Low Response Variability in Simultaneously Recorded Retinal, Thalamic, and Cortical Neurons

Kara, Prakash; Reinagel, Pamela; Reid, R. Clay

doi:10.1016/s0896-6273(00)00072-6

Cited by 356 publications

(425 citation statements)

References 58 publications

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“…While it is frequently assumed that cortical neurons can be modeled by Poisson processes with a refractory period (Shadlen and Newsome 1998;Kara et al 2000) due to their high variability (Softky and Koch 1993), this view is being challenged more and more (Engel et al 2008;Farkhooi et al 2009;Maimon and Assad 2009). In particular, recent studies have found that cortical neurons could display nonrenewal spike train statistics in vivo (Engel et al 2008).…”

Section: Universality Of Nonrenewal Spike Train Statisticsmentioning

confidence: 99%

“…Such a description assumes that the spiking activity can be described by renewal process where all memory is erased after each action potential firing (Cox and Lewis 1966). The Poisson process is, perhaps, the best-known example of a renewal process: it is characterized by an exponential distribution of ISIs and has been frequently used to describe the firing of cortical neurons (Shadlen and Newsome 1998;Kara et al 2000). There is, however, accumulating evidence that the spiking activities of many neurons are not well described by renewal processes as they display memory in their spiking activity that persists over multiple action potential firings (i.e.…”

Section: Introductionmentioning

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: Universality Of Nonrenewal Spike Train Statisticsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Ávila-Ǻkerberg

Chacron

2011

Exp Brain Res

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“…Previous empirical studies reported that FFs change with stimulus due to negative correlations between FF and firing rate (6,39), whereas stimulus-dependent changes of trial-by-trial variability have only been observed in the midbrain and thalamus (40,41). Variations of noise correlations with respect to speed of moving targets and movement direction have been reported in pairs of MT and motor-cortical neurons, respectively (42,43).…”

Section: Discussionmentioning

confidence: 99%

Stimulus-dependent variability and noise correlations in cortical MT neurons

Ponce‐Alvarez

Thiele

Albright

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

123

152

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Population codes assume that neural systems represent sensory inputs through the firing rates of populations of differently tuned neurons. However, trial-by-trial variability and noise correlations are known to affect the information capacity of neural codes. Although recent studies have shown that stimulus presentation reduces both variability and rate correlations with respect to their spontaneous level, possibly improving the encoding accuracy, whether these second order statistics are tuned is unknown. If so, second-order statistics could themselves carry information, rather than being invariably detrimental. Here we show that rate variability and noise correlation vary systematically with stimulus direction in directionally selective middle temporal (MT) neurons, leading to characteristic tuning curves. We show that such tuning emerges in a stochastic recurrent network, for a set of connectivity parameters that overlaps with a single-state scenario and multistability. Information theoretic analysis shows that second-order statistics carry information that can improve the accuracy of the population code.C ortical activity is highly variable during spontaneous activity (1-4) and even when tested under constant experimental conditions (5-8). This variability is thought to limit the capacity of individual neurons to transmit information (9). Furthermore, variability is often correlated among neurons, and thus, it cannot be completely removed by averaging the population response (9-12). Recent experimental studies have examined the secondorder statistics of neural responses across a variety of species, cortical areas, tasks, and stimulus and/or attentional conditions (13-17). In particular, it has been shown that the Fano factor (FF)-that is, the ratio between the variance of the spike counts over trials and its mean-is reduced when a stimulus is applied (16), thus improving the encoding of the stimulus. Importantly, both preferred and nonpreferred stimuli reduced the FF. In addition, the evoked noise correlation-that is, the trial-to-trial covariance of stimulus induced activity between two simultaneously recorded neurons-is also reduced upon stimulus presentation (18), after stimulus adaptation (19) or perceptual learning (20), and under attention (14, 21), an effect that could, under certain conditions, lead to more reliable estimates of the mean population activity (22). Hence, there is a growing body of evidence suggesting that the encoding of a signal through cortical activity may be improved by minimizing both trialby-trial variability and noise correlations. However, it remains an open experimental and theoretical question, whether these statistics are themselves tuned to different stimulus features, an aspect that may be overlooked when only analyzing preferred and nonpreferred stimuli.Here, we examined the statistics of responses of area-middle temporal (MT) neurons in awake, fixating primates, to moving gratings and different plaid patterns of different directions, as well as moving gratings of differ...

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“…Overall, the degree of precision of spiking in response to repeated presentations of the same stimulus appears to decrease along the visual pathway 50 , whereas spike-count variability increases 42,43,47,[51][52][53] . This could be due to the presence of background cortical activity, in which case a method to uncover the stimulus-locked precision is needed (FIG.…”

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

confidence: 97%

Regulation of spike timing in visual cortical circuits

2008

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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...

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Low Response Variability in Simultaneously Recorded Retinal, Thalamic, and Cortical Neurons

Cited by 356 publications

References 58 publications

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

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

Stimulus-dependent variability and noise correlations in cortical MT neurons

Regulation of spike timing in visual cortical circuits

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