Precision of Spike Trains in Primate Retinal Ganglion Cells

Uzzell, Valerie; Chichilnisky, E. J.

doi:10.1152/jn.01171.2003

Cited by 188 publications

(176 citation statements)

References 42 publications

Supporting

Mentioning

154

Contrasting

Unclassified

Order By: Relevance

“…Although spike jitter has been characterized and considered in many studies (Bryant et al, 1973;Mainen and Sejnowski, 1995;Victor and Purpura, 1996;Berry et al, 1997;Maršálek et al, 1997;Reich et al, 1997;Reinagel and Reid, 2000;Keat et al, 2001;Liu et al, 2001;Zoccolan et al, 2002;Hsu et al, 2004;Uzzell and Chichilnisky, 2004), it is not taken into account fully when analyzing feature selectivity with standard analysis techniques. Conventional methods for the derivation of the STA, STRF, or Volterra and Wiener kernels misrepresent the class of stimulus waveforms that elicit spikes in a cell: those techniques underestimate the amplitude and bandwidth of the mean stimulus waveform and overestimate the variance of the waveforms around that mean.…”

Section: Discussionmentioning

confidence: 99%

“…Specifically, these algorithms register the waveform segments to one another based on the precise time of spike occurrence. We know, however, that the biophysical processes underlying sensory transduction, synaptic integration, spike initiation, and synaptic transmission are not perfectly deterministic, and some significant degree of "jitter" in stimulus-to-spike latency is always observable after repeated presentation of identical stimuli (Bryant et al, 1973;Berry et al, 1997;Maršálek et al, 1997;Liu et al, 2001;Zoccolan et al, 2002;Hsu et al, 2004;Uzzell and Chichilnisky, 2004). By ignoring this "intrinsic" jitter, these analytical algorithms all yield distorted estimates of the relevant stimulus waveform and of the variance around that waveform.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Dejittered Spike-Conditioned Stimulus Waveforms Yield Improved Estimates of Neuronal Feature Selectivity and Spike-Timing Precision of Sensory Interneurons

Aldworth¹,

Miller²,

Gedeon³

et al. 2005

J. Neurosci.

View full text Add to dashboard Cite

What is the meaning associated with a single action potential in a neural spike train? The answer depends on the way the question is formulated. One general approach toward formulating this question involves estimating the average stimulus waveform preceding spikes in a spike train. Many different algorithms have been used to obtain such estimates, ranging from spike-triggered averaging of stimuli to correlation-based extraction of "stimulus-reconstruction" kernels or spatiotemporal receptive fields. We demonstrate that all of these approaches miscalculate the stimulus feature selectivity of a neuron. Their errors arise from the manner in which the stimulus waveforms are aligned to one another during the calculations. Specifically, the waveform segments are locked to the precise time of spike occurrence, ignoring the intrinsic "jitter" in the stimulus-to-spike latency. We present an algorithm that takes this jitter into account. "Dejittered" estimates of the feature selectivity of a neuron are more accurate (i.e., provide a better estimate of the mean waveform eliciting a spike) and more precise (i.e., have smaller variance around that waveform) than estimates obtained using standard techniques. Moreover, this approach yields an explicit measure of spike-timing precision. We applied this technique to study feature selectivity and spike-timing precision in two types of sensory interneurons in the cricket cercal system. The dejittered estimates of the mean stimulus waveforms preceding spikes were up to three times larger than estimates based on the standard techniques used in previous studies and had power that extended into higher-frequency ranges. Spike timing precision was ϳ5 ms.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dejittered Spike-Conditioned Stimulus Waveforms Yield Improved Estimates of Neuronal Feature Selectivity and Spike-Timing Precision of Sensory Interneurons

Aldworth¹,

Miller²,

Gedeon³

et al. 2005

J. Neurosci.

View full text Add to dashboard Cite

show abstract

“…Although the available results are vague, two studies showing striking similarities to our results are worth mentioning. In the studies of parasol retinal ganglion cells in the macaque monkey (homologs of cat's Y cells) (Crook et al, 2008), spike count variance across trials was much lower than its mean (Uzzell and Chichilnisky, 2004). The finding was inconsistent with Poisson statistics but matched a model that included the relative refractory period.…”

Section: Differences Between Cell Classesmentioning

confidence: 93%

Variability of Visual Responses of Superior Colliculus Neurons Depends on Stimulus Velocity

