Weak pairwise correlations imply strongly correlated network states in a neural population

Schneidman, Elad; Berry, Michael; Segev, Ronen; Bialek, William

doi:10.1038/nature04701

Cited by 1,607 publications

(2,480 citation statements)

References 41 publications

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“…Such input most likely contributes to experimentally observed correlations between the activities of neighboring neurons across sensory systems (Meister et al 1995;Averbeck and Lee 2006). While there is still an ongoing debate on whether these correlations are beneficial or detrimental for information transmission, it is widely agreed that their presence will significantly alter the way that information is transmitted by neural populations and its subsequent decoding by higherorder neurons (Nirenberg et al 2001;Schneidman et al 2003Schneidman et al , 2006Latham and Nirenberg 2005;. How nonrenewal spike train statistics affect correlated activity among neurons is an interesting topic that has been largely overlooked so far.…”

Section: Nonrenewal Spike Train Statistics and Population Codingmentioning

confidence: 99%

Nonrenewal spike train statistics: causes and functional consequences on neural coding

Ávila-Ǻkerberg

Chacron

2011

Exp Brain Res

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Many neurons display significant patterning in their spike trains (e.g. oscillations, bursting), and there is accumulating evidence that information is contained in these patterns. In many cases, this patterning is caused by intrinsic mechanisms rather than external signals. In this review, we focus on spiking activity that displays nonrenewal statistics (i.e. memory that persists from one firing to the next). Such statistics are seen in both peripheral and central neurons and appear to be ubiquitous in the CNS. We review the principal mechanisms that can give rise to nonrenewal spike train statistics. These are separated into intrinsic mechanisms such as relative refractoriness and network mechanisms such as coupling with delayed inhibitory feedback. Next, we focus on the functional roles for nonrenewal spike train statistics. These can either increase or decrease information transmission. We also focus on how such statistics can give rise to an optimal integration timescale at which spike train variability is minimal and how this might be exploited by sensory systems to maximize the detection of weak signals. We finish by pointing out some interesting future directions for research in this area. In particular, we explore the interesting possibility that synaptic dynamics might be matched with the nonrenewal spiking statistics of presynaptic spike trains in order to further improve information transmission.

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Section: Nonrenewal Spike Train Statistics and Population Codingmentioning

confidence: 99%

Nonrenewal spike train statistics: causes and functional consequences on neural coding

Ávila-Ǻkerberg

Chacron

2011

Exp Brain Res

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“…With increased oscillatory synaptic inputs and/or soma- todendritic coupling there would be increased probability and power of oscillatory spike activity. Even in the cases where there was only a small number of oscillatory neurons, their activity could still produce a significant effect on STN target nuclei because a weak correlation among very many neurons could add up to become prominent at the population level (Schneidman et al 2006).…”

Section: Discussionmentioning

confidence: 99%

Beta Oscillatory Activity in the Subthalamic Nucleus and Its Relation to Dopaminergic Response in Parkinson's Disease

Weinberger

Mahant²,

Hutchison³

et al. 2006

Journal of Neurophysiology

540

389

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. Beta oscillatory activity in the subthalamic nucleus and its relation to dopaminergic response in Parkinson's disease. J Neurophysiol 96: 3248 -3256, 2006. First published September 27, 2006 doi:10.1152/jn.00697.2006. Recent studies suggest that beta (15-30 Hz) oscillatory activity in the subthalamic nucleus (STN) is dramatically increased in Parkinson's disease (PD) and may interfere with movement execution. Dopaminergic medications decrease beta activity and deep brain stimulation (DBS) in the STN may alleviate PD symptoms by disrupting this oscillatory activity. Depth recordings from PD patients have demonstrated beta oscillatory neuronal and local field potential (LFP) activity in STN, although its prevalence and relationship to neuronal activity are unclear. In this study, we recorded both LFP and neuronal spike activity from the STN in 14 PD patients during functional neurosurgery. Of 200 singleand multiunit recordings 56 showed significant oscillatory activity at about 26 Hz and 89% of these were coherent with the simultaneously recorded LFP. The incidence of neuronal beta oscillatory activity was significantly higher in the dorsal STN (P ϭ 0.01) and corresponds to the significantly increased LFP beta power recorded in the same region. Of particular interest was a significant positive correlation between the incidence of oscillatory neurons and the patient's benefit from dopaminergic medications, but not with baseline motor deficits off medication. These findings suggest that the degree of neuronal beta oscillatory activity is related to the magnitude of the response of the basal ganglia to dopaminergic agents rather than directly to the motor symptoms of PD. The study also suggests that LFP beta oscillatory activity is generated largely within the dorsal portion of the STN and can produce synchronous oscillatory activity of the local neuronal population.

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“…Covariance measurements are also important when examining how sensory information is represented. This is true both when relative spike timing is hypothesized to convey information [e.g., 17], and in the more traditional case of rate coding, where correlations may affect the accuracy of the representation [18][19][20][21]. Although many of the above issues can be addressed via recordings from multiple conventional electrodes [e.g., 22], implanted-arrays have an advantage both in isolation stability and in scale (the number of comparable pairs scales as the square of the number of recorded neurons).…”

Section: General Motivations For Implanted-array Recordingmentioning

confidence: 99%

Techniques for extracting single-trial activity patterns from large-scale neural recordings

Churchland

Sahani

et al. 2007

Current Opinion in Neurobiology

142

115

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SummaryLarge, chronically-implanted arrays of microelectrodes are an increasingly common tool for recording from primate cortex, and can provide extracellular recordings from many (order of 100) neurons. While the desire for cortically-based motor prostheses has helped drive their development, such arrays also offer great potential to advance basic neuroscience research. Here we discuss the utility of array recording for the study of neural dynamics. Neural activity often has dynamics beyond that driven directly by the stimulus. While governed by those dynamics, neural responses may nevertheless unfold differently for nominally identical trials, rendering many traditional analysis methods ineffective. We review recent studies -some employing simultaneous recording, some not -indicating that such variability is indeed present both during movement generation, and during the preceding premotor computations. In such cases, large-scale simultaneous recordings have the potential to provide an unprecedented view of neural dynamics at the level of single trials. However, this enterprise will depend not only on techniques for simultaneous recording, but also on the use and further development of analysis techniques that can appropriately reduce the dimensionality of the data, and allow visualization of single-trial neural behavior.

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Weak pairwise correlations imply strongly correlated network states in a neural population

Cited by 1,607 publications

References 41 publications

Nonrenewal spike train statistics: causes and functional consequences on neural coding

Nonrenewal spike train statistics: causes and functional consequences on neural coding

Beta Oscillatory Activity in the Subthalamic Nucleus and Its Relation to Dopaminergic Response in Parkinson's Disease

Techniques for extracting single-trial activity patterns from large-scale neural recordings

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