Shared, trial-to-trial variability in neuronal populations has a strong impact on the accuracy of information processing in the brain. Estimates of the level of such noise correlations are diverse, ranging from 0.01 to 0.4, with little consensus on which factors account for these differences. Here we addressed one important factor that varied across studies, asking how anesthesia affects the population activity structure in macaque primary visual cortex. We found that under opioid anesthesia, activity was dominated by strong coordinated fluctuations on a timescale of 1–2 Hz, which were mostly absent in awake, fixating monkeys. Accounting for these global fluctuations markedly reduced correlations under anesthesia, matching those observed during wakefulness and reconciling earlier studies conducted under anesthesia and in awake animals. Our results show that internal signals, such as brain state transitions under anesthesia, can induce noise correlations, but can also be estimated and accounted for based on neuronal population activity.

The neural code is believed to have adapted to the statistical properties of the natural environment. However, the principles that govern the organization of ensemble activity in the visual cortex during natural visual input are unknown. We recorded populations of up to 500 neurons in the mouse primary visual cortex and characterized the structure of their activity, comparing responses to natural movies with those to control stimuli. We found that higher-order correlations in natural scenes induce a sparser code, in which information is encoded by reliable activation of a smaller set of neurons and can be read-out more easily. This computationally advantageous encoding for natural scenes was state-dependent and apparent only in anesthetized and active awake animals, but not during quiet wakefulness. Our results argue for a functional benefit of sparsification that could be a general principle governing the structure of the population activity throughout cortical microcircuits.

Categorization is a cornerstone of perception and cognition. Computationally, categorization amounts to applying decision boundaries in the space of stimulus features. We designed a visual categorization task in which optimal performance requires observers to incorporate trial-to-trial knowledge of the level of sensory uncertainty when setting their decision boundaries. We found that humans and monkeys did adjust their decision boundaries from trial to trial as the level of sensory noise varied, with some subjects performing near optimally. We constructed a neural network that implements uncertainty-based, near-optimal adjustment of decision boundaries. Divisive normalization emerges automatically as a key neural operation in this network. Our results offer an integrated computational and mechanistic framework for categorization under uncertainty.Bayesian inference | vision | decision-making | optimality I magine a woman is approaching you from a distance and you are trying to determine whether or not she is the friend you are waiting for. Because of various sources of noise, your observations of her facial features, hair color, etc. will be uncertain. A sensible strategy would be to be more tolerant to deviations between your observations and your knowledge of your friend's looks when she is far away than when she is close by and your observations are less uncertain. In this categorization problem, you are determining whether the image of the approaching woman falls into the narrow category of images of your friend or the wide category of images of all other people. Categorization can be modeled as a process of applying one or more decision boundaries to a noisy measurement in a space of stimulus features (1-7). The example suggests that adjusting such decision boundaries based on the current level of sensory uncertainty might be a better strategy than using uncertainty-independent decision boundaries.Previous studies have not addressed whether organisms adjust their decision boundaries from trial to trial according to the level of sensory uncertainty. Perceptual studies of categorization under sensory uncertainty have typically used category distributions for which the level of uncertainty was irrelevant for optimal behavior (2,3,6,8). For example, in a classic task, observers categorize the direction of motion of a set of dots coherently moving to the left or to the right, in the presence of distractor dots moving in random directions (8). Regardless of the level of sensory noise corrupting the brain's measurement of the net motion direction, the optimal decision is simply to report whether this measurement was to the right or to the left. In other words, applying a fixed decision boundary to a scalar estimate is optimal in this task; no knowledge of uncertainty about motion direction is needed. In cognitive models of categorization, dynamic decision boundaries have been invoked to explain a broad range of phenomenona, including sequential effects (9, 10), context effects (11), and generalization (12). Howeve...

Orientation tuning has been a classic model for understanding single neuron computation in the neocortex. However, little is known about how orientation can be read out from the activity of neural populations, in particular in alert animals. Our study is a first step towards that goal. We recorded from up to 20 well-isolated single neurons in the primary visual cortex of alert macaques simultaneously and applied a simple, neurally plausible decoder to read out the population code. We focus on two questions: First, what are the time course and the time scale at which orientation can be read out from the population response? Second, how complex does the decoding mechanism in a downstream neuron have to be in order to reliably discriminate between visual stimuli with different orientations? We show that the neural ensembles in primary visual cortex of awake macaques represent orientation in a way that facilitates a fast and simple read-out mechanism: with an average latency of 30–80 ms, the population code can be read out instantaneously with a short integration time of only tens of milliseconds and neither stimulus contrast nor correlations need to be taken into account to compute the optimal synaptic weight pattern. Our study shows that – similar to the case of single neuron computation – the representation of orientation in the spike patterns of neural populations can serve as an exemplary case for understanding of the computations performed by neural ensembles underlying visual processing during behavior.

The atmospheric neutrino flavour ratio measured using a 1.52 kton-year exposure of Soudan 2 is found to be 0.72 +- 0.19 +0.05 -0.07 relative to the expected value from a Monte Carlo calculation. The possible background of interactions of neutrons and photons produced in muon interactions in the rock surrounding the detector has been investigated and is shown not to produce low values of the ratio.Comment: 18 pages, LaTeX, 3 ps files, and 2 style files. Submitted to Physics Letter