Partitioning neuronal variability

Goris, Robbe L. T.; Movshon, J. Anthony; Simoncelli, Eero P.

doi:10.1038/nn.3711

Cited by 490 publications

(833 citation statements)

References 47 publications

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“…The large trial-to-trial variability of cortical responses is a major obstacle in the readout of neural codes (14). Our results above-and several previous studies-show that variations in cortical state (12,(15)(16)(17) and neuromodulation (4) contribute to trial-to-trial cortical response variability. Here, we explore the consequences of successfully modeling the neuromodulatory cortical state for the decoding of cortical responses to sensory stimuli.…”

Section: Extracting Sensory Information From State-dependent Corticalsupporting

confidence: 82%

Modeling the effect of locus coeruleus firing on cortical state dynamics and single-trial sensory processing

Safaai

Neves

Eschenko

et al. 2015

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

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Neuronal responses to sensory stimuli are not only driven by feedforward sensory pathways but also depend upon intrinsic factors (collectively known as the network state) that include ongoing spontaneous activity and neuromodulation. To understand how these factors together regulate cortical dynamics, we recorded simultaneously spontaneous and somatosensory-evoked multiunit activity from primary somatosensory cortex and from the locus coeruleus (LC) (the neuromodulatory nucleus releasing norepinephrine) in urethane-anesthetized rats. We found that bursts of ipsilateral-LC firing preceded by few tens of milliseconds increases of cortical excitability, and that the 1-to 10-Hz rhythmicity of LC discharge appeared to increase the power of deltaband (1-4 Hz) cortical synchronization. To investigate quantitatively how LC firing might causally influence spontaneous and stimulus-driven cortical dynamics, we then constructed and fitted to these data a model describing the dynamical interaction of stimulus drive, ongoing synchronized cortical activity, and noradrenergic neuromodulation. The model proposes a coupling between LC and cortex that can amplify delta-range cortical fluctuations, and shows how suitably timed phasic LC bursts can lead to enhanced cortical responses to weaker stimuli and increased temporal precision of cortical stimulus-evoked responses. Thus, the temporal structure of noradrenergic modulation may selectively and dynamically enhance or attenuate cortical responses to stimuli. Finally, using the model prediction of single-trial cortical stimulus-evoked responses to discount single-trial state-dependent variability increased by ∼70% the sensory information extracted from cortical responses. This suggests that downstream circuits may extract information more effectively after estimating the state of the circuit transmitting the sensory message.oscillations | state dependence | dynamical systems | information coding | somatosensation R esponsiveness of cortical sensory neurons is state dependent. In other words, the neural responses to a sensory stimulus do not only depend on the features of extrinsic sensory inputs but also on intrinsic network variables that can be collectively defined as the "network state" (1). Cortical sensory neurons receive information about the external world from peripheral receptors via feedforward sensory pathways. However, the abundance of recurrent and feedback connectivity (2) may generate ongoing activity that shapes the background on which the afferent information is processed (3). In addition, neuromodulatory inputs from neurochemically specialized brain nuclei that are not part of the direct spino-thalamo-cortical pathway can modulate the dynamics of cortical networks (4), as well as control the animal's behavioral state. The concurrent integration of information about the external world and about internal states is likely to be central for computational operations of cortical circuits and for the production of complex behavior, yet its mechanisms and implications for...

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Section: Extracting Sensory Information From State-dependent Corticalsupporting

confidence: 82%

Modeling the effect of locus coeruleus firing on cortical state dynamics and single-trial sensory processing

Safaai

Neves

Eschenko

et al. 2015

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

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“…A rate network model with adaptation showing stochastic transitions between two attractors reproduced the ρ−S relation observed during spontaneous and evoked conditions. Two recent studies have proposed that fluctuations in neuronal excitability cause noise correlations in monkey visual cortex (34,37). Whereas in one study correlated fluctuations of excitability only accounted for a fraction of the total correlation (37), in the other fluctuations "resembling up and down states" explained almost all measured correlations (34).…”

Section: Discussionmentioning

confidence: 99%

“…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). For example, it has been shown that individual spike count statistics can be described by a Poisson process in which the rate varies from trial to trial following a broad unimodal distribution (37).…”

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

confidence: 99%

“…Under this assumption, spike count correlations could be explained, at least in part, if the rate variability was correlated across neurons (35,37). For example, it has been shown that individual spike count statistics can be described by a Poisson process in which the rate varies from trial to trial following a broad unimodal distribution (37). We tested this model in spontaneous conditions across different brain states and compared it with a model in which the rate followed a bimodal distribution reflecting network transitions between a silent (r = 0) and an active state (r > 0).…”

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

confidence: 99%

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Stochastic transitions into silence cause noise correlations in cortical circuits

Mochol

Hermoso-Mendizabal

Sakata

et al. 2015

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

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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...

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Flexible models for spike count data with both over- and under- dispersion

Stevenson

2016

J Comput Neurosci

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A key observation in systems neuroscience is that neural responses vary, even in controlled settings where stimuli are held constant. Many statistical models assume that trial-to-trial spike count variability is Poisson, but there is considerable evidence that neurons can be substantially more or less variable than Poisson depending on the stimuli, attentional state, and brain area. Here we examine a set of spike count models based on the Conway-Maxwell-Poisson (COM-Poisson) distribution that can flexibly account for both over- and under-dispersion in spike count data. We illustrate applications of this noise model for Bayesian estimation of tuning curves and peri-stimulus time histograms. We find that COM-Poisson models with group/observation-level dispersion, where spike count variability is a function of time or stimulus, produce more accurate descriptions of spike counts compared to Poisson models as well as negative-binomial models often used as alternatives. Since dispersion is one determinant of parameter standard errors, COM-Poisson models are also likely to yield more accurate model comparison. More generally, these methods provide a useful, model-based framework for inferring both the mean and variability of neural responses.

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Partitioning neuronal variability

Cited by 490 publications

References 47 publications

Modeling the effect of locus coeruleus firing on cortical state dynamics and single-trial sensory processing

Modeling the effect of locus coeruleus firing on cortical state dynamics and single-trial sensory processing

Stochastic transitions into silence cause noise correlations in cortical circuits

Flexible models for spike count data with both over- and under- dispersion

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