Population Coding of Stimulus Location in Rat Somatosensory Cortex

Petersen, Rasmus S.; Panzeri, Stefano; Diamond, Mathew E.

doi:10.1016/s0896-6273(01)00481-0

Cited by 219 publications

(181 citation statements)

References 52 publications

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“…Here we solidify this identification by showing that ⌬I is a rigorous upper bound on the information loss. This result is useful because it allows us to interpret the spate of recent experiments in which ⌬I/I was found to be on the order of 10% or less (Nirenberg et al, 2001;Petersen et al, 2001Petersen et al, , 2002Panzeri et al, 2002b;Pola et al, 2003): in those experiments, one could ignore correlations when decoding and lose at most ϳ10% of the information.…”

Section: Redundancy Reductionmentioning

confidence: 92%

“…Several authors (Eckhorn et al, 1988;Meister et al, 1995;Vaadia et al, 1995;deCharms and Merzenich, 1996;Dan et al, 1998;Steinmetz et al, 2000) have suggested that they are, whereas others (Nirenberg et al, 2001;Oram et al, 2001;Petersen et al, 2001;Levine et al, 2002;Panzeri et al, 2002a,b;Averbeck and Lee, 2003;Golledge et al, 2003) have argued that they are not or that they play a minor role. The debate has arisen in large part because different methods have been used to assess the role of correlations, and different methods yield different answers.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Synergy, Redundancy, and Independence in Population Codes, Revisited

Latham¹,

Nirenberg²

2005

J. Neurosci.

206

239

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Decoding the activity of a population of neurons is a fundamental problem in neuroscience. A key aspect of this problem is determining whether correlations in the activity, i.e., noise correlations, are important. If they are important, then the decoding problem is high dimensional: decoding algorithms must take the correlational structure in the activity into account. If they are not important, or if they play a minor role, then the decoding problem can be reduced to lower dimension and thus made more tractable. The issue of whether correlations are important has been a subject of heated debate. The debate centers around the validity of the measures used to address it. Here, we evaluate three of the most commonly used ones: synergy, ⌬I shuffled , and ⌬I. We show that synergy and ⌬I shuffled are confounded measures: they can be zero when correlations are clearly important for decoding and positive when they are not. In contrast, ⌬I is not confounded. It is zero only when correlations are not important for decoding and positive only when they are; that is, it is zero only when one can decode exactly as well using a decoder that ignores correlations as one can using a decoder that does not, and it is positive only when one cannot decode as well. Finally, we show that ⌬I has an information theoretic interpretation; it is an upper bound on the information lost when correlations are ignored.

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Section: Redundancy Reductionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Synergy, Redundancy, and Independence in Population Codes, Revisited

Latham¹,

Nirenberg²

2005

J. Neurosci.

206

239

View full text Add to dashboard Cite

show abstract

“…Although ⌬I shuff led cannot be used to assess directly the role of correlations in decoding information, it can be used to provide information about how correlations affect the transformation from stimulus to response (21,31,33). In particular, the quantity ⌬I shuff led Ϫ ⌬I is especially useful for understanding the relation between signal and noise correlations (21).…”

Section: Other Measures Of the Importance Of Correlationsmentioning

confidence: 99%

Decoding neuronal spike trains: How important are correlations?

Nirenberg

Latham²

2003

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

199

208

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It has been known for >30 years that neuronal spike trains exhibit correlations, that is, the occurrence of a spike at one time is not independent of the occurrence of spikes at other times, both within spike trains from single neurons and across spike trains from multiple neurons. The presence of these correlations has led to the proposal that they might form a key element of the neural code. Specifically, they might act as an extra channel for information, carrying messages about events in the outside world that are not carried by other aspects of the spike trains, such as firing rate. Currently, there is no general consensus about whether this proposal applies to real spike trains in the nervous system. This is largely because it has been hard to separate information carried in correlations from that not carried in correlations. Here we propose a framework for performing this separation. Specifically, we derive an information-theoretic cost function that measures how much harder it is to decode neuronal responses when correlations are ignored than when they are taken into account. This cost function can be readily applied to real neuronal data. E ver since Adrian and Zotterman observed that the firing rate of peripheral touch receptors coded for the pressure applied to a patch of skin (1), neuroscientists have been trying to crack the neural code, that is, to understand the relationship between neuronal activity and events in the outside world. For much of that time, the working hypothesis was that information is carried by firing rate. More recently it has been proposed that firing rate is not the whole story: Information might also be carried in spike patterns, both within spike trains from single neurons (2-6) and across spike trains from multiple neurons (7-9).One aspect of this proposal, an aspect that has led to a great deal of debate, is that correlations in spike patterns may be of particular importance (7)(8)(9)(10)12). It has been known for many years that spike trains contain correlations; that is, the presence of a spike at one time is not independent of the presence of spikes at other times. These correlations exist not just within spike trains but across them as well, with the most common example being synchronous spikes across pairs of cells (13)(14)(15)(16). What has led to the debate is the suggestion that these correlations might form a key aspect of the code. The idea is that they might serve as an extra information channel, conveying messages not carried elsewhere in the spike trains.How might the correlations do this? An example, using synchronous spikes, is shown in Fig. 1a. In this example there are two stimuli, A and B, and two neurons. When stimulus A is presented, the two neurons produce five spikes on average. When stimulus B is presented, they also produce five spikes on average. What is different, though, is the correlational structure of the responses: When stimulus A is presented, the two neurons tend to produce few synchronous spikes, whereas when stimulus B is presented, they ...

