The Role of Spike Timing in the Coding of Stimulus Location in Rat Somatosensory Cortex

Panzeri, Stefano; Petersen, Rasmus S.; Schultz, Simon R.; Lebedev, Mikhail А.; Diamond, Mathew E.

doi:10.1016/s0896-6273(01)00251-3

Cited by 401 publications

(368 citation statements)

References 41 publications

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“…Among the most well supported temporal coding schemes are those in which the latencies or order of arrival of spikes encode the stimulus (Gawne et al, 1996;Panzeri et al, 2001;Heil, 2004;Johansson and Birznieks, 2004;VanRullen et al, 2005). Spike latency may be a natural variable in systems in which sensory acquisition is linked to discrete behavioral events (e.g., saccadic eye movements, active touch, sniffing, echolocation, and active electrolocation).…”

Section: Discussionmentioning

confidence: 99%

Effects of Sensing Behavior on a Latency Code

Sawtell¹,

Williams²,

Roberts³

et al. 2006

J. Neurosci.

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Sensory information is often acquired through active exploration, yet relatively little is known about how neurons encode sensory stimuli in the context of natural patterns of sensing behavior. We examined the effects of sensing behavior on a spike latency code in the active electrosensory system of mormyrid fish. These fish actively probe their environment by emitting brief electric organ discharge (EOD) pulses. Nearby objects alter the spatial pattern of current flowing through the skin. These changes are encoded by small shifts in the latency of individual electroreceptor afferent spikes after the EOD. In nature, the temporal pattern of EOD intervals is highly structured and varies depending on the behavioral context. We performed experiments in which we varied both the EOD amplitude and the intervals between EODs to understand how sensing behavior affects afferent latency coding. We use white-noise stimuli and linear filter estimation methods to develop simple models characterizing the dependence of afferent spike latency on the preceding sequence of EOD intervals and amplitudes. Comparing the predictions of these models with actual afferent responses for natural patterns of EOD intervals and amplitudes reveals an unexpectedly rich interplay between sensing behavior and stimulus encoding. Implications of our results for how afferent spike latency is decoded at central stages of electrosensory processing are discussed.

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Section: Discussionmentioning

confidence: 99%

Effects of Sensing Behavior on a Latency Code

Sawtell¹,

Williams²,

Roberts³

et al. 2006

J. Neurosci.

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show abstract

“…For transient responses, latency has been shown to contain a high amount of information about the stimuli (e. g. Reich et al, 2001b;Panzeri et al, 2001). To model this effect in Figure 4 we consider two constant stimuli S x and S y that elicit, during a short fixed interval, the same elevation of the rate with respect to a baseline level, but with different delays with respect to the onset time.…”

Section: Dependence On the Length Of The Spike Trains For Different Smentioning

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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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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“…Given previous findings that neurons carry substantial sensory information in their response latencies (Panzeri et al, 2001;Reich et al, 2001;Gollisch and Meister, 2008), consideration of temporal correlations across the time bins may be important. Statistical models that take account of time-lagged correlations can be constructed based on the maximum entropy method with a Markovian assumption of temporal evolution (Marre et al, 2009) or based on a generalized linear model (Pillow et al, 2005(Pillow et al, , 2008.…”

Section: Temporal Correlations Across Time Binsmentioning

confidence: 99%

Mismatched Decoding in the Brain

Oizumi

Ishii²,

Ishibashi³

et al. 2010

J. Neurosci.

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"How is information decoded in the brain?" is one of the most difficult and important questions in neuroscience. We have developed a general framework for investigating to what extent the decoding process in the brain can be simplified. First, we hierarchically constructed simplified probabilistic models of neural responses that ignore more than Kth-order correlations using the maximum entropy principle. We then computed how much information is lost when information is decoded using these simplified probabilistic models (i.e., "mismatched decoders"). To evaluate the information obtained by mismatched decoders, we introduced an information theoretic quantity, I*, which was derived by extending the mutual information in terms of communication rate across a channel. We showed that I* provides consistent results with the minimum mean-square error as well as the mutual information, and demonstrated that a previously proposed measure quantifying the importance of correlations in decoding substantially deviates from I* when many cells are analyzed. We then applied this proposed framework to spike data for vertebrate retina using short natural scene movies of 100 ms duration as a set of stimuli and computing the information contained in neural activities. Although significant correlations were observed in population activities of ganglion cells, information loss was negligibly small even if all orders of correlation were ignored in decoding. We also found that, if we inappropriately assumed stationarity for long durations in the information analysis of dynamically changing stimuli, such as natural scene movies, correlations appear to carry a large proportion of total information regardless of their actual importance.

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The Role of Spike Timing in the Coding of Stimulus Location in Rat Somatosensory Cortex

Cited by 401 publications

References 41 publications

Effects of Sensing Behavior on a Latency Code

Effects of Sensing Behavior on a Latency Code

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

Mismatched Decoding in the Brain

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