An exact method to quantify the information transmitted by different mechanisms of correlational coding

Pola, Giordano; Thiele, Alexander; Kp, Hoffmann; Panzeri, Stefano

doi:10.1088/0954-898x/14/1/303

Cited by 156 publications

(286 citation statements)

References 38 publications

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“…However, the absolute MI values might be biased. To access redundancy, synergy, and independence of information (Pola et al 2003;Schneidman et al 2003) conveyed by two features F 1 and F 2 about the spiking activity S, we estimate mutual information using two classifiers trained on features F 1 and F 2 individually, yielding I(S; F 1 ):ϭ I[S; C(F 1 )] and I(S; F 2 ) :ϭ I[S; C(F 2 )]. Then a third classifier is trained on both features jointly, yielding I(S; F 1 , F 2 ) :ϭ I[S; C(F 1 , F 2 )].…”

Section: I͓smentioning

confidence: 99%

Inferring Spike Trains From Local Field Potentials

Rasch¹,

Gretton²,

Murayama³

et al. 2008

Journal of Neurophysiology

205

200

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We investigated whether it is possible to infer spike trains solely on the basis of the underlying local field potentials (LFPs). Using support vector machines and linear regression models, we found that in the primary visual cortex (V1) of monkeys, spikes can indeed be inferred from LFPs, at least with moderate success. Although there is a considerable degree of variation across electrodes, the low-frequency structure in spike trains (in the 100-ms range) can be inferred with reasonable accuracy, whereas exact spike positions are not reliably predicted. Two kinds of features of the LFP are exploited for prediction: the frequency power of bands in the high ␥-range (40 -90 Hz) and information contained in lowfrequency oscillations (Ͻ10 Hz), where both phase and power modulations are informative. Information analysis revealed that both features code (mainly) independent aspects of the spike-to-LFP relationship, with the low-frequency LFP phase coding for temporally clustered spiking activity. Although both features and prediction quality are similar during seminatural movie stimuli and spontaneous activity, prediction performance during spontaneous activity degrades much more slowly with increasing electrode distance. The general trend of data obtained with anesthetized animals is qualitatively mirrored in that of a more limited data set recorded in V1 of non-anesthetized monkeys. In contrast to the cortical field potentials, thalamic LFPs (e.g., LFPs derived from recordings in the dorsal lateral geniculate nucleus) hold no useful information for predicting spiking activity.

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Section: I͓smentioning

confidence: 99%

Inferring Spike Trains From Local Field Potentials

Rasch¹,

Gretton²,

Murayama³

et al. 2008

Journal of Neurophysiology

205

200

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“…7 was first derived by Nirenberg et al (20). More recently, Pola et al (21) derived an identical expression based on slightly different considerations; they called it I corϪdep rather than ⌬I.…”

Section: [7]mentioning

confidence: 99%

Decoding neuronal spike trains: How important are correlations?

Nirenberg

Latham²

2003

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

198

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, an exact breakdown approach allowed a very similar information component approach to be used (Pola et al, 2003). In this approach, we consider a population response (spike pattern) r, and define the normalized noise cross--correlation to be…”

Section: A More General Information Component Analysis Approachmentioning

confidence: 99%

“…However, the components can also be written as a sum of entropies and entropy--like quantities, which can then be computed using the entropy estimation algorithms described earlier in this chapter (Pola et al, 2003;Montani et al, 2007;Schultz et al, 2009). Note that, as shown by Scaglione and colleagues (Scaglione et al, 2008(Scaglione et al, , 2010) the components in Eq.…”

Section: A More General Information Component Analysis Approachmentioning

confidence: 99%

Estimating Information-Theoretic Quantities

Ince¹,

Schultz²,

Panzeri

2015

Encyclopedia of Computational Neuroscience

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(Tn) Italy16 pages 5724 words. DefinitionInformation theory is a practical and theoretical framework developed for the study of communication over noisy channels. Its probabilistic basis and capacity to relate statistical structure to function make it ideally suited for studying information flow in the nervous system. It has a number of useful properties: it is a general measure sensitive to any relationship, not only linear effects; it has meaningful units which in many cases allow direct comparison between different experiments; and it can be used to study how much information can be gained by observing neural responses in single trials, rather than in averages over multiple trials. A variety of information theoretic quantities are in common use in neuroscience -(see entry "Summary of Information--Theoretic Quantities"). Estimating these quantities in an accurate and unbiased way from real neurophysiological data frequently presents challenges, which are explained in this entry.

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An exact method to quantify the information transmitted by different mechanisms of correlational coding

Cited by 156 publications

References 38 publications

Inferring Spike Trains From Local Field Potentials

Inferring Spike Trains From Local Field Potentials

Decoding neuronal spike trains: How important are correlations?

Estimating Information-Theoretic Quantities

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