Variance as a Signature of Neural Computations during Decision Making

Churchland, Anne K.; Kiani, Roozbeh; Chaudhuri, Rishidev; Wang, Xiao Jing; Pouget, Alexandre; Shadlen, Michael N.

doi:10.1016/j.neuron.2010.12.037

Cited by 347 publications

(424 citation statements)

References 47 publications

Supporting

Mentioning

374

Contrasting

Unclassified

Order By: Relevance

“…When gain is higher, a stimulus is encoded with higher precision (11,12). Variability in gain across items and trials is consistent with observations of single-neuron firing rate variability (13)(14)(15) and attentional fluctuations (16,17).…”

supporting

confidence: 85%

See 1 more Smart Citation

Variability in encoding precision accounts for visual short-term memory limitations

Berg

Shin

Chou

et al. 2012

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

518

806

View full text Add to dashboard Cite

It is commonly believed that visual short-term memory (VSTM) consists of a fixed number of "slots" in which items can be stored. An alternative theory in which memory resource is a continuous quantity distributed over all items seems to be refuted by the appearance of guessing in human responses. Here, we introduce a model in which resource is not only continuous but also variable across items and trials, causing random fluctuations in encoding precision. We tested this model against previous models using two VSTM paradigms and two feature dimensions. Our model accurately accounts for all aspects of the data, including apparent guessing, and outperforms slot models in formal model comparison. At the neural level, variability in precision might correspond to variability in neural population gain and doubly stochastic stimulus representation. Our results suggest that VSTM resource is continuous and variable rather than discrete and fixed and might explain why subjective experience of VSTM is not all or none.T homas Chamberlin famously warned scientists against entertaining only a single hypothesis, for such a modus operandi might lead to undue attachment and "a pressing of the facts to make them fit the theory" (ref. 1, p. 840). For half a century, the study of short-term memory limitations has been dominated by a single hypothesis, namely that a fixed number of items can be held in memory and any excess items are discarded (2-5). The alternative notion that short-term memory resource is a continuous quantity distributed over all items, with a lower amount per item translating into lower encoding precision, has enjoyed some success (6-8), but has been unable to account for the finding that humans often seem to make a random guess when asked to report the identity of one of a set of remembered items, especially when many items are present (9). Specifically, if resource were evenly distributed across items (6, 10), observers would never guess. Thus, at present, no viable continuous-resource model exists.Here, we propose a more sophisticated continuous-resource model, the variable-precision (VP) model, in which the amount of resource an item receives, and thus its encoding precision, varies randomly across items and trials and on average decreases with set size. Resource might correspond to the gain of a neural population pattern of activity encoding a memorized feature. When gain is higher, a stimulus is encoded with higher precision (11,12). Variability in gain across items and trials is consistent with observations of single-neuron firing rate variability (13-15) and attentional fluctuations (16, 17).We tested the VP model against three alternative models (Fig. 1). According to the classic item-limit (IL) model (4), a fixed number of items is kept in memory, and memorized items are recalled perfectly. In the equal-precision (EP) model (6, 10), a continuous resource is evenly distributed across all items. The slots-plus-averaging (SA) model (9) acknowledges the presence of noise but combines it with the notion of ...

show abstract

supporting

confidence: 85%

“…Supporting this notion, doubly stochastic processes can well describe spike counts in lateral intraparietal cortex (LIP) (13), visual cortex (15), and other areas (14). Thus, the VP model is broadly consistent with emerging physiological findings.…”

Section: B Asupporting

confidence: 66%

Variability in encoding precision accounts for visual short-term memory limitations

Berg

Shin

Chou

et al. 2012

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

518

806

View full text Add to dashboard Cite

show abstract

“…We assumed that neuronal variability had two sources (1,11,(35)(36)(37): the first resulted from the variations in the population rate r(t) mainly caused by transitions between silent and active network attractors, and the second arising from spiking stochasticity existent at constant rate. Neurons fired statistically identical Poisson spike trains with rate r(t).…”

Section: Methodsmentioning

confidence: 99%

“…To understand the mechanisms underlying correlations and their relation with silence density, we analyzed a model with two sources of neuronal variability: the first reflecting variations in the firing rate r, and the second reflecting the spiking stochasticity existent at constant rate (1,11,(35)(36)(37). Under this assumption, spike count correlations could be explained, at least in part, if the rate variability was correlated across neurons (35,37).…”

Section: Computational Rate Model Reproduces ρ-S Relation Across Brainmentioning

confidence: 99%

Stochastic transitions into silence cause noise correlations in cortical circuits

