Stimulus onset quenches neural variability: a widespread cortical phenomenon

Churchland, Mark M.; Yu, Byron M.; Cunningham, John P.; Sugrue, Leo P.; Cohen, Marlene R.; Corrado, Greg S.; Newsome, William T.; Clark, Andrew M.; Hosseini, Paymon; Scott, Bruce R.; Bradley, David; Ma, Smith; Kohn, Adam; Movshon, J. Anthony; Armstrong, Katherine M.; Moore, Tirin; Chang, Steve; Snyder, Lawrence H.; Lisberger, Stephen G.; Priebe, Nicholas J.; Finn, Ian M.; Ferster, David; Ryu, Stephen I.; Santhanam, Gopal; Sahani, Maneesh; Shenoy, Krishna V.

doi:10.1038/nn.2501

Cited by 999 publications

(1,296 citation statements)

References 48 publications

Supporting

176

Mentioning

1,070

Contrasting

Order By: Relevance

“…1B). Note that both the evoked trial-by-trial variability and noise correlations are reduced compared with their spontaneous levels, consistent with previous empirical observations (16,18), a feature that is captured by the model. Although the present model explains well the stationary distribution of variability and correlations, it hardly accounts for the rich temporal evolution of neural response statistics.…”

Section: Discussionsupporting

confidence: 90%

“…Furthermore, variability is often correlated among neurons, and thus, it cannot be completely removed by averaging the population response (9)(10)(11)(12). Recent experimental studies have examined the secondorder statistics of neural responses across a variety of species, cortical areas, tasks, and stimulus and/or attentional conditions (13)(14)(15)(16)(17). In particular, it has been shown that the Fano factor (FF)-that is, the ratio between the variance of the spike counts over trials and its mean-is reduced when a stimulus is applied (16), thus improving the encoding of the stimulus.…”

mentioning

confidence: 99%

“…Recent experimental studies have examined the secondorder statistics of neural responses across a variety of species, cortical areas, tasks, and stimulus and/or attentional conditions (13)(14)(15)(16)(17). In particular, it has been shown that the Fano factor (FF)-that is, the ratio between the variance of the spike counts over trials and its mean-is reduced when a stimulus is applied (16), thus improving the encoding of the stimulus. Importantly, both preferred and nonpreferred stimuli reduced the FF.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Stimulus-dependent variability and noise correlations in cortical MT neurons

Ponce‐Alvarez

Thiele

Albright

et al. 2013

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

124

152

View full text Add to dashboard Cite

Population codes assume that neural systems represent sensory inputs through the firing rates of populations of differently tuned neurons. However, trial-by-trial variability and noise correlations are known to affect the information capacity of neural codes. Although recent studies have shown that stimulus presentation reduces both variability and rate correlations with respect to their spontaneous level, possibly improving the encoding accuracy, whether these second order statistics are tuned is unknown. If so, second-order statistics could themselves carry information, rather than being invariably detrimental. Here we show that rate variability and noise correlation vary systematically with stimulus direction in directionally selective middle temporal (MT) neurons, leading to characteristic tuning curves. We show that such tuning emerges in a stochastic recurrent network, for a set of connectivity parameters that overlaps with a single-state scenario and multistability. Information theoretic analysis shows that second-order statistics carry information that can improve the accuracy of the population code.C ortical activity is highly variable during spontaneous activity (1-4) and even when tested under constant experimental conditions (5-8). This variability is thought to limit the capacity of individual neurons to transmit information (9). Furthermore, variability is often correlated among neurons, and thus, it cannot be completely removed by averaging the population response (9-12). Recent experimental studies have examined the secondorder statistics of neural responses across a variety of species, cortical areas, tasks, and stimulus and/or attentional conditions (13-17). In particular, it has been shown that the Fano factor (FF)-that is, the ratio between the variance of the spike counts over trials and its mean-is reduced when a stimulus is applied (16), thus improving the encoding of the stimulus. Importantly, both preferred and nonpreferred stimuli reduced the FF. In addition, the evoked noise correlation-that is, the trial-to-trial covariance of stimulus induced activity between two simultaneously recorded neurons-is also reduced upon stimulus presentation (18), after stimulus adaptation (19) or perceptual learning (20), and under attention (14, 21), an effect that could, under certain conditions, lead to more reliable estimates of the mean population activity (22). Hence, there is a growing body of evidence suggesting that the encoding of a signal through cortical activity may be improved by minimizing both trialby-trial variability and noise correlations. However, it remains an open experimental and theoretical question, whether these statistics are themselves tuned to different stimulus features, an aspect that may be overlooked when only analyzing preferred and nonpreferred stimuli.Here, we examined the statistics of responses of area-middle temporal (MT) neurons in awake, fixating primates, to moving gratings and different plaid patterns of different directions, as well as moving gratings of differ...

show abstract

Section: Discussionsupporting

confidence: 90%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Stimulus-dependent variability and noise correlations in cortical MT neurons

Ponce‐Alvarez

Thiele

Albright

et al. 2013

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

124

152

View full text Add to dashboard Cite

show abstract

“…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: 84%

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

Berg

Shin

Chou

et al. 2012

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

526

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

“…An associated question is how conscious content can be flexibly refreshed and or maintained in a transient memory system. Popular notions of rate coding [223] would require a labelled line hierarchical coding system, where each synapse and neuron in the system has a specific semantic dedication. How can such a solution provide a representational substrate for the practically infinite set of phenomenal states, i.e.…”

Section: Discussionmentioning

confidence: 99%

Synthetic consciousness: the distributed adaptive control perspective

Verschure

2016

Phil. Trans. R. Soc. B

View full text Add to dashboard Cite

One contribution of 13 to a theme issue 'The major synthetic evolutionary transitions'. Understanding the nature of consciousness is one of the grand outstanding scientific challenges. The fundamental methodological problem is how phenomenal first person experience can be accounted for in a third person verifiable form, while the conceptual challenge is to both define its function and physical realization. The distributed adaptive control theory of consciousness (DACtoc) proposes answers to these three challenges. The methodological challenge is answered relative to the hard problem and DACtoc proposes that it can be addressed using a convergent synthetic methodology using the analysis of synthetic biologically grounded agents, or quale parsing. DACtoc hypothesizes that consciousness in both its primary and secondary forms serves the ability to deal with the hidden states of the world and emerged during the Cambrian period, affording stable multi-agent environments to emerge. The process of consciousness is an autonomous virtualization memory, which serializes and unifies the parallel and subconscious simulations of the hidden states of the world that are largely due to other agents and the self with the objective to extract norms. These norms are in turn projected as value onto the parallel simulation and control systems that are driving action. This functional hypothesis is mapped onto the brainstem, midbrain and the thalamo-cortical and cortico-cortical systems and analysed with respect to our understanding of deficits of consciousness. Subsequently, some of the implications and predictions of DACtoc are outlined, in particular, the prediction that normative bootstrapping of conscious agents is predicated on an intentionality prior. In the view advanced here, human consciousness constitutes the ultimate evolutionary transition by allowing agents to become autonomous with respect to their evolutionary priors leading to a post-biological Anthropocene.This article is part of the themed issue 'The major synthetic evolutionary transitions'.

show abstract

Stimulus onset quenches neural variability: a widespread cortical phenomenon

Cited by 999 publications

References 48 publications

Stimulus-dependent variability and noise correlations in cortical MT neurons

Stimulus-dependent variability and noise correlations in cortical MT neurons

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

Synthetic consciousness: the distributed adaptive control perspective

Contact Info

Product

Resources

About