Response Variability and Timing Precision of Neuronal Spike Trains In Vivo

Reich, Daniel S., Jonathan D. Victor, Bruce W. Knight, Tsuyoshi Ozaki, and Ehud Kaplan. Response variability and timing precision of neuronal spike trains in vivo. J. Neurophysiol. 77: 2836–2841, 1977. We report that neuronal spike trains can exhibit high, stimulus-dependent temporal precision even while the trial-to-trial response variability, measured in several traditional ways, remains substantially independent of the stimulus. We show that retinal ganglion cells and neurons in the lateral geniculate nucleus (LGN) of cats in vivo display both these aspects of firing behavior, which have previously been reported to be contradictory. We develop a simple model that treats neurons as “leaky” integrate-and-fire devices and show that it, too, can exhibit both behaviors. We consider the implications of our findings for the problem of neural coding.

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Temporally precise control of single-neuron spiking by juxtacellular nanostimulation

Journal of Neurophysiology ◽

10.1152/jn.00479.2016 ◽

2017 ◽

Vol 117 (3) ◽

pp. 1363-1378 ◽

Cited By ~ 4

Author(s):

Maik C. Stüttgen ◽

Lourens J. P. Nonkes ◽

H. Rüdiger A. P. Geis ◽

Paul H. Tiesinga ◽

Arthur R. Houweling

Keyword(s):

Short Duration ◽

Cortical Neurons ◽

Action Potentials ◽

Neural Coding ◽

Spike Trains ◽

Precise Control ◽

Neuronal Spike Trains ◽

The Impact ◽

Activity Dependent

Temporal patterns of action potentials influence a variety of activity-dependent intra- and intercellular processes and play an important role in theories of neural coding. Elucidating the mechanisms underlying these phenomena requires imposing spike trains with precisely defined patterns, but this has been challenging due to the limitations of existing stimulation techniques. Here we present a new nanostimulation method providing control over the action potential output of individual cortical neurons. Spikes are elicited through the juxtacellular application of short-duration fluctuating currents (“kurzpulses”), allowing for the sub-millisecond precise and reproducible induction of arbitrary patterns of action potentials at all physiologically relevant firing frequencies (<120 Hz), including minute-long spike trains recorded in freely moving animals. We systematically compared our method to whole cell current injection, as well as optogenetic stimulation, and show that nanostimulation performance compares favorably with these techniques. This new nanostimulation approach is easily applied, can be readily performed in awake behaving animals, and thus promises to be a powerful tool for systematic investigations into the temporal elements of neural codes, as well as the mechanisms underlying a wide variety of activity-dependent cellular processes. NEW & NOTEWORTHY Assessing the impact of temporal features of neuronal spike trains requires imposing arbitrary patterns of spiking on individual neurons during behavior, but this has been difficult to achieve due to limitations of existing stimulation methods. We present a technique that overcomes these limitations by using carefully designed short-duration fluctuating juxtacellular current injections, which allow for the precise and reliable evocation of arbitrary patterns of neuronal spikes in single neurons in vivo.

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Evolutionary Modularity Optimization Clustering of Neuronal Spike Trains

Neural Information Processing - Lecture Notes in Computer Science ◽

10.1007/978-3-319-70093-9_55 ◽

2017 ◽

pp. 525-532

Author(s):

Chaojie Yu ◽

Yuquan Zhu ◽

Yuqing Song ◽

Hu Lu

Keyword(s):

Spike Trains ◽

Neuronal Spike ◽

Neuronal Spike Trains

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A Computational Model as Neurodecoder Based on Synchronous Oscillation in the Visual Cortex

Neural Computation ◽

10.1162/089976603322362419 ◽

2003 ◽

Vol 15 (10) ◽

pp. 2399-2418 ◽

Cited By ~ 5

Author(s):

Zhao Songnian ◽

Xiong Xiaoyun ◽

Yao Guozheng ◽

Fu Zhi

Keyword(s):

Visual Cortex ◽

Computational Model ◽

Visual Information ◽

Spike Trains ◽

Spike Timing ◽

Contour Detection ◽

System Level ◽

Neuronal Spike ◽

Neuronal Spike Trains ◽

External Stimuli

Based on synchronized responses of neuronal populations in the visual cortex to external stimuli, we proposed a computational model consisting primarily of a neuronal phase-locked loop (NPLL) and multiscaled operator. The former reveals the function of synchronous oscillations in the visual cortex. Regardless of which of these patterns of the spike trains may be an average firing-rate code, a spike-timing code, or a rate-time code, the NPLL can decode original visual information from neuronal spike trains modulated with patterns of external stimuli, because a voltage-controlled oscillator (VCO), which is included in the NPLL, can precisely track neuronal spike trains and instantaneous variations, that is, VCO can make a copy of an external stimulus pattern. The latter, however, describes multi-scaled properties of visual information processing, but not merely edge and contour detection. In this study, in which we combined NPLL with a multiscaled operator and maximum likelihood estimation, we proved that the model, as a neurodecoder, implements optimum algorithm decoding visual information from neuronal spike trains at the system level. At the same time, the model also obtains increasingly important supports, which come from a series of experimental results of neurobiology on stimulus-specific neuronal oscillations or synchronized responses of the neuronal population in the visual cortex. In addition, the problem of how to describe visual acuity and multiresolution of vision by wavelet transform is also discussed. The results indicate that the model provides a deeper understanding of the role of synchronized responses in decoding visual information.

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