While behavioral studies demonstrated that these internal models are optimally adapted to the statistics of the environment, the neural underpinning of this adaptation is unknown. Using a Bayesian model of sensory cortical processing, we related stimulus-evoked and spontaneous activities to inferences and prior expectations in an internal model and predicted that they should match if the model is statistically optimal. To test this prediction, we analyzed visual cortical activity of awake ferrets during development. Similarity between spontaneous and evoked activities increased with age and was specific to responses evoked by natural scenes. This demonstrates the progressive adaptation of internal models to the statistics of natural stimuli at the neural level.

Human perception has recently been characterized as statistical inference based on noisy and ambiguous sensory inputs. Moreover, suitable neural representations of uncertainty have been identified that could underlie such probabilistic computations. In this review, we argue that learning an internal model of the sensory environment is another key aspect of the same statistical inference procedure and thus perception and learning need to be treated jointly. We review evidence for statistically optimal learning in humans and animals, and reevaluate possible neural representations of uncertainty based on their potential to support statistically optimal learning. We propose that spontaneous activity can have a functional role in such representations leading to a new, samplingbased, framework of how the cortex represents information and uncertainty. Probabilistic perception, learning and representation of uncertainty: in need of a unifying approachOne of the longstanding computational principles in neuroscience is that the nervous system of animals and humans is adapted to the statistical properties of the environment [1]. This principle is reflected across all organizational levels, ranging from the activity of single neurons to networks and behavior, and it has been identified as key to the survival of organisms [2]. Such adaptation takes place on at least two distinct behaviorally relevant time scales: on the time scale of immediate inferences, as a moment-by-moment processing of sensory input (perception), and on a longer time scale by learning from experience. Although statistically optimal perception and learning have most often been considered in isolation, here we promote them as two facets of the same underlying principle and treat them together under a unified approach.Although there is considerable behavioral evidence that humans and animals represent, infer and learn about the statistical properties of their environment efficiently [3], and there is also NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author Manuscript converging theoretical and neurophysiological work on potential neural mechanisms of statistically optimal perception [4], there is a notable lack of convergence from physiological and theoretical studies explaining whether and how statistically optimal learning might occur in the brain. Moreover, there is a missing link between perception and learning: there exists virtually no crosstalk between these two lines of research focusing on common principles and on a unified framework down to the level of neural implementation. With recent advances in understanding the bases of probabilistic coding and the accumulating evidence supporting probabilistic computations in the cortex, it is now possible to take a closer look at both the basis of probabilistic learning and its relation to probabilistic perception.We first provide a brief overview of the theoretical framework as well as behavioral and neural evidence for representing uncertainty in perceptual processes. To highlight th...

Three experiments investigated the ability of human observers to extract the joint and conditional probabilities of shape co-occurrences during passive viewing of complex visual scenes. Results indicated that statistical learning of shape conjunctions was both rapid and automatic, as subjects were not instructed to attend to any particularfeatures of the displays. Moreover, in addition to single-shape frequency, subjects acquired in parallel several different higher-order aspects of the statistical structure of the displays, including absolute shape-position relations in an array, shape-pair arrangements independent of position, and conditional probabilities of shape co-occurrences. Unsupervised learning of these higher-order statistics provides support for Barlow's theory of visual recognition, which posits that detecting "suspicious coincidences" of elements during recognition is a necessary prerequisite for efficient learning of new visual features.

In 3 experiments, the authors investigated the ability of observers to extract the probabilities of successive shape co-occurrences during passive viewing. Participants became sensitive to several temporal-order statistics, both rapidly and with no overt task or explicit instructions. Sequences of shapes presented during familiarization were distinguished from novel sequences of familiar shapes, as well as from shape sequences that were seen during familiarization but less frequently than other shape sequences, demonstrating at least the extraction of joint probabilities of 2 consecutive shapes. When joint probabilities did not differ, another higher-order statistic (conditional probability) was automatically computed, thereby allowing participants to predict the temporal order of shapes. Results of a single-shape test documented that lower-order statistics were retained during the extraction of higher-order statistics. These results suggest that observers automatically extract multiple statistics of temporal events that are suitable for efficient associative learning of new temporal features.

