Neuronal Spike Trains and Stochastic Point Processes

Perkel, Donald H.; Gerstein, George L.; Ge, Moore

doi:10.1016/s0006-3495(67)86597-4

Cited by 1,352 publications

(795 citation statements)

References 5 publications

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“…the probability of firing is influenced by the past spiking activity). Such memory can be quantified in terms of the ISI serial correlation coefficients ρ j (Perkel et al 1967a): where j is the lag. The ISI serial correlation coefficient, ρ j , measures whether the value of ISI i has any influence on the value of ISI i+j (i.e.…”

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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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: 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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“…This relationship is also found by estimating the cross-correlation function of cortical and hippocampal theta units (Perkel et al 1967b). The estimated cross-correlation function C(~) shows the probability of spike generation by one unit conditional upon the occurrence of a (Fig.…”

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confidence: 81%

“…1B). These phenomena can be better put in evidence by estimating the renewal density (often called autocorrelation) (Perkel et al 1967a) and the (spike-)-triggered correlation with the theta waves of the same record. The estimated renewal density function V(~) shows the probability of spike generation within successive intervals of 4 ms during 1 s after the occurrence of a spike at 9 = 0 s (Fig.…”

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confidence: 99%

Generation of theta activity (RSA) in the cingulate cortex of the rat

Holsheimer

1982

Exp Brain Res

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Summary. Unit activity recorded from the cingulate cortex during theta rhythm shows periodic trains of spikes which are phase-locked to the local theta field potential waves. These cortical theta units were also shown to be correlated with hippocampal theta units. These findings, along with the fact that theta field potentials show a phase reversal within the cingulate cortex, lead to the conclusion that this cortical area is a source of theta activity.Key words: Theta rhythm -Spike trains -Field potential -Cingulate cortex -Hippocampus Theta field potentials, or rhythmic slow activity (RSA) in the frequency band 4-10 Hz in the rat, recorded in parts of the cortex overlying the hippocampal complex, have been noted by several authors. Although without specifying the cortical areas from which it was recorded, Artemenko (1972), Winson (1974), Bland and Whishaw (1976), and Gerbrandt et al. (1978 described cortical theta to be isomorphic with theta in the neighboring part of the hippocampus, the amplitude decreasing toward the cortical surface. Cortical theta would thus be accounted for in terms of volume conduction theory, since a stationary neuronal source in a purely resistive medium causes a field potential, the amplitude of which decreases with increasing distance from this source without phase shift (Holsheimer and Feenstra 1977).However, Petsche and Stumpf (1960) found a "loose phase coupling" between cortical and hippocampal theta field potentials in the rabbit; the theta waves in these two regions were never more than a fraction of a period apart. During arousal they found regular theta rhythm in the hippocampus, but irregular theta activity in the cortex of the awake rabbit. Winson (1974) reported that both intermittent theta or absence of theta in the cortex could be found during regular hippocampal theta rhythm in the awake rat. These findings suggest that theta field potentials in parts of the cortex overlying the hippocampal complex may not be considered solely the result of volume conduction from a hippocampal source, i.e., there is a possibility that hippocampal and cortical theta have different neuronal sources.We made a quantitative analysis of the relationship between theta field potentials in the laminae of the dorsal hippocampus and overlying cortex of the urethane anesthetized rat, which indeed revealed phase differences of approximately 180 deg not only in the hippocampus but also in the overlying cortex Holsheimer et al. 1979). These theta phase reversals, on tracks normal to the hippocampal and to the cortical surface (laminar profiles), were considered as indicating that neuronal sources of theta activity exist in each of these structures; each source causing a dipole-like potential field. The cortical areas concerned were those parts lying near the midline, namely the cingulate cortex (Fig. 1). The corresponding neuronal source might be the population of large pyramidal cells having their somata in layer V, since the theta phase reversal was found in this layer. We also found that the coher...

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“…Discovering, or learning, such a network with our (Gerstein and Perkel 1969;Aertsen et al 1989) Cross-correlation (Perkel et al 1967) Frequency a Pairwise Information theory Frequency a Multivariate (Rieke et al 1999;Borst et al 1999;Dayan and Abbott 2005 Legend: required/intended use, possible use, n/a not applicable. a Frequency meant in terms of a frequentist's probability estimate (Cox 1946).…”

Section: The Snap Shot Scorementioning

confidence: 99%

Causal pattern recovery from neural spike train data using the Snap Shot Score

2009

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We present a new approach to learning directed information flow networks from multi-channel spike train data. A novel scoring function, the Snap Shot Score, is used to assess potential networks with respect to their quality of causal explanation for the data. Additionally, we suggest a generic concept of plausibility in order to assess network learning techniques under partial observability conditions. Examples demonstrate the assessment of networks with the Snap Shot Score, and neural network simulations show its performance in complex situations with partial observability. We discuss the application of the new score to real data and indicate how it can be modified to suit other neural data types.

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Neuronal Spike Trains and Stochastic Point Processes

Cited by 1,352 publications

References 5 publications

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

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

Generation of theta activity (RSA) in the cingulate cortex of the rat

Causal pattern recovery from neural spike train data using the Snap Shot Score

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