Neural correlations, population coding and computation

Averbeck, Bruno B.; Latham, Peter E.; Pouget, Alexandre

doi:10.1038/nrn1888

Cited by 1,495 publications

(1,564 citation statements)

References 67 publications

Supporting

Mentioning

1,494

Contrasting

Unclassified

Order By: Relevance

“…AC had relatively weak coupling and a short timescale (hundreds of milliseconds), which might aid representations of rapidly fluctuating stimuli and high dimensional sensory features. Previous studies have proposed that noise correlations can have a detrimental, information-limiting effect 23,26,29 and have thus suggested that sensory codes may benefit from weak coupling, which appears consistent with our findings in AC. However, in contrast, PPC had strong coupling and a long population timescale (~1 s), which appear to have a beneficial effect because higher levels of coupling and temporal information consistency corresponded to accurate task performance.…”

supporting

confidence: 92%

“…To examine the structure of functional interactions in population activity 18,23–27 , we modified our encoding model to predict a given neuron’s activity based on the past activity of each of the other imaged neurons (“coupling predictors”; activity from up to ~2 s in the past, within defined lag ranges). These coupling predictors were included in a single model along with the task-related predictors described above 20 (“task predictors”; Fig.…”

mentioning

confidence: 99%

“…Higher coupling indices indicated that other neurons’ activity provided greater improvement in the prediction of a neuron’s moment-to-moment activity patterns beyond what could be modeled with task features alone. Coupling is conceptually similar to “noise” correlation 23 , but it discounts, on a trial-by-trial basis, effects arising from shared tuning to measured task events and includes all other simultaneously imaged neurons.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Distinct timescales of population coding across cortex

Runyan

Piasini

Panzeri

et al. 2017

Nature

318

429

View full text Add to dashboard Cite

The cortex represents information across widely varying timescales1–5. For instance, sensory cortex encodes stimuli that fluctuate over few tens of milliseconds6,7, whereas in association cortex behavioral choices can require the maintenance of information over seconds8,9. However, it remains poorly understood if diverse timescales result mostly from features intrinsic to individual neurons or from neuronal population activity. This question is unanswered because the timescales of coding in populations of neurons have not been studied extensively, and population codes have not been compared systematically across cortical regions. Here we discovered that population codes can be essential to achieve long coding timescales. Furthermore, we found that the properties of population codes differ between sensory and association cortices. We compared coding for sensory stimuli and behavioral choices in auditory cortex (AC) and posterior parietal cortex (PPC) as mice performed a sound localization task. Auditory stimulus information was stronger in AC than in PPC, and both regions contained choice information. Although AC and PPC coded information by tiling in time neurons that were transiently informative for ~200 milliseconds, the areas had major differences in functional coupling between neurons, measured as activity correlations that could not be explained by task events. Coupling among PPC neurons was strong and extended over long time lags, whereas coupling among AC neurons was weak and short-lived. Stronger coupling in PPC led to a population code with long timescales and a representation of choice that remained consistent for approximately one second. In contrast, AC had a code with rapid fluctuations in stimulus and choice information over hundreds of milliseconds. Our results reveal that population codes differ across cortex and that coupling is a variable property of cortical populations that affects the timescale of information coding and the accuracy of behavior.

show abstract

supporting

confidence: 92%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Distinct timescales of population coding across cortex

Runyan

Piasini

Panzeri

et al. 2017

Nature

318

429

View full text Add to dashboard Cite

show abstract

“…Several measures, such as Mutual Information, Fischer information and coherence (for an overview, see, e.g. Averbeck et al 2006), have been used to quantify the amount of information in the input and output. These studies have provided a thorough overview of the neuronal properties and of the neural encoding of information that is critical for reliable information transmission (see, e.g.…”

Section: Introductionmentioning

confidence: 99%

Gamma oscillations as a mechanism for selective information transmission

2010

View full text Add to dashboard Cite

In the past decades, many studies have focussed on the relation between the input and output of neurons with the aim to understand information processing by neurons. A particular aspect of neuronal information, which has not received much attention so far, concerns the problem of information transfer when a neuron or a population of neurons receives input from two or more (populations of) neurons, in particular when these (populations of) neurons carry different types of information. The aim of the present study is to investigate the responses of neurons to multiple inputs modulated in the gamma frequency range. By a combination of theoretical approaches and computer simulations, we test the hypothesis that enhanced modulation of synchronized excitatory neuronal activity in the gamma frequency range provides an advantage over a less synchronized input for various types of neurons. The results of this study show that the spike output of various types of neurons [i.e. the leaky integrate and fire neuron, the quadratic integrate and fire neuron and the Hodgkin-Huxley (HH) neuron] and that of excitatory-inhibitory coupled pairs of neurons, like the Pyramidal Interneuronal Network Gamma (PING) model, is highly phase-locked to the larger of two gamma-modulated input signals. This implies that the neuron selectively responds to the input with the larger gamma modulation if the amplitude of the gamma modulation exceeds that of the other signals by a certain amount. In that case, the output of the neuron is entrained by one of multiple inputs and that other inputs are not represented in the output. This mechanism for selective information transmission is enhanced for short membrane time constants of the neuron.

show abstract

“…Furthermore, Averbeck and colleagues investigated how neural activity in PFC evolves as the monkey gradually acquires knowledge of the correct movement sequence and found that the sequence predicted by neuronal activity changes gradually from the sequence that had been correct in the previous block to the sequence that is correct in the current block. They showed that the information coded in the ensemble neuronal activity about the correct sequence increases as the monkey discovers it [62] .…”

mentioning

confidence: 99%

Anticipatory activity in rat medial prefrontal cortex during a working memory task

Bai

Liu

et al. 2012

Neurosci. Bull.

View full text Add to dashboard Cite

Objective Working memory is a key cognitive function in which the prefrontal cortex plays a crucial role.This study aimed to show the firing patterns of a neuronal population in the prefrontal cortex of the rat in a working memory task and to explore how a neuronal ensemble encodes a working memory event. Methods Sprague-Dawley rats were trained in a Y-maze until they reached an 80% correct rate in a working memory task. Then a 16-channel microelectrode array was implanted in the prefrontal cortex. After recovery, neuronal population activity was recorded during the task, using the Cerebus data-acquisition system. Spatio-temporal trains of action potentials were obtained from the original neuronal population signals. Results During the Y-maze working memory task, some neurons showed significantly increased firing rates and evident neuronal ensemble activity. Moreover, the anticipatory activity was associated with the delayed alternate choice of the upcoming movement. In correct trials, the averaged pre-event firing rate (10.86 ± 1.82 spikes/ bin) was higher than the post-event rate (8.17 ± 1.15 spikes/bin) (P <0.05). However, in incorrect trials, the rates did not differ. Conclusion The results indicate that the anticipatory activity of a neuronal ensemble in the prefrontal cortex may play a role in encoding working memory events.

show abstract

Neural correlations, population coding and computation

Cited by 1,495 publications

References 67 publications

Distinct timescales of population coding across cortex

Distinct timescales of population coding across cortex

Gamma oscillations as a mechanism for selective information transmission

Anticipatory activity in rat medial prefrontal cortex during a working memory task

Contact Info

Product

Resources

About