Neuronal Spike Trains and Stochastic Point Processes

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

doi:10.1016/s0006-3495(67)86596-2

Cited by 1,139 publications

(447 citation statements)

References 14 publications

Supporting

Mentioning

438

Contrasting

Order By: Relevance

“…One property of this function is that it asymptotes to a constant limiting value so that highly regular spike trains reach this value more slowly than spike trains with a higher degree of variability. Thus, the number of peaks in the autocorrelogram that occur at integral multiples of the mean interspike interval, before reaching a constant limiting value, represent an index of regularity of firing (31). Neurons that exhibit an initial peak with a decay to a steady-state level are classified as bursty, whereas neurons that exhibit a random firing pattern display an autocorrelation function characterized by an initial trough that rises to a steady-state value (6).…”

Section: Methodsmentioning

confidence: 99%

Dopamine controls the firing pattern of dopamine neurons via a network feedback mechanism

Paladini

Robinson

Morikawa

et al. 2003

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

View full text Add to dashboard Cite

Changes in the firing pattern of midbrain dopamine neurons are thought to encode information for certain types of reward-related learning. In particular, the burst pattern of firing is predicted to result in more efficient dopamine release at target loci, which could underlie changes in synaptic plasticity. In this study, the effects of dopamine on the firing patterns of dopaminergic neurons in vivo and their electrophysiological characteristics in vitro were examined by using a genetic dopamine-deficient (DD) mouse model. Extracellular recordings in vivo showed that, although the firing pattern of dopamine neurons in normal mice included bursting activity, DD mice recordings showed only a single-spike pattern of activity with no bursts. Bursting was restored in DD mice after systemic administration of the dopamine precursor, L-3,4-dihydroxyphenylalanine (L-dopa). Whole-cell recordings in vitro demonstrated that the basic electrophysiology and pharmacology of dopamine neurons were identical between DD and control mice, except that amphetamine did not elicit a hyperpolarizing current in slices from DD mice. These data suggest that endogenously released dopamine plays a critical role in the afferent control of dopamine neuron bursting activity and that this control is exerted via a network feedback mechanism.T he activity of dopamine neurons has been shown to correlate with behavioral adaptations during reward-related learning in primates and rodents (1-4). Dopamine neurons fire spontaneously in vivo in a spectrum of patterns ranging from pacemaker, to random, to bursting modes (5, 6). Clusters of two to eight spikes characterize the burst mode (7,8). The random mode is the most common pattern encountered in vivo and is characterized by bursts of spikes followed by single-spike activity (5, 9). The pacemaker pattern, encountered in Ϸ20% of neurons recorded in vivo, is characterized by single spikes firing in a clock-like manner, interrupted by infrequent bursts (2,5,6). Determining the origins and mechanisms responsible for burst firing in vivo is of interest because this firing pattern is thought to be responsible for large increases in dopamine release in the striatum that may mediate synaptic plasticity and contribute to reward-related learning (4, 10-17).The only pattern recorded spontaneously in vitro is the single-spike, pacemaker pattern without bursts (18)(19)(20). This contrasts markedly with recordings in vivo where bursts can still be encountered even if a neuron is classified as firing in a pacemaker mode (2). This disparity between in vivo and in vitro recordings suggests that afferents play a critical role in the control of dopamine neuron firing pattern.Release of dopamine in the basal ganglia and other projection areas may influence the afferent regulation of dopamine neurons through reciprocal and other long distance, multisynaptic connections (e.g., see ref. 21). This study investigates the effects of removing dopamine on the activity of dopamine neurons by using mice that were rendered dopamine-...

show abstract

Section: Methodsmentioning

confidence: 99%

Dopamine controls the firing pattern of dopamine neurons via a network feedback mechanism

Paladini

Robinson

Morikawa

et al. 2003

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

View full text Add to dashboard Cite

show abstract

“…In a model with fall times of the coupling between the oscillators, Tsodyks et al [40] showed that the complete synchronized state is unstable to inhomogeneity in the oscillators frequencies. Finally Gerstner [43,17] achieved a synthesis of results on IF oscillators models by introducing a general model containing various versions of IF models as special cases and an analytical approach from the point of view of a renewal theory [44,45,46]. In the previous studies the oscillators are typically model neurons described as leaky integrator on a membrane potential.…”

