Neural Variability, Detection Thresholds, and Information Transmission in the Vestibular System

Sadeghi, Soroush G.; Chacron, Maurice J.; Taylor, Michael; Cullen, Kathleen E.

doi:10.1523/jneurosci.4690-06.2007

Cited by 222 publications

(268 citation statements)

References 72 publications

(110 reference statements)

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“…Moreover, both models have been shown to be qualitatively equivalent for the most part (Liu and Wang 2001), although a recent study has pointed out some differences in terms of spike frequency adaptation . The leaky integrate-and-fire model with dynamic threshold can furthermore successfully reproduce experimental data from the electroreceptors of weakly electric fish (Chacron et al 2000(Chacron et al , 2004aBrandman and Nelson 2002;Chacron 2006;Savard et al 2011) as well as vestibular afferents (Sadeghi et al 2007). Recent experimental evidence shows that threshold fatigue is also present in electrosensory pyramidal neurons ).…”

Section: Mechanisms That Give Rise To a Single Negative Isi Correlatimentioning

confidence: 88%

“…This assumes that the stimulus intensity is small so that the perturbations brought about by the stimulus are small in magnitude and will thus scale linearly with the stimulus intensity. Experimental results have shown that this is the case in both electroreceptor (Chacron et al 2005b) and vestibular afferents (Sadeghi et al 2007), provided that the stimulus intensity is not too high (but see (Savard et al 2011)). Investigators have therefore focused on understanding how ISI correlations can alter the power spectrum of the baseline activity.…”

Section: Effects Of Isi Correlations On Information Transmission: Noimentioning

confidence: 99%

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Nonrenewal spike train statistics: causes and functional consequences on neural coding

Ávila-Ǻkerberg

Chacron

2011

Exp Brain Res

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Many neurons display significant patterning in their spike trains (e.g. oscillations, bursting), and there is accumulating evidence that information is contained in these patterns. In many cases, this patterning is caused by intrinsic mechanisms rather than external signals. In this review, we focus on spiking activity that displays nonrenewal statistics (i.e. memory that persists from one firing to the next). Such statistics are seen in both peripheral and central neurons and appear to be ubiquitous in the CNS. We review the principal mechanisms that can give rise to nonrenewal spike train statistics. These are separated into intrinsic mechanisms such as relative refractoriness and network mechanisms such as coupling with delayed inhibitory feedback. Next, we focus on the functional roles for nonrenewal spike train statistics. These can either increase or decrease information transmission. We also focus on how such statistics can give rise to an optimal integration timescale at which spike train variability is minimal and how this might be exploited by sensory systems to maximize the detection of weak signals. We finish by pointing out some interesting future directions for research in this area. In particular, we explore the interesting possibility that synaptic dynamics might be matched with the nonrenewal spiking statistics of presynaptic spike trains in order to further improve information transmission.

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Section: Mechanisms That Give Rise To a Single Negative Isi Correlatimentioning

confidence: 88%

Section: Effects Of Isi Correlations On Information Transmission: Noimentioning

confidence: 99%

Nonrenewal spike train statistics: causes and functional consequences on neural coding

Ávila-Ǻkerberg

Chacron

2011

Exp Brain Res

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“…As a consequence, Oswald et al (2007) showed that an ideal observer could distinguish different stimulus amplitudes using the amount of time between two consecutive spikes within a burst. As the value of the burst ISI was typically <10 ms and the time scales associated with the stimulus were in that study were greater than 16 ms, they showed that information about the stimulus is contained in the structure of the burst at time scales that are smaller than those contained in the stimulus, which constitutes a temporal code by definition (Theunissen and Miller 1995;Dayan and Abbott 2001;Jones et al 2004;Sadeghi et al 2007). Our previous results obtained in vivo did not find such strong correlations between burst and stimulus attributes (Avila Akerberg et al 2010).…”

Section: Do Bursts Contain Information About Sensory Stimuli In Theirmentioning

confidence: 91%

“…Previous theoretical studies have investigated the consequences of variability in the baseline activity on information transmission (Chacron et al 2004b;Chacron et al 2005b, d;Lindner et al 2005). In particular, the power spectrum of the baseline activity can be seen as a "noise spectrum" and determines the amount of noise at each frequency during stimulation (Sadeghi et al 2007). As such, a more variable baseline activity will give rise to greater noise power and thus, a lower signal-to-noise ratio and information transmission.…”

Section: Increasing Variability Decreases Correlations Between Burstmentioning

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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“…Equivalently, this implies that information is contained in the response at time scales that are shorter than those contained in the stimulus, which is by definition a temporal code (Theunissen and Miller, 1995;Dayan and Abbott, 2001;Jones et al, 2004;Sadeghi et al, 2007).…”

Section: Modeling Electroreceptor Afferent Responses To Am Stimulimentioning

confidence: 99%

Neural heterogeneities influence envelope and temporal coding at the sensory periphery

Savard¹,

Krahe²,

Chacron³

2011

Neuroscience

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Peripheral sensory neurons respond to stimuli containing a wide range of spatio-temporal frequencies. We investigated electroreceptor neuron coding in the gymnotiform wave-type weakly electric fish Apteronotus leptorhynchus. Previous studies used low to mid temporal frequencies (<256 Hz) and showed that electroreceptor neuron responses to sensory stimuli could be almost exclusively accounted for by linear models, thereby implying a rate code. We instead used temporal frequencies up to 425 Hz, which is in the upper behaviorally relevant range for this species. We show that electroreceptors can: (A) respond up to the highest frequencies tested and (B) display strong nonlinearities in their responses to such stimuli. These nonlinearities were manifested by the fact that the responses to repeated presentations of the same stimulus were coherent at temporal frequencies outside of those contained in the stimulus waveform. Specifically, these consisted of low frequencies corresponding to the time varying contrast or envelope of the stimulus as well as higher harmonics of the frequencies contained in the stimulus. Heterogeneities in the afferent population influenced nonlinear coding as afferents with the lowest baseline firing rates tended to display the strongest nonlinear responses. To understand the link between afferent heterogeneity and nonlinear responsiveness, we used a phenomenological mathematical model of electrosensory afferents. Varying a single parameter in the model was sufficient to account for the variability seen in our experimental data and yielded a prediction: nonlinear responses to the envelope and at higher harmonics are both due to afferents with lower baseline firing rates displaying greater degrees of rectification in their responses. This prediction was verified experimentally as we found that the coherence between the half-wave rectified stimulus and the response resembled the coherence between the responses to repeated presentations of the stimulus in our dataset. This result shows that rectification cannot only give rise to responses to low frequency envelopes but also at frequencies that are higher than those contained in the stimulus. The latter result implies that information is contained in the fine temporal structure of electroreceptor afferent spike trains. Our results show that heterogeneities in peripheral neuronal populations can have dramatic consequences on the nature of the neural code.

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Neural Variability, Detection Thresholds, and Information Transmission in the Vestibular System

Cited by 222 publications

References 72 publications

Nonrenewal spike train statistics: causes and functional consequences on neural coding

Nonrenewal spike train statistics: causes and functional consequences on neural coding

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

Neural heterogeneities influence envelope and temporal coding at the sensory periphery

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