Resonance Effect for Neural Spike Time Reliability

Hunter, John D.; Milton, John; Thomas, Peter J.; Cowan, Jack D.

doi:10.1152/jn.1998.80.3.1427

Cited by 139 publications

(127 citation statements)

References 57 publications

(4 reference statements)

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“…3g). Across the two trials the neuron will thus produce distinct spike patterns 18,26 , as observed in vitro 19,23 . When there is a prolonged period without spiking on both trials, the voltage trajectories will converge back and the same spike pattern will be obtained.…”

mentioning

confidence: 89%

“…Simple models of an in vitro preparation in which synaptic transmission is blocked and the same somatic current is injected repeatedly suggest that imprecision is mainly due to variability in the membrane voltage just before the spike, and that this is inversely proportional to the rate of change of the voltage 19,20 . Thus, a precisely timed spike follows a rapidly depolarizing current.…”

Section: Factors Affecting Reliability and Precisionmentioning

confidence: 99%

“…When a small periodic drive is added (FIG. 4b), however, the precision will improve significantly when the stimulus frequency matches the DC firing rate 19,35,36 . This phenomenon occurs even when the neuron does not have a sub-threshold preferred frequency.…”

Section: Stimulus Locking and Phase Lockingmentioning

confidence: 99%

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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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confidence: 89%

Section: Factors Affecting Reliability and Precisionmentioning

confidence: 99%

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Regulation of spike timing in visual cortical circuits

2008

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“…This has been done in a variety of neuronal cell classes (Brumberg 2002, Carrandini et al 1996Hutcheon et al 1996;Nowak et al 1997;Hunter et al 1998;Volgushev et al 1998;Fellous et al 2001). Results suggest that cortical pyramidal neurons can be viewed as non-linear oscillators.…”

Section: Introductionmentioning

confidence: 99%

“…Results suggest that cortical pyramidal neurons can be viewed as non-linear oscillators. Additionally, subthreshold resonances intrinsic to the membrane have significant impact on the response properties of cortical neurons (Llinas 1991;Hutcheon et al 1996;Hunter et al 1998). This in turn could imply a certain degree of nonlinearity in the firing patterns of neurons in response to stimuli of varying frequencies.…”

Section: Introductionmentioning

confidence: 99%

Cortical pyramidal cells as non-linear oscillators: Experiment and spike-generation theory

Brumberg

Gutkin

2007

Brain Research

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Cortical neurons are capable of generating trains of action potentials in response to current injections. These discharges can take different forms, e.g. repetive firing that adapts during the period of current injection or bursting behaviors. We have used a combined experimental and computational approach to characterize the dynamics leading to action potential responses in single neurons. Specifically we investigated the origin of complex firing patterns in response to sinusoidal current injections. Using a reduced model, the theta neuron, alongside recordings from cortical pyramidal cells we show that both real and simulated neurons show phase locking to sine wave stimuli up to a critical frequency, above which period skipping and 1-to-x phase locking occurs. The locking behavior follows a complex "devil's staircase" phenomena, where locked modes are interleaved with irregular firing. We further show that the critical frequency depends on the time scale of spike generation and on the level of spike frequency adaptation. These results suggest that phase locking of neuronal responses to complex input patterns can be explained by basic properties of the spike generating machinery.

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A binless correlation measure reduces the variability of memory reactivation estimates

Kruskal

Stanis

McNaughton

et al. 2007

Statistics in Medicine

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The standard procedure for measuring correlations between pairs of spike trains is to count the numbers of spikes occurring within a specified set of time intervals partitioning the continuous time line into discrete bins of width w (seconds). One then computes the Pearson correlation between pairs of the bin occupancy vectors. This method introduces a form of quantization noise, similar to that in analog-to-digital signal processing devices, due to the arbitrary positioning of bin boundaries relative to pairs of spikes. Small changes in bin width and small uniform shifts in bin boundaries typically produce large variations in the apparent correlation. An alternative method of determining a correlation between pairs of spike trains was recently introduced. Rather than discretize the data in time, the original spike trains are convolved with a Gaussian kernel with parameter sigma chosen to give an effective width matching the bin width omega = square root 12 sigma . Calculating the Pearson correlation of the resulting smooth functions gives an estimate of the correlation between the spike trains matching that given by the bin-based procedure, without introducing the significant variability of the bin-based estimate. Measures of memory reactivation based on the partial correlations between ensembles of pairwise spike train correlations are biased downwards by the quantization noise present in the pairwise correlation estimates. Using a binless method to measure pairwise correlation, we find that the partial correlation (or explained variance) of rat hippocampal maze activity and post-maze sleep, taking into account pre-maze sleep correlations, increases significantly over estimates made with the standard bin-based procedure.

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Resonance Effect for Neural Spike Time Reliability

Cited by 139 publications

References 57 publications

Regulation of spike timing in visual cortical circuits

Regulation of spike timing in visual cortical circuits

Cortical pyramidal cells as non-linear oscillators: Experiment and spike-generation theory

A binless correlation measure reduces the variability of memory reactivation estimates

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