Mochol

Hermoso-Mendizabal

Sakata

et al. 2015

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

View full text Add to dashboard Cite

The spiking activity of cortical neurons is highly variable. This variability is generally correlated among nearby neurons, an effect commonly interpreted to reflect the coactivation of neurons due to anatomically shared inputs. Recent findings, however, indicate that correlations can be dynamically modulated, suggesting that the underlying mechanisms are not well understood. Here, we investigate the hypothesis that correlations are dominated by neuronal coinactivation: the occurrence of brief silent periods during which all neurons in the local network stop firing. We recorded spiking activity from large populations of neurons in the auditory cortex of anesthetized rats across different brain states. During spontaneous activity, the reduction of correlation accompanying brain state desynchronization was largely explained by a decrease in the density of the silent periods. The presentation of a stimulus caused an initial drop of correlations followed by a rebound, a time course that was mimicked by the instantaneous silence density. We built a rate network model with fluctuation-driven transitions between a silent and an active attractor and assumed that neurons fired Poisson spike trains with a rate following the model dynamics. Variations of the network external input altered the transition rate into the silent attractor and reproduced the relation between correlation and silence density found in the data, both in spontaneous and evoked conditions. This suggests that the observed changes in correlation, occurring gradually with brain state variations or abruptly with sensory stimulation, are due to changes in the likeliness of the microcircuit to transiently cease firing.neuronal variability | noise correlations | brain state | auditory cortex | stochastic network dynamics N euronal noise correlations are defined as common fluctuations in the spiking activity of neurons under conditions of constant sensory input or motor output. Traditionally, they have been thought to arise from the dense connectivity of the cortex, such that neighboring neurons sharing a fraction of their inputs should also share a fraction of their output variability (1). Several observations are consistent with this hypothesis: pairwise correlations in the cortex decrease with cell pair distance (2) or with the difference in stimulus selectivity (3), dependencies that could follow from a variation in shared input given the anatomy of cortical circuits. Recent findings, however, challenge this simple interpretation. Recordings in the primate visual cortex have shown that attention or task context can change correlation structure (4-6) and that the magnitude of averaged correlation can be very low (7). In anesthetized rodents correlations decrease with brain state desynchronization (8, 9) or when animals switch from quiet wakefulness to active whisking during waking (10). Moreover, the commonly observed drop of spiking variability following stimulus onset (11-13) seems to occur jointly with a transient decrease in correlation (2, 14, 15). Th...

show abstract

“…Previous studies reported increasing trial-to-trial variability in neural activities leading up to a voluntary movement onset (Steinmetz and Moore, 2010;Churchland et al, 2011), whereas stimulus onset quenches neural variability (Steinmetz and Moore, 2010;Churchland et al, 2010;Litwin-Kumar and Doiron, 2012). As the EODR reflects the neural activity associated with sensory sampling, we examined how EODR activity varied between trials during spontaneous and evoked transitions as a proxy for the variation in neural activity.…”

Section: Preparatory Increase In the Sensory Sampling Variabilitymentioning

confidence: 99%

Enhanced sensory sampling precedes self-initiated locomotion in an electric fish

Jun

Longtin

Maler

2014

Journal of Experimental Biology

View full text Add to dashboard Cite

Cortical activity precedes self-initiated movements by several seconds in mammals; this observation has led into inquiries on the nature of volition. Preparatory neural activity is known to be associated with decision making and movement planning. Selfinitiated locomotion has been linked to increased active sensory sampling; however, the precise temporal relationship between sensory acquisition and voluntary movement initiation has not been established. Based on long-term monitoring of sensory sampling activity that is readily observable in freely behaving pulse-type electric fish, we show that heightened sensory acquisition precedes spontaneous initiation of swimming. Gymnotus sp. revealed a bimodal distribution of electric organ discharge rate (EODR) demonstrating down-and up-states of sensory sampling and neural activity; movements only occurred during up-states and up-states were initiated before movement onset. EODR during voluntary swimming initiation exhibited greater trial-to-trial variability than the sound-evoked increases in EODR. The sampling variability declined after voluntary movement onset as previously observed for the neural variability associated with decision making in primates. Spontaneous movements occurred randomly without a characteristic timescale, and no significant temporal correlation was found between successive movement intervals. Using statistical analyses of spontaneous exploratory behaviours and associated preparatory sensory sampling increase, we conclude that electric fish exhibit key attributes of volitional movements, and that voluntary behaviours in vertebrates may generally be preceded by increased sensory sampling. Our results suggest that comparative studies of the neural basis of volition may therefore be possible in pulse-type electric fish, given the substantial homologies between the telencephali of teleost fish and mammals.

show abstract

Variance as a Signature of Neural Computations during Decision Making

Cited by 347 publications

References 47 publications

Variability in encoding precision accounts for visual short-term memory limitations

Variability in encoding precision accounts for visual short-term memory limitations

Stochastic transitions into silence cause noise correlations in cortical circuits

Enhanced sensory sampling precedes self-initiated locomotion in an electric fish

Contact Info

Product

Resources

About