Reading population codes: a neural implementation of ideal observers

Denève, Sophie; Latham, Peter E.; Pouget, Alexandre

doi:10.1038/11205

Cited by 352 publications

(338 citation statements)

References 24 publications

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“…This result holds so long as the noise is Poisson and is independent among neurons. In a subsequent study, we extended this finding to networks encoding multiple independent variables [6]. More recently, we presented simulations suggesting an even more general result: networks encoding multiple variables, related to one another through nonlinear transformations, can be tuned to perform optimal computation even when the reliability of the input variables change from trial to trial [7].…”

Section: Introductionmentioning

confidence: 76%

Optimal computation with attractor networks

Latham¹,

Pouget²

2003

Journal of Physiology-Paris

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Section: Introductionmentioning

confidence: 76%

Optimal computation with attractor networks

Latham¹,

Pouget²

2003

Journal of Physiology-Paris

Self Cite

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“…5: Deneve et al 1999;Green and Swets 1966;Jazayeri and Movshon 2006;Ma et al 2006;Navalpakkam and Itti 2007;Pouget et al 2003;Seung and Sompolinsky 1993). In the present experiment, in which subjects had to discriminate between orientations sepa-FIG.…”

Section: Discussionmentioning

confidence: 99%

“…Stable perceptual representations are thought to be based on the shape of the response profile across populations of sensory neurons that individually respond to different low-level visual features (such as directions of motion, colors, orientations, etc.). In turn, this vector of neural responses represents the probability distribution of a given feature being present in the visual field (Deneve et al 1999;Jazayeri and Movshon 2006;Ma et al 2006;Pouget et al 2003). Thus understanding how value influences population response profiles is necessary to understand how value influences the perception of behaviorally relevant objects.…”

Section: Introductionmentioning

confidence: 99%

Population Response Profiles in Early Visual Cortex Are Biased in Favor of More Valuable Stimuli

Serences

Saproo

2010

Journal of Neurophysiology

104

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Serences JT, Saproo S. Population response profiles in early visual cortex are biased in favor of more valuable stimuli. J Neurophysiol 104: 76 -87, 2010. First published April 21, 2010 doi:10.1152/jn.01090.2009. Voluntary and stimulus-driven shifts of attention can modulate the representation of behaviorally relevant stimuli in early areas of visual cortex. In turn, attended items are processed faster and more accurately, facilitating the selection of appropriate behavioral responses. Information processing is also strongly influenced by past experience and recent studies indicate that the learned value of a stimulus can influence relatively late stages of decision making such as the process of selecting a motor response. However, the learned value of a stimulus can also influence the magnitude of cortical responses in early sensory areas such as V1 and S1. These early effects of stimulus value are presumed to improve the quality of sensory representations; however, the nature of these modulations is not clear. They could reflect nonspecific changes in response amplitude associated with changes in general arousal or they could reflect a bias in population responses so that high-value features are represented more robustly. To examine this issue, subjects performed a two-alternative forced choice paradigm with a variableinterval payoff schedule to dynamically manipulate the relative value of two stimuli defined by their orientation (one was rotated clockwise from vertical, the other counterclockwise). Activation levels in visual cortex were monitored using functional MRI and feature-selective voxel tuning functions while subjects performed the behavioral task. The results suggest that value not only modulates the relative amplitude of responses in early areas of human visual cortex, but also sharpens the response profile across the populations of feature-selective neurons that encode the critical stimulus feature (orientation). Moreover, changes in space-or feature-based attention cannot easily explain the results because representations of both the selected and the unselected stimuli underwent a similar feature-selective modulation. This sharpening in the population response profile could theoretically improve the probability of correctly discriminating high-value stimuli from low-value alternatives.

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“…In cases in which more than one unit was used, the activities of the units were assumed to be independent. The ML estimator is optimal in the sense that it is unbiased and has minimum variance (Deneve et al, 1999) if the prior probability of direction or go vs. no go are uniform. Moreover, it is a particularly attractive approach to decoding when the number of recorded cells is small.…”

Section: Discussionmentioning

confidence: 99%