Bursting Neurons Signal Input Slope

Kepecs, Ádám; Wang, Xiao Jing; Lisman, John

doi:10.1523/jneurosci.22-20-09053.2002

Cited by 182 publications

(182 citation statements)

References 71 publications

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“…Furthermore, spike timing jitter in the pyramidal neurons that electroreceptor neurons synapse onto is on the order of the EOD cycle (Chacron et al, 2003b) suggesting that these bursts might be decoded by the higher brain. Such a criterion has already been used to classify bursts Kepecs et al, 2002).…”

Section: Burst Criterionmentioning

confidence: 99%

“…1(b)): this implies that B tends to fire action potentials on consecutive EOD cycles (bursts) followed by a variable period of quiescence. We take an ISI threshold criterion to distinguish bursts from single spikes Kepecs et al, 2002) and set the ISI threshold value to 1.5 EOD cycles. Parameters for the model were chosen such that both units have the same firing rate for baseline activity.…”

Section: Calibrationmentioning

confidence: 99%

“…most if not all features should be encoded by bursts). As it has been shown that bursts could encode the rising phases of the stimulus Kepecs et al, 2002), we thus measure the probability of a burst occurring during the rising phase of the stimulus κ 1 (correlation) and the probability of a rising phase having at least one burst associated to it κ 2 (reliability). The measure κ = κ 1 * κ 2 can thus take values between zero and one and measures the efficiency of encoding features by bursts .…”

Section: Feature Detectionmentioning

confidence: 99%

“…Furthermore, the relative computational advantages of bursting and tonically firing neurons have not been clearly established. While it has been shown that both tonically firing and bursting neurons can encode information (Reinagel et al, 1999), one possible function of bursting is to detect specific stimulus features and some results do support this hypothesis Sherman, 2001;Kepecs et al, 2002). On the other hand, bursting behavior might simply be used to improve synaptic reliability (Lisman, 1997).…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Burst Criterionmentioning

confidence: 99%

Section: Calibrationmentioning

confidence: 99%

Section: Feature Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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To Burst or Not to Burst?

2004

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“…the temporal ordering of spikes within a burst or the number of spikes within a burst) and stimulus attributes (e.g. its amplitude of its rate of rise before burst onset), thereby suggesting that the detailed structure of a burst could provide information about the stimulus (DeBusk et al 1997;Kepecs et al 2002;Martinez-Conde et al 2002;Oswald et al 2007;Eyherabide et al 2008;Gaudry and Reinagel 2008;Marsat and Pollack 2010;Samengo and Montemurro 2010). Studies conducted in simple systems with well-characterized anatomy and relatively simple behaviors are likely to yield significant insight into the functional role of burst firing.…”

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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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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Neurophysiological Oscillations and Action Monitoring

Cohen¹

2017

The Wiley Handbook of Cognitive Control

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Bursting Neurons Signal Input Slope

Cited by 182 publications

References 71 publications

To Burst or Not to Burst?

To Burst or Not to Burst?

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

Neurophysiological Oscillations and Action Monitoring

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