During vision, it is believed that neural activity in the primary visual cortex is predominantly driven by sensory input from the environment. However, visual cortical neurons respond to repeated presentations of the same stimulus with a high degree of variability. Although this variability has been considered to be noise owing to random spontaneous activity within the cortex, recent studies show that spontaneous activity has a highly coherent spatio-temporal structure. This raises the possibility that the pattern of this spontaneous activity may shape neural responses during natural viewing conditions to a larger extent than previously thought. Here, we examine the relationship between spontaneous activity and the response of primary visual cortical neurons to dynamic natural-scene and random-noise film images in awake, freely viewing ferrets from the time of eye opening to maturity. The correspondence between evoked neural activity and the structure of the input signal was weak in young animals, but systematically improved with age. This improvement was linked to a shift in the dynamics of spontaneous activity. At all ages including the mature animal, correlations in spontaneous neural firing were only slightly modified by visual stimulation, irrespective of the sensory input. These results suggest that in both the developing and mature visual cortex, sensory evoked neural activity represents the modulation and triggering of ongoing circuit dynamics by input signals, rather than directly reflecting the structure of the input signal itself.

SummaryNeural responses in the visual cortex are variable, and there is now an abundance of data characterizing how the magnitude and structure of this variability depends on the stimulus. Current theories of cortical computation fail to account for these data; they either ignore variability altogether or only model its unstructured Poisson-like aspects. We develop a theory in which the cortex performs probabilistic inference such that population activity patterns represent statistical samples from the inferred probability distribution. Our main prediction is that perceptual uncertainty is directly encoded by the variability, rather than the average, of cortical responses. Through direct comparisons to previously published data as well as original data analyses, we show that a sampling-based probabilistic representation accounts for the structure of noise, signal, and spontaneous response variability and correlations in the primary visual cortex. These results suggest a novel role for neural variability in cortical dynamics and computations.

Implicit skill learning underlies obtaining not only motor, but also cognitive and social skills through the life of an individual. Yet, the ontogenetic changes in humans’ implicit learning abilities have not yet been characterized, and, thus, their role in acquiring new knowledge efficiently during development is unknown. We investigated such learning across the life span, between 4–85 years of age with an implicit probabilistic sequence learning task, and we found that the difference in implicitly learning high vs. low probability events - measured by raw reaction time (RT) - exhibited a rapid decrement around age of 12. Accuracy and z-transformed data showed partially different developmental curves suggesting a re-evaluation of analysis methods in developmental research. The decrement in raw RT differences supports an extension of the traditional 2-stage lifespan skill acquisition model: in addition to a decline above the age 60 reported in earlier studies, sensitivity to raw probabilities and, therefore, acquiring new skills is significantly more effective until early adolescence than later in life. These results suggest that due to developmental changes in early adolescence, implicit skill learning processes undergo a marked shift in weighting raw probabilities vs. more complex interpretations of events, which, with appropriate timing, prove to be an optimal strategy for human skill learning.

We address two main challenges facing systems neuroscience today: understanding the nature and function of cortical feedback between sensory areas and of correlated variability. Starting from the old idea of perception as probabilistic inference, we show how to use knowledge of the psychophysical task to make testable predictions for the influence of feedback signals on early sensory representations. Applying our framework to a two-alternative forced choice task paradigm, we can explain multiple empirical findings that have been hard to account for by the traditional feedforward model of sensory processing, including the task dependence of neural response correlations and the diverging time courses of choice probabilities and psychophysical kernels. Our model makes new predictions and characterizes a component of correlated variability that represents task-related information rather than performance-degrading noise. It demonstrates a normative way to integrate sensory and cognitive components into physiologically testable models of perceptual decision-making.