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“…However, most studies quantifying first-spike latency information assume that the brain has an independent reference for stimulus onset from which to extract latency. In the majority of situations, this independent onset reference does not exist; the need for a timing reference has caused some to question the ultimate feasibility of first-spike latency codes (8).A number of authors have suggested possible alternative latency measures (1,3,5,6), but few have actually compared the information contained in different onset references. Stecker and Middlebrooks (9) computed the information contained in the relative spike timing of pairs of simultaneously recorded neurons in auditory cortex, and Furukawa et al (10) compared the median errors from neural-network estimates of location with similar data.…”

mentioning

confidence: 99%

“…A number of authors have suggested possible alternative latency measures (1,3,5,6), but few have actually compared the information contained in different onset references. Stecker and Middlebrooks (9) computed the information contained in the relative spike timing of pairs of simultaneously recorded neurons in auditory cortex, and Furukawa et al (10) compared the median errors from neural-network estimates of location with similar data.…”

mentioning

confidence: 99%

First-spike latency information in single neurons increases when referenced to population onset

Chase

Young

2007

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

168

147

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It is well known that many stimulus parameters, such as sound location in the auditory system or contrast in the visual system, can modulate the timing of the first spike in sensory neurons. Could first-spike latency be a candidate neural code? Most studies measuring first-spike latency information assume that the brain has an independent reference for stimulus onset from which to extract latency. This assumption creates an obvious confound that casts doubt on the feasibility of first-spike latency codes. If latency is measured relative to an internal reference of stimulus onset calculated from the responses of the neural population, the information conveyed by the latency of single neurons might decrease because of correlated changes in latency across the population. Here we assess the effects of a realistic model of stimulus onset detection on the first-spike latency information conveyed by single neurons in the auditory system. Contrary to expectation, we find that on average, the information contained in single neurons does not decrease; in fact, the majority of neurons show a slight increase in the information conveyed by latency referenced to a population onset. Our results show that first-spike latency codes are a feasible mechanism for information transfer even when biologically plausible estimates of stimulus onset are taken into account.coding ͉ inferior colliculus ͉ mutual information ͉ sound localization T he first-spike latency has been shown to carry information in several sensory modalities, including the auditory (1, 2), visual (3, 4), and somatosensory (5-7) systems. However, most studies quantifying first-spike latency information assume that the brain has an independent reference for stimulus onset from which to extract latency. In the majority of situations, this independent onset reference does not exist; the need for a timing reference has caused some to question the ultimate feasibility of first-spike latency codes (8).A number of authors have suggested possible alternative latency measures (1,3,5,6), but few have actually compared the information contained in different onset references. Stecker and Middlebrooks (9) computed the information contained in the relative spike timing of pairs of simultaneously recorded neurons in auditory cortex, and Furukawa et al. (10) compared the median errors from neural-network estimates of location with similar data. In both cases, performance with relative-latency measures was worse than with an independent onset reference, presumably because using a single neuron as the onset reference increases the overall measurement jitter. Other authors (11-13) have investigated rank order codes, where information is conveyed by the relative order in which neurons fire. Jenison (14) has shown by using modeling and maximum likelihood techniques that correlation can, in principle, increase the information available in first-spike latency, provided the decoder knows the correlation structure. However, such location estimates get noisier when stimulus onset is estimate...

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Population Coding of Stimulus Location in Rat Somatosensory Cortex

Cited by 219 publications

References 52 publications

Synergy, Redundancy, and Independence in Population Codes, Revisited

Synergy, Redundancy, and Independence in Population Codes, Revisited

Decoding neuronal spike trains: How important are correlations?

First-spike latency information in single neurons increases when referenced to population onset

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