Response Variability of Neurons in Primary Visual Cortex (V1) of Alert Monkeys

Gur, Moshe; Beylin, Alexander; Snodderly, D. Max

doi:10.1523/jneurosci.17-08-02914.1997

Cited by 243 publications

(238 citation statements)

References 36 publications

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“…More recently, Stevens and Zador (1998) (see also Destexhe and Paré, 2000) have also suggested, based on experiments in a slice preparation, that input synchrony is required to produce the high variability observed in vivo. This is consistent with the suggestion that stochastic eye movements, which provide a common, correlating signal, are responsible for a large fraction of the variability observed in primary visual neurons (Gur et al, 1997). It also agrees with recent theoretical results (Feng and Brown, 2000), and with simulation studies in which network interactions produce synchronized recurrent input, which also leads to high variability Tsodyks and Sejnowski, 1995;Van Vreeswijk and Sompolinsky, 1996).…”

Section: Discussionsupporting

confidence: 92%

Impact of Correlated Synaptic Input on Output Firing Rate and Variability in Simple Neuronal Models

2000

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Cortical neurons are typically driven by thousands of synaptic inputs. The arrival of a spike from one input may or may not be correlated with the arrival of other spikes from different inputs. How does this interdependence alter the probability that the postsynaptic neuron will fire? We constructed a simple random walk model in which the membrane potential of a target neuron fluctuates stochastically, driven by excitatory and inhibitory spikes arriving at random times. An analytic expression was derived for the mean output firing rate as a function of the firing rates and pairwise correlations of the inputs. This stochastic model made three quantitative predictions. (1) Correlations between pairs of excitatory or inhibitory inputs increase the fluctuations in synaptic drive, whereas correlations between excitatory-inhibitory pairs decrease them. (2) When excitation and inhibition are fully balanced (the mean net synaptic drive is zero), firing is caused by the fluctuations only. (3) In the balanced case, firing is irregular. These theoretical predictions were in excellent agreement with simulations of an integrate-and-fire neuron that included multiple conductances and received hundreds of synaptic inputs. The results show that, in the balanced regime, weak correlations caused by signals shared among inputs may have a multiplicative effect on the input-output rate curve of a postsynaptic neuron, i.e. they may regulate its gain; in the unbalanced regime, correlations may increase firing probability mainly around threshold, when output rate is low; and in all cases correlations are expected to increase the variability of the output spike train. Key words: random-walk; integrate-and-fire; computer simulation; spike synchrony; oscillations; cross-correlation; balanced inhibition; cerebral cortexThe output of a typical cortical neuron depends on the activity of a large number of synaptic inputs-several thousands of them, as estimated by anatomical techniques (White, 1989; Braitenberg and Shüz, 1997). What kind of response should be expected from a postsynaptic neuron driven by so many inputs? Answering this question in detail requires a deep understanding of dendritic integration, synaptic function, and spike generation mechanisms; but, given the large numbers commonly involved, as a first approximation it is natural to cast the problem in statistical terms. The strategy then is to compute the output responses of a model neuron (or their statistics), given a set of driving inputs with known statistical properties. These inputs may be either independent or temporally correlated. In the latter case, spikes from different input neurons arrive close together in time more often or less often than expected by chance.In general, the situation with independent inputs is easier to analyze, and for many applications it is probably a good approximation. However, there are at least three reasons why the effects of correlations on single cells should be fully characterized. First, correlations in spike counts have indeed been o...

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Section: Discussionsupporting

confidence: 92%

Impact of Correlated Synaptic Input on Output Firing Rate and Variability in Simple Neuronal Models

2000

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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%

“…The reliability of events in these spike trains was initially found to be low; however, a re-analysis revealed multiple reliable spike-time patterns 18 . Further evidence for precise spike timing at this level is found in the barrel cortex 48 and the auditory cortex 49 .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.…”

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confidence: 94%

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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“…Neurons in this area exhibit broad spatial tuning and are thus fairly insensitive to small sensory and motor variations typically observed across behavioral trials, which can confound the interpretation of correlated firing in cortical areas with much smaller receptive fields (Gur and Snodderly 2001;Gur et al 1997;Leopold and Logothetis 1998). Recent studies from this and other laboratories have exploited the action potential waveforms and discharge rate properties of cortical neurons to distinguish pyramidal cells and interneurons and to study their spatial and temporal interactions in vivo (Constantinidis et al 2002;Csicsvari et al 1998;Frank et al 2001;Jung et al 1998;Rao et al 1999;Swadlow 1995;Wilson et al 1994).…”

Section: Introductionmentioning

confidence: 99%

Correlated Discharges Among Putative Pyramidal Neurons and Interneurons in the Primate Prefrontal Cortex

Constantinidis

Goldman‐Rakic

2002

Journal of Neurophysiology

274

233

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Constantinidis, Christos, and Patricia S. Goldman-Rakic. Correlated discharges among putative pyramidal neurons and interneurons in the primate prefrontal cortex. J Neurophysiol 88: 3487-3497, 2002; 10.1152/jn.00188.2002. Neurophysiological recordings have revealed that the discharges of nearby cortical cells are positively correlated in time scales that range from millisecond synchronization of action potentials to much slower firing rate co-variations, evident in rates averaged over hundreds of milliseconds. The presence of correlated firing can offer insights into the patterns of connectivity between neurons; however, few models of population coding have taken account of the neuronal diversity present in cerebral cortex, notably a distinction between inhibitory and excitatory cells. We addressed this question in the monkey dorsolateral prefrontal cortex by recording neuronal activity from multiple micro-electrodes, typically spaced 0.2-0.3 mm apart. Putative excitatory and inhibitory neurons were distinguished based on their action potential waveform and baseline discharge rate. We tested each pair of simultaneously recorded neurons for presence of significant cross-correlation peaks and measured the correlation of their averaged firing rates in successive trials. When observed, cross-correlation peaks were centered at time 0, indicating synchronous firing consistent with two neurons receiving common input. Discharges in pairs of putative inhibitory interneurons were found to be significantly more strongly correlated than in pairs of putative excitatory cells. The degree of correlated firing was also higher for neurons with similar spatial receptive fields and neurons active in the same epochs of the behavioral task. These factors were important in predicting the strength of both short time scale (Ͻ5 ms) correlations and of trial-to-trial discharge rate covariations. Correlated firing was only marginally accounted for by motor and behavioral variations between trials. Our findings suggest that nearby inhibitory neurons are more tightly synchronized than excitatory ones and account for much of the correlated discharges commonly observed in undifferentiated cortical networks. In contrast, the discharge of pyramidal neurons, the sole projection cells of the cerebral cortex, appears largely independent, suggesting that correlated firing may be a property confined within local circuits and only to a lesser degree propagated to distant cortical areas and modules.

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Response Variability of Neurons in Primary Visual Cortex (V1) of Alert Monkeys

Cited by 243 publications

References 36 publications

Impact of Correlated Synaptic Input on Output Firing Rate and Variability in Simple Neuronal Models

Impact of Correlated Synaptic Input on Output Firing Rate and Variability in Simple Neuronal Models

Regulation of spike timing in visual cortical circuits

Correlated Discharges Among Putative Pyramidal Neurons and Interneurons in the Primate Prefrontal Cortex

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