PRC Estimation with Varying Width Intervals

Polhamus, Daniel; Wilson, Charles J.; Paladini, Carlos A.

doi:10.1007/978-1-4614-0739-3_7

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…2A and 3A) does not result from a rate-independent time-to-spike preference. Our observations are not changed when using the truncated Gaussian method [45] as illustrated in S2 Fig.…”

Section: Resultsmentioning

confidence: 79%

“…1A ), employed to speed-up convergence to the firing rate steady state and reduce very slow fluctuations. In addition, following [ 45 ] we employed a quasi-random Sobol number generator in a reactive-clamp configuration, to sample uniformly and more efficiently the input phase φ interval [0; 1]. Using the Khaliq-Raman model (see Methods ), we investigated how the closed loop system affects the PRC estimate.…”

Section: Resultsmentioning

confidence: 99%

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On the Firing Rate Dependency of the Phase Response Curve of Rat Purkinje Neurons In Vitro

et al. 2015

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Synchronous spiking during cerebellar tasks has been observed across Purkinje cells: however, little is known about the intrinsic cellular mechanisms responsible for its initiation, cessation and stability. The Phase Response Curve (PRC), a simple input-output characterization of single cells, can provide insights into individual and collective properties of neurons and networks, by quantifying the impact of an infinitesimal depolarizing current pulse on the time of occurrence of subsequent action potentials, while a neuron is firing tonically. Recently, the PRC theory applied to cerebellar Purkinje cells revealed that these behave as phase-independent integrators at low firing rates, and switch to a phase-dependent mode at high rates. Given the implications for computation and information processing in the cerebellum and the possible role of synchrony in the communication with its post-synaptic targets, we further explored the firing rate dependency of the PRC in Purkinje cells. We isolated key factors for the experimental estimation of the PRC and developed a closed-loop approach to reliably compute the PRC across diverse firing rates in the same cell. Our results show unambiguously that the PRC of individual Purkinje cells is firing rate dependent and that it smoothly transitions from phase independent integrator to a phase dependent mode. Using computational models we show that neither channel noise nor a realistic cell morphology are responsible for the rate dependent shift in the phase response curve.

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“…2A and 3A) does not result from a rate-independent time-to-spike preference. Our observations are not changed when using the truncated Gaussian method [45] as illustrated in S2 Fig.…”

Section: Resultsmentioning

confidence: 79%

Section: Resultsmentioning

confidence: 99%

On the Firing Rate Dependency of the Phase Response Curve of Rat Purkinje Neurons In Vitro

et al. 2015

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“…However, for a given stimulated ISI, we do have some additional information about its unstimulated duration: we know that it would have been at least as long as the input time, i.e., the stimulus time relative to the most recent spike. Instead of using the average of all spontaneous ISIs as our estimator of the unstimulated ISI, we use the average only of spontaneous ISIs longer than the input time; this avoids some artifacts associated with empirical PRC estimation (Polhamus et al 2012). Furthermore, the pool of spontaneous ISIs used to compute this average for a given stimulated ISI is limited to those occurring within 30 s of the stimulus, to account for the effects of any slow nonstationarities in the spontaneous ISI distribution.…”

Section: Methodsmentioning

confidence: 99%

Phase response curves of subthalamic neurons measured with synaptic input and current injection

Farries

Wilson

2012

Journal of Neurophysiology

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Farries MA, Wilson CJ. Phase response curves of subthalamic neurons measured with synaptic input and current injection. J Neurophysiol 108: 1822-1837. First published July 11, 2012 doi:10.1152/jn.00053.2012.-Infinitesimal phase response curves (iPRCs) provide a simple description of the response of repetitively firing neurons and may be used to predict responses to any pattern of synaptic input. Their simplicity makes them useful for understanding the dynamics of neurons when certain conditions are met. For example, the sizes of evoked phase shifts should scale linearly with stimulus strength, and the form of the iPRC should remain relatively constant as firing rate varies. We measured the PRCs of rat subthalamic neurons in brain slices using corticosubthalamic excitatory postsynaptic potentials (EPSPs; mediated by both AMPA-and NMDA-type receptors) and injected current pulses and used them to calculate the iPRC. These were relatively insensitive to both the size of the stimulus and the cell's firing rate, suggesting that the iPRC can predict the response of subthalamic nucleus cells to extrinsic inputs. However, the iPRC calculated using EPSPs differed from that obtained using current pulses. EPSPs (normalized for charge) were much more effective at altering the phase of subthalamic neurons than current pulses. The difference was not attributable to the extended time course of NMDA receptor-mediated currents, being unaffected by blockade of NMDA receptors. The iPRC provides a good description of subthalamic neurons' response to input, but iPRCs are best estimated using synaptic inputs rather than somatic current injection.

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Hippocampal CA1 pyramidal neurons exhibit type 1 phase-response curves and type 1 excitability

Wang

Musharoff

Canavier

et al. 2013

Journal of Neurophysiology

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Phase-resetting properties of neurons determine their functionality as integrators (type 1) vs. resonators (type 2), as well as their synchronization tendencies. We introduce a novel, bias-correction method to estimate the infinitesimal phase-resetting curve (iPRC) and confirm type 1 excitability in hippocampal pyramidal CA1 neurons in vitro by two independent methods. First, PRCs evoked using depolarizing pulses consisted only of advances, consistent with type 1. Second, the frequency/current (f/I) plots showed no minimum frequency, again consistent with type 1. Type 1 excitability was also confirmed by the absence of a resonant peak in the interspike interval histograms derived from the f/I data. The PRC bias correction assumed that the distribution of noisy phase resetting is truncated, because an input cannot advance a spike to a point in time before the input (the causal limit) and successfully removed the statistical bias for delays in the null PRC in response to zero-magnitude input by computing the phase resetting as the mean of the untruncated distribution. The PRC for depolarization peaked at late phases and decreased to zero by the end of the cycle, whereas delays observed in response to hyperpolarization increased monotonically. The bias correction did not affect this difference in shape, which was due instead to the causal limit obscuring the iPRC for depolarization but not hyperpolarization. Our results suggest that weak periodic hyperpolarizing drive can theoretically entrain CA1 pyramidal neurons at any phase but that strong excitation will preferentially phase-lock them with zero time lag.

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PRC Estimation with Varying Width Intervals

Cited by 5 publications

References 11 publications

On the Firing Rate Dependency of the Phase Response Curve of Rat Purkinje Neurons In Vitro

On the Firing Rate Dependency of the Phase Response Curve of Rat Purkinje Neurons In Vitro

Phase response curves of subthalamic neurons measured with synaptic input and current injection

Hippocampal CA1 pyramidal neurons exhibit type 1 phase-response curves and type 1 excitability

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