Entropy and Information in Neural Spike Trains

Strong, S. P.; Köberle, R.; Steveninck, Rob R. de Ruyter van; Bialek, William

doi:10.1103/physrevlett.80.197

Cited by 869 publications

(1,063 citation statements)

References 20 publications

(19 reference statements)

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“…This is in contrast to the emphasis usually put on the spike train distances. In particular, the use of a metric specific for point processes like the Victor distance (Victor and Purpura, 1996) or a metric in an Euclidean space like for the binning distances is not so determinant for the discrimination performance as the classifier, given that in this case the binning procedure is not used to construct a high-dimensional space for the probability distributions (Strong et al, 1998). Notice that here we restricted ourselves to the type of classifier originally proposed by Victor and Purpura (1996), which has been also the most used in studies applying the discrimination analysis.…”

Section: Discussionmentioning

confidence: 99%

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What can spike train distances tell us about the neural code?

Chicharro

Kreuz

Andrzejak

2011

Journal of Neuroscience Methods

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Time scale parametric spike train distances like the Victor and the van Rossum distances are often applied to study the neural code based on neural stimuli discrimination. Different neural coding hypotheses, such as rate or coincidence coding, can be assessed by combining a time scale parametric spike train distance with a classifier in order to obtain the optimal discrimination performance. The time scale for which the responses to different stimuli are distinguished best is assumed to be the discriminative precision of the neural code. The relevance of temporal coding is evaluated by comparing the optimal discrimination performance with the one achieved when assuming a rate code.We here characterize the measures quantifying the discrimination performance, the discriminative precision, and the relevance of temporal coding. Furthermore, we evaluate the information these quantities provide about the neural code. We show that the discriminative precision is too unspecific to be interpreted in terms of the time scales relevant for encoding. Accordingly, the time scale parametric nature of the distances is mainly an advantage because it allows maximizing the discrimination performance across a whole set of measures with different sensitivities determined by the time scale parameter, but not due to the possibility to examine the temporal properties of the neural code.

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Section: Discussionmentioning

confidence: 99%

“…To check to which degree binning is inconvenient by itself or becomes problematic only when used to construct a high dimensional R space (e. g. Strong et al, 1998), we also study a spike train distance based on binning (Schnupp et al, 2006). For each spike train, the spikes are binned in N B bins of width τ B .…”

Section: Binning Distance D Bmentioning

confidence: 99%

What can spike train distances tell us about the neural code?

Chicharro

Kreuz

Andrzejak

2011

Journal of Neuroscience Methods

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“…Current entropy rate estimation methods (e.g., Strong et al 1998) proceed by substituting the observed relative frequenciesp i for p i in Eq. 2, and subsequently extrapolating the finite l behavior of H (l)/l when l goes to infinite (i.e., when 1/l goes to 0).…”

Section: The Entropy Rate and The Lempel-ziv Estimatormentioning

confidence: 99%

Mutual information and redundancy in spontaneous communication between cortical neurons

et al. 2011

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An important question in neural information processing is how neurons cooperate to transmit information. To study this question, we resort to the concept of redundancy in the information transmitted by a group of neurons and, at the same time, we introduce a novel concept for measuring cooperation between pairs of neurons called relative mutual information (RMI). Specifically, we studied these two parameters for spike trains generated by neighboring neurons from the primary visual cortex in the awake, freely moving rat. The spike trains studied here were spontaneously generated in the cortical network, in the absence of visual stimulation. Under these conditions, our analysis revealed that while the value of RMI oscillated slightly around an average value, the redundancy exhibited a behavior character- ized by a higher variability. We conjecture that this combination of approximately constant RMI and greater variable redundancy makes information transmission more resistant to noise disturbances. Furthermore, the redundancy values suggest that neurons can cooperate in a flexible way during information transmission. This mostly occurs via a leading neuron with higher transmission rate or, less frequently, through the information rate of the whole group being higher than the sum of the individual information rates-in other words in a synergetic manner. The proposed method applies not only to the stationary, but also to locally stationary neural signals.

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“…The methods used to compute this measure are based on the difference between the response entropy rate H(R) and the response entropy rate given the stimulus H(R/S) and have been described in detail elsewhere (Strong et al, 1998;Chacron et al, 2001bChacron et al, , 2003a.…”

Section: Information Theory and Stimulus Estimationmentioning

confidence: 99%

“…The direct method of estimating information (Strong et al, 1998) will provide such an estimate. Alternatively, one can use the indirect method to obtain a lower bound on information transfer since this measure relies on linear decoding (Gabbiani, 1996;Rieke et al, 1996).…”

Section: Information Theory: Linear Versus Nonlinear Codingmentioning

confidence: 99%

To Burst or Not to Burst?

2004

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It is well known that some neurons tend to fire packets of action potentials followed by periods of quiescence (bursts) while others within the same stage of sensory processing fire in a tonic manner. However, the respective computational advantages of bursting and tonic neurons for encoding time varying signals largely remain a mystery. Weakly electric fish use cutaneous electroreceptors to convey information about sensory stimuli and it has been shown that some electroreceptors exhibit bursting dynamics while others do not. In this study, we compare the neural coding capabilities of tonically firing and bursting electroreceptor model neurons using information theoretic measures. We find that both bursting and tonically firing model neurons efficiently transmit information about the stimulus. However, the decoding mechanisms that must be used for each differ greatly: a non-linear decoder would be required to extract all the available information transmitted by the bursting model neuron whereas a linear one might suffice for the tonically firing model neuron. Further investigations using stimulus reconstruction techniques reveal that, unlike the tonically firing model neuron, the bursting model neuron does not encode the detailed time course of the stimulus. A novel measure of feature detection reveals that the bursting neuron signals certain stimulus features. Finally, we show that feature extraction and stimulus estimation are mutually exclusive computations occurring in bursting and tonically firing model neurons, respectively. Our results therefore suggest that stimulus estimation and feature extraction might be parallel computations in certain sensory systems rather than being sequential as has been previously proposed.

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Entropy and Information in Neural Spike Trains

Cited by 869 publications

References 20 publications

What can spike train distances tell us about the neural code?

What can spike train distances tell us about the neural code?

Mutual information and redundancy in spontaneous communication between cortical neurons

To Burst or Not to Burst?

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