Section: Introductionmentioning

confidence: 99%

Synchronization of integrate and fire oscillators with global coupling

Bottani

1996

Phys. Rev. E

View full text Add to dashboard Cite

In this article we study the behavior of globally coupled assemblies of a large number of Integrate and Fire oscillators with excitatory pulse-like interactions. On some simple models we show that the additive effects of pulses on the state of Integrate and Fire oscillators are sufficient for the synchronization of the relaxations of all the oscillators. This synchronization occurs in two forms depending on the system: either the oscillators evolve "en bloc" at the same phase and therefore relax together or the oscillators do not remain in phase but their relaxations occur always in stable avalanches. We prove that synchronization can occur independently of the convexity or concavity of the oscillators evolution function. Furthermore the presence of disorder, up to some level, is not only compatible with synchronization, but removes some possible degeneracy of identical systems and allows new mechanisms towards this state.

show abstract

“…The coefficient of reliability ¶ is a quantitative measure of the variance of spike time firing to repeated identical stimuli, adapted from multiple crosscorrelation analysis (13). Each single trial recording, considered as a point process (14), was convolved with a Gaussian smoothing function (standard deviation of 10 msec) truncated with a total width of three standard deviations. The reliability coefficient is the mean crosscorrelation of each smoothed single-trial spike train with every other smoothed single-trial spike train in a block of trials.…”

Section: Methodsmentioning

confidence: 99%

Masking and scrambling in the auditory thalamus of awake rats by Gaussian and modulated noises

Martin

West

Bedenbaugh

2004

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

View full text Add to dashboard Cite

This paper provides a look at how modulated broad-band noises modulate the thalamic response evoked by brief probe sounds in the awake animal. We demonstrate that noise not only attenuates the response to probe sounds (masking) but also changes the temporal response pattern (scrambling). Two brief probe sounds, a Gaussian noise burst and a brief sinusoidal tone, were presented in silence and in three ongoing noises. The three noises were targeted at activating the auditory system in qualitatively distinct ways. Dynamic ripple noise, containing many random tone-like elements, is targeted at those parts of the auditory system that respond well to tones. International Collegium of Rehabilitative Audiology noise, comprised of the sum of several simultaneous streams of Schroeder-phase speech, is targeted at those parts of the auditory system that respond well to modulated sounds but lack a well defined response to tones. Gaussian noise is targeted at those parts of the auditory system that respond to acoustic energy regardless of modulation. All noises both attenuated and decreased the precise temporal repeatability of the onset response to probe sounds. In addition, the modulated noises induced contextspecific changes in the temporal pattern of the response to probe sounds. Scrambling of the temporal response pattern may be a direct neural correlate of the unfortunate experience of being able to hear, but not understand, speech sounds in noisy environments.T his paper examines how neurons in the auditory thalamus [medial geniculate body (MGB)] of awake rats respond to transient probe sounds in silence and in the presence of three ongoing noises. We demonstrate that noise changes not only probe response magnitude but also changes the temporal pattern of the probe-evoked response.The unwanted acoustic interference in natural listening situations is more closely modeled by modulated noise than by Gaussian, pink noise. This situation forces audiologists to employ complex noises, such as multispeaker ''babble,'' to predict the ability of hearing-aid users to comprehend speech in noisy environments. This study selected two complementary noises from the vast space of possible modulated noises. One noise was derived from a standardized babble noise developed by the International Collegium of Rehabilitative Audiology (ICRA) (1). The noise contains speech-like temporal modulations but without the narrow-band, tone-like elements of speech. Complementing the ICRA-derived noise is dynamic-ripple noise (DRN) (2), which was originally conceived as an aid to study responses of auditory neurons by the white-noise system identification approach (3). DRN presents many combinations of tone-like elements in a random order. Together, these noises should randomly stimulate parts of the auditory system that respond quite selectively to tones within a relatively narrow frequency range and parts of the auditory system that respond better to broad-band sounds.This study focused on the MGB because, in anesthetized animals, thalamic neurons res...

show abstract

Neuronal Spike Trains and Stochastic Point Processes

Cited by 1,139 publications

References 14 publications

Dopamine controls the firing pattern of dopamine neurons via a network feedback mechanism

Dopamine controls the firing pattern of dopamine neurons via a network feedback mechanism

Synchronization of integrate and fire oscillators with global coupling

Masking and scrambling in the auditory thalamus of awake rats by Gaussian and modulated noises

Contact Info

Product

Resources

About