Effects of Noise Correlations on Information Encoding and Decoding

Averbeck, Bruno B.; Lee, Dongsoo

doi:10.1152/jn.00919.2005

Cited by 210 publications

(255 citation statements)

References 67 publications

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“…Indeed, cortical neurons (Engel et al 2008), cochlear nuclear neurons (Goldberg and Greenwood 1966) as well as electrosensory pyramidal cells (Chacron and Bastian 2008) all receive thousands of synaptic input and display nonrenewal spike train statistics. 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;.…”

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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“…Specifically, the structure of correlated noise in vestibular afferents is very difficult to measure (but refer to ref. the neural population (38)(39)(40)(41)(42)(43)(44). When correlated noise is present, as is the case in the VN/CN (13,35), population thresholds may not decrease with the square root of the number of neurons and predictions based on the square root law could be dramatically inaccurate.…”

Section: Discussionmentioning

confidence: 99%

Neuronal thresholds and choice-related activity of otolith afferent fibers during heading perception

Dickman

DeAngelis

et al. 2015

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

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How activity of sensory neurons leads to perceptual decisions remains a challenge to understand. Correlations between choices and single neuron firing rates have been found early in vestibular processing, in the brainstem and cerebellum. To investigate the origins of choice-related activity, we have recorded from otolith afferent fibers while animals performed a fine heading discrimination task. We find that afferent fibers have similar discrimination thresholds as central cells, and the most sensitive fibers have thresholds that are only twofold or threefold greater than perceptual thresholds. Unlike brainstem and cerebellar nuclei neurons, spike counts from afferent fibers do not exhibit trial-by-trial correlations with perceptual decisions. This finding may reflect the fact that otolith afferent responses are poorly suited for driving heading perception because they fail to discriminate self-motion from changes in orientation relative to gravity. Alternatively, if choice probabilities reflect top-down inference signals, they are not relayed to the vestibular periphery.neuronal threshold | choice probability | psychophysical threshold | otolith afferent | heading discrimination T he neural basis of perception holds a long-standing fascination for neuroscientists. How do the properties of single neurons and populations of neurons relate to, and account for, sensory perception? In all sensory systems, information about the world is first translated into neural activity by peripheral receptor neurons, and then transformed by multiple stages of subcortical and cortical processing into a perceptual decision. How and where does perception emerge from multiple neural representations that appear to be at least partially redundant? These questions have been addressed often in sensory and multisensory cortex (e.g., in refs. 1-3 for vestibular perception), but much less is known about how the activity of sensory afferents relates to perceptual sensitivity and perceptual decisions.One way to assess a potential role of sensory neurons in a perceptual task is to compare neuronal and perceptual sensitivity, measured simultaneously in the same subject (4). Although this comparison has been done many times for cortical neurons (1, 5-7), little is known about how the sensitivity of peripheral afferents compares with behavior apart from microneurography studies of tactile afferents in humans (8, 9). To our knowledge, the present study provides the first direct comparison of afferent neuronal sensitivity and perceptual sensitivity, measured simultaneously in experimental animals. Results of such comparisons have important implications for understanding how population encoding and decoding may constrain and shape the information that guides behavior.Another way that neuroscientists have explored the functional links between sensory neurons and perception is by measuring the trial-by-trial correlations between neural activity and perceptual decisions, which are typically quantified as "choice probabilities" (CPs) (10). The "bottom-...

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“…Correlation may be helpful or harmful depending on stimulus statistics, noise statistics, population size, and decoding mechanisms (25)(26)(27)(28)(29)(30)(31). Here we focus on how correlations influence two primary functions of spike trains: encoding information and propagating activity to downstream neurons.…”

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confidence: 99%

“…Correlated spiking facilitates propagation (30,(32)(33)(34). However, these correlations reduce the available repertoire of population activity patterns (31,35,36), thereby potentially impairing sensory discriminations (27). Because correlations can impact propagation and encoding in opposing ways, how do networks structure their correlations?…”

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confidence: 99%

Timescale-dependent shaping of correlation by olfactory bulb lateral inhibition

Giridhar

Doiron

Urban

2011

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

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Neurons respond to sensory stimuli by altering the rate and temporal pattern of action potentials. These spike trains both encode and propagate information that guides behavior. Local inhibitory networks can affect the information encoded and propagated by neurons by altering correlations between different spike trains. Correlations introduce redundancy that can reduce encoding but also facilitate propagation of activity to downstream targets. Given this trade-off, how can networks maximize both encoding and propagation efficacy? Here, we examine this problem by measuring the effects of olfactory bulb inhibition on the pairwise statistics of mitral cell spiking. We evoked spiking activity in the olfactory bulb in vitro and measured how lateral inhibition shapes correlations across timescales. We show that inhibitory circuits simultaneously increase fast correlation (i.e., synchrony increases) and decrease slow correlation (i.e., firing rates become less similar). Further, we use computational models to show the benefits of fast correlation/slow decorrelation in the context of odor coding. Olfactory bulb inhibition enhances population-level discrimination of similar inputs, while improving propagation of mitral cell activity to cortex. Our findings represent a targeted strategy by which a network can optimize the correlation structure of its output in a dynamic, activity-dependent manner. This trade-off is not specific to the olfactory system, but rather our work highlights mechanisms by which neurons can simultaneously accomplish multiple, and sometimes competing, aspects of sensory processing.

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Effects of Noise Correlations on Information Encoding and Decoding

Cited by 210 publications

References 67 publications

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

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

Neuronal thresholds and choice-related activity of otolith afferent fibers during heading perception

Timescale-dependent shaping of correlation by olfactory bulb lateral inhibition

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