Feature Extraction by Burst-Like Spike Patterns in Multiple Sensory Maps

Metzner, Walter; Koch, Christof; Weßel, Ralf; Gabbiani, Fabrizio

doi:10.1523/jneurosci.18-06-02283.1998

Cited by 124 publications

(186 citation statements)

References 71 publications

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“…Our results instead support the notion that bursts in ELL pyramidal neurons are produced primarily to signal the presence of particular features in the sensory environment rather than carry information about their details (i.e. feature detection) Metzner et al 1998;Sherman 2001;Sherman and Guillery 2002). This is because the correlation coefficients between burst and stimulus attributes were weak and non-significant for the most part.…”

Section: Do Bursts Contain Information About Sensory Stimuli In Theircontrasting

confidence: 48%

“…Although the intrinsic and network mechanisms that give rise to burst firing are generally well understood (Izhikevich 2000;Sherman and Guillery 2002;Krahe and Gabbiani 2004), the functional role of burst firing is less well understood. In particular, it has been proposed that bursts, when treated as a single event, can signal the presence of particular stimulus features Metzner et al 1998;Sherman 2001;Sherman and Guillery 2002;Chacron et al 2004a;Lesica and Stanley 2004;Oswald et al 2004). Alternatively, other studies have shown strong correlations between various burst attributes (e.g.…”

Section: Introductionmentioning

confidence: 99%

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In vivo conditions influence the coding of stimulus features by bursts of action potentials

Akerberg

Chacron

2011

J Comput Neurosci

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The functional role of burst firing (i.e. the firing of packets of action potentials followed by quiescence) in sensory processing is still under debate. Should bursts be considered as unitary events that signal the presence of a particular feature in the sensory environment or is information about stimulus attributes contained within their temporal structure? We compared the coding of stimulus attributes by bursts in vivo and in vitro of electrosensory pyramidal neurons in weakly electric fish by computing correlations between burst and stimulus attributes. Our results show that, while these correlations were strong in magnitude and significant in vitro, they were actually much weaker in magnitude if at all significant in vivo. We used a mathematical model of pyramidal neuron activity in vivo and showed that such a model could reproduce the correlations seen in vitro, thereby suggesting that differences in burst coding were not due to differences in bursting seen in vivo and in vitro. We next tested whether variability in the baseline (i.e. without stimulation) activity of ELL pyramidal neurons could account for these differences. To do so, we injected noise into our model whose intensity was calibrated to mimic baseline activity variability as quantified by the coefficient of variation. We found that this noise caused significant decreases in the magnitude of correlations between burst and stimulus attributes and could account for differences between in vitro and in vivo conditions. We then tested this prediction experimentally by directly injecting noise in vitro through the recording electrode. Our results show that this caused a lowering in magnitude of the correlations between burst and stimulus attributes in vitro and gave rise to values that were quantitatively similar to those seen under in vivo conditions.While it is expected that noise in the form of baseline activity variability will lower correlations between burst and stimulus attributes, our results show that such variability can account for differences seen in vivo. Thus, the high variability seen under in vivo conditions has profound consequences on the coding of information by bursts in ELL pyramidal neurons. In particular, our results support the viewpoint that bursts serve as a detector of particular stimulus features but do not carry detailed information about such features in their structure.

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Section: Do Bursts Contain Information About Sensory Stimuli In Theircontrasting

confidence: 48%

Section: Introductionmentioning

confidence: 99%

In vivo conditions influence the coding of stimulus features by bursts of action potentials

Akerberg

Chacron

2011

J Comput Neurosci

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“…Pyramidal cell responses to time-varying inputs have been well characterized both in vitro (Berman and Maler, 1998a,b,c) and in vivo (Bastian, 1981(Bastian, , 2002Chacron, 2006;Chacron and Bastian, 2008;Chacron et al, 2003a;Chacron et al, 2005a). In particular, while receptor afferents generally respond equally well to all frequencies (Chacron et al, 2005a,b), pyramidal cells are generally tuned to a given range of frequencies (Bastian, 1981;Bastian et al, 2002;Chacron, 2006;Chacron et al, 2003bChacron et al, , 2005bKrahe et al, 2002;Metzner et al, 1998;Shumway, 1989b). Previous studies have shown important differences in tuning between pyramidal cells of the different segments (Shumway, 1989a): pyramidal cells of the CMS/CLS/LS segments are tuned to low, mid, and high frequencies, respectively.…”

Section: Pyramidal Cell Frequency Tuningmentioning

confidence: 99%

Ionic and neuromodulatory regulation of burst discharge controls frequency tuning

Mehaffey

Ellis

Krahe

et al. 2008

Journal of Physiology-Paris

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Sensory neurons encode natural stimuli by changes in firing rate or by generating specific firing patterns, such as bursts. Many neural computations rely on the fact that neurons can be tuned to specific stimulus frequencies. It is thus important to understand the mechanisms underlying frequency tuning. In the electrosensory system of the weakly electric fish, Apteronotus leptorhynchus, the primary processing of behaviourally relevant sensory signals occurs in pyramidal neurons of the electrosensory lateral line lobe (ELL). These cells encode low frequency prey stimuli with bursts of spikes and high frequency communication signals with single spikes. We describe here how bursting in pyramidal neurons can be regulated by intrinsic conductances in a cell subtype specific fashion across the sensory maps found within the ELL, thereby regulating their frequency tuning. Further, the neuromodulatory regulation of such conductances within individual cells and the consequences to frequency tuning are highlighted. Such alterations in the tuning of the pyramidal neurons may allow weakly electric fish to preferentially select for certain stimuli under various behaviourally relevant circumstances.

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“…It has previously been shown that the E-type pyramidal cells receiving excitatory input from electroreceptive afferents Metzner et al, 1998) perform feature detection of rising phases in the time-varying stimulus. We thus focus on the rising phase during which the P-receptor afferents preferentially fire (Bastian, 1981).…”

Section: Feature Detectionmentioning

confidence: 99%

“…It has generally been thought that sensory neurons at the periphery should be able to encode the detailed time course of the stimulus, while neurons at a higher stage would extract certain features Metzner et al, 1998). However, sensory neurons at the periphery display considerable variability in several sensory systems (Xu et al, 1996;Goldberg, 2000).…”

Section: Stimulus Reconstruction Vs Feature Extractionmentioning

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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Feature Extraction by Burst-Like Spike Patterns in Multiple Sensory Maps

Cited by 124 publications

References 71 publications

In vivo conditions influence the coding of stimulus features by bursts of action potentials

In vivo conditions influence the coding of stimulus features by bursts of action potentials

Ionic and neuromodulatory regulation of burst discharge controls frequency tuning

To Burst or Not to Burst?

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