Efficient Discrimination of Temporal Patterns by Motion-Sensitive Neurons in Primate Visual Cortex

Buračas, Giedrius T.; Zador, Anthony M.; DeWeese, Michael R.; Albright, Thomas D.

doi:10.1016/s0896-6273(00)80477-8

Cited by 359 publications

(381 citation statements)

References 33 publications

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“…Our results are consistent with those obtained in Buracas et al (1998) which show a difference between the direct and indirect methods of estimating information rates. Our results further imply that information from bursting neurons can only be efficiently decoded by non-linear means while linear decoding will be effective for tonic neurons.…”

Section: Information Theory: Linear Versus Nonlinear Codingsupporting

confidence: 92%

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: Information Theory: Linear Versus Nonlinear Codingsupporting

confidence: 92%

To Burst or Not to Burst?

2004

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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%

“…Neurons in the mediotemporal cortex in turn receive inputs from the primary visual cortex and respond to motion. It has been shown that neurons in the mediotemporal cortex respond precisely to rapid changes in the direction of motion 47 . 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 .…”

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

confidence: 99%

“…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%

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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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“…For instance, sensory cortex encodes stimuli that fluctuate over few tens of milliseconds 6,7 , whereas in association cortex behavioral choices can require the maintenance of information over seconds 8,9 . However, it remains poorly understood if diverse timescales result mostly from features intrinsic to individual neurons or from neuronal population activity.…”

mentioning

confidence: 99%

Distinct timescales of population coding across cortex

Runyan

Piasini

Panzeri

et al. 2017

Nature

329

429

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The cortex represents information across widely varying timescales1–5. For instance, sensory cortex encodes stimuli that fluctuate over few tens of milliseconds6,7, whereas in association cortex behavioral choices can require the maintenance of information over seconds8,9. However, it remains poorly understood if diverse timescales result mostly from features intrinsic to individual neurons or from neuronal population activity. This question is unanswered because the timescales of coding in populations of neurons have not been studied extensively, and population codes have not been compared systematically across cortical regions. Here we discovered that population codes can be essential to achieve long coding timescales. Furthermore, we found that the properties of population codes differ between sensory and association cortices. We compared coding for sensory stimuli and behavioral choices in auditory cortex (AC) and posterior parietal cortex (PPC) as mice performed a sound localization task. Auditory stimulus information was stronger in AC than in PPC, and both regions contained choice information. Although AC and PPC coded information by tiling in time neurons that were transiently informative for ~200 milliseconds, the areas had major differences in functional coupling between neurons, measured as activity correlations that could not be explained by task events. Coupling among PPC neurons was strong and extended over long time lags, whereas coupling among AC neurons was weak and short-lived. Stronger coupling in PPC led to a population code with long timescales and a representation of choice that remained consistent for approximately one second. In contrast, AC had a code with rapid fluctuations in stimulus and choice information over hundreds of milliseconds. Our results reveal that population codes differ across cortex and that coupling is a variable property of cortical populations that affects the timescale of information coding and the accuracy of behavior.

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Efficient Discrimination of Temporal Patterns by Motion-Sensitive Neurons in Primate Visual Cortex

Cited by 359 publications

References 33 publications

To Burst or Not to Burst?

To Burst or Not to Burst?

Regulation of spike timing in visual cortical circuits

Distinct timescales of population coding across cortex

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