Mochol

Wójcik²,

Wypych³

et al. 2010

J. Neurosci.

View full text Add to dashboard Cite

Visually responding neurons in the superficial, retinorecipient layers of the cat superior colliculus receive input from two primarily parallel information processing channels, Y and W, which is reflected in their velocity response profiles. We quantified the timedependent variability of responses of these neurons to stimuli moving with different velocities by Fano factor (FF) calculated in discrete time windows. The FF for cells responding to low-velocity stimuli, thus receiving W inputs, increased with the increase in the firing rate. In contrast, the dynamics of activity of the cells responding to fast moving stimuli, processed by Y pathway, correlated negatively with FF whether the response was excitatory or suppressive. These observations were tested against several types of surrogate data. Whereas Poisson description failed to reproduce the variability of all collicular responses, the inclusion of secondary structure to the generating point process recovered most of the observed features of responses to fast moving stimuli. Neither model could reproduce the variability of low-velocity responses, which suggests that, in this case, more complex time dependencies need to be taken into account. Our results indicate that Y and W channels may differ in reliability of responses to visual stimulation. Apart from previously reported morphological and physiological differences of the cells belonging to Y and W channels, this is a new feature distinguishing these two pathways.

show abstract

“…In an eye-cup preparation, retinal ganglion cells produced precise and reliable spike trains in response to a temporally fluctuating visual stimulus 24,25,[40][41][42] . Precision increased as stimulus contrast increased, because of an enlargement in the somatic amplitude of the inputs.…”

Section: Evidence For Spike-time Precision In the Visual Systemmentioning

confidence: 99%

Regulation of spike timing in visual cortical circuits

2008

View full text Add to dashboard Cite

A train of action potentials (a spike train) can carry information in both the average firing rate and the pattern of spikes in the train. But can such a spike-pattern code be supported by cortical circuits? Neurons in vitro produce a spike pattern in response to the injection of a fluctuating current. However, cortical neurons in vivo are modulated by local oscillatory neuronal activity and by top-down inputs. In a cortical circuit, precise spike patterns thus reflect the interaction between internally generated activity and sensory information encoded by input spike trains. We review the evidence for precise and reliable spike timing in the cortex and discuss its computational role.Reliability and precision are two different quantities. When you make an appointment with your friend, she can either keep the appointment or not show up at all. If she does show up, she might or might not be on time. The former uncertainty is related to reliability, whereas the latter is related to precision. When the same stimulus waveform is repeatedly injected at the soma of a neuron in vitro (FIG. 1a), a similar spike train is obtained on each trial 1,2 (FIG. 1b). When approximately the same number of spikes occur on each trial the neuron is said to be reliable, whereas when the spikes occur almost at the same time across trials it is said to be precise (FIG. 1c). For a single neuron, the potential information content of precise and reliable spike times is many times larger than that which is contained in the firing rate, which is averaged across a typical interval of a hundred milliseconds [3][4][5][6] . The information contained in spike timing is available immediately, rather than after an averaging period. Furthermore, the timing of patterns of spikes can potentially transmit even more information than the timing of the individual constituent spikes 3,7 . The potential relevance of spike patterns becomes apparent when we consider neurons at the population level: when a group of similar neurons (a 'pool') produces precise and reliable spike trains, the neurons they project to receive volleys of synchronous spikes 8,9 . This opens up the possibility of communicating between different cortical areas through synchronous spike volleys.In contrast to the in vitro situation described above, in the intact cortex most excitatory synaptic inputs arrive at the dendrites rather than at the soma (FIG. 1d), and synaptic transmission is typically unreliable [10][11][12][13] . Furthermore, most of these dendritic inputs are not directly related to ongoing sensory stimulation; rather, they reflect spatiotemporally structured internal activity. Therefore, when the same stimulus is presented repeatedly, the resulting spike trains are usually neither precise nor reliable when they are aligned to the stimulus onset 6 . Instead, HHMI Author ManuscriptHHMI Author Manuscript HHMI Author Manuscript neural activity in vivo might be dominated by internally generated complex reverberations or rhythmic oscillations, and precise and reliable sp...

show abstract

Precision of Spike Trains in Primate Retinal Ganglion Cells

Cited by 188 publications

References 42 publications

Dejittered Spike-Conditioned Stimulus Waveforms Yield Improved Estimates of Neuronal Feature Selectivity and Spike-Timing Precision of Sensory Interneurons

Dejittered Spike-Conditioned Stimulus Waveforms Yield Improved Estimates of Neuronal Feature Selectivity and Spike-Timing Precision of Sensory Interneurons

Variability of Visual Responses of Superior Colliculus Neurons Depends on Stimulus Velocity

Regulation of spike timing in visual cortical circuits

Contact Info

Product

Resources

About