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A phase response curve (PRC) describes the dependency of shifts in spike timing on the phase at which an input occurs within the ongoing inter-spike interval (ISI). We have previously applied PRC analysis to a morphologically realistic GP neuron model exhibiting intrinsically driven regular spiking. Somatic or proximal dendritic inputs resulted in an advance of the next spike that depended on the input phase and amplitude of the input. In contrast, distal dendritic inputs led to a delay of the subsequent spike if an excitatory input was delivered early in the spike cycle. In vivo, GP neurons are driven to spiking by a balance of excitatory and inhibitory inputs in addition to intrinsic pacemaking. To evaluate how a background of synaptic inputs to GP might influence the phase response properties of an additional test input, we constructed phase response curves for the following conditions: 1) randomly timed, spatially distributed synaptic background leading to irregular spike activity, and 2) a tonic increase in dendritic conductance matching the average synaptic input while removing stochasticity. Baseline simulations were run without stimuli to determine times of control spikes. Then a current injection was delivered at one of 36 timepoints within the ISI to somatic, proximal or distal dendritic locations. PRCs constructed from the tonic case were similar to those previously measured in the absence of synaptic background, except the positive peaks of the PRCs were attenuated when input was applied distally. With stochastic synaptic background, somatic or proximal dendritic stimuli could elicit a spike at any time within the spike cycle if the momentary balance of synaptic inputs brought the neuron close to threshold. To estimate the average effect of stochastic background inputs, we obtained phase response curves for 500 different random background input patterns. The resulting average PRC showed a peak early in the spike cycle, indicating a period of time after a synaptically triggered spike during which the neuron was still close to firing threshold. Thus, for proximal inputs stochastic synaptic background changes the phase response curve considerably by favoring additional spiking early in the synaptically driven spike cycle. In contrast, distal inputs were not large enough to directly trigger spikes, but still resulted on average in either small spike delays or advances depending on input phase even in the presence of stochastic backgrounds. These findings suggest that stochastic background inputs influence GP neuron responses to additional input signals, and thus a change in background input pattern in Parkinson's disease could disturb signal processing.

Authors:	*N. W. SCHULTHEISS, J. R. EDGERTON, D. JAEGER; Dept Biol., Emory Univ., Atlanta, GA
A phase response curve (PRC) describes the dependency of shifts in spike timing on the phase at which an input occurs within the ongoing inter-spike interval (ISI). We have previously applied PRC analysis to a morphologically realistic GP neuron model exhibiting intrinsically driven regular spiking. Somatic or proximal dendritic inputs resulted in an advance of the next spike that depended on the input phase and amplitude of the input. In contrast, distal dendritic inputs led to a delay of the subsequent spike if an excitatory input was delivered early in the spike cycle. In vivo, GP neurons are driven to spiking by a balance of excitatory and inhibitory inputs in addition to intrinsic pacemaking. To evaluate how a background of synaptic inputs to GP might influence the phase response properties of an additional test input, we constructed phase response curves for the following conditions: 1) randomly timed, spatially distributed synaptic background leading to irregular spike activity, and 2) a tonic increase in dendritic conductance matching the average synaptic input while removing stochasticity. Baseline simulations were run without stimuli to determine times of control spikes. Then a current injection was delivered at one of 36 timepoints within the ISI to somatic, proximal or distal dendritic locations. PRCs constructed from the tonic case were similar to those previously measured in the absence of synaptic background, except the positive peaks of the PRCs were attenuated when input was applied distally. With stochastic synaptic background, somatic or proximal dendritic stimuli could elicit a spike at any time within the spike cycle if the momentary balance of synaptic inputs brought the neuron close to threshold. To estimate the average effect of stochastic background inputs, we obtained phase response curves for 500 different random background input patterns. The resulting average PRC showed a peak early in the spike cycle, indicating a period of time after a synaptically triggered spike during which the neuron was still close to firing threshold. Thus, for proximal inputs stochastic synaptic background changes the phase response curve considerably by favoring additional spiking early in the synaptically driven spike cycle. In contrast, distal inputs were not large enough to directly trigger spikes, but still resulted on average in either small spike delays or advances depending on input phase even in the presence of stochastic backgrounds. These findings suggest that stochastic background inputs influence GP neuron responses to additional input signals, and thus a change in background input pattern in Parkinson's disease could disturb signal processing.

Support:	NINDS RO1 NS039852