Literature DB >> 28490643

Predicting the response of striatal spiny neurons to sinusoidal input.

Charles J Wilson1.   

Abstract

Spike-timing effects of small-amplitude sinusoidal currents were measured in mouse striatal spiny neurons firing repetitively. Spike-timing reliability varied with the stimulus frequency. For frequencies near the cell's firing rate, the cells altered firing rate to match the stimulus and became phase locked to it. The stimulus phase of firing during lock depended on the stimulus frequency relative to the cell's unperturbed firing rate. Interspike intervals during sinusoidal stimulation were predicted using an iterative map constructed from the cells' phase-resetting curve. Variability of interspike intervals was reduced by stimulation at all frequencies higher than about half the cell's unperturbed rate, and interspike intervals were accurately predicted by the map. Long sequences of spike times were predicted by iterating on the map. The accuracy of that prediction varied with frequency. Spike time predictability was highest near and during phase lock. The map predicted the phase of firing on the input and its dependence on stimulus frequency. Prediction errors, when they occurred, were of two kinds: unpredicted variation in interspike interval from intrinsic cell noise and accumulation of prediction errors from previous interspike intervals. Each type of prediction error arose from a different mechanism, and their impact was also predicted from the phase model. When two oscillatory input currents were presented simultaneously, striatal neurons responded selectively to only one of them, the one closest in frequency to the cell's unperturbed firing rate. Their spike times encoded the frequency and phase of that single oscillatory input.NEW & NOTEWORTHY During repetitive firing, the timing of action potentials is determined by the interaction between the input and voltage-sensitive currents throughout the interspike interval. This interaction is encapsulated in the neuron's phase-resetting curve. The phase-resetting curve predicted spike timing to small sinusoidal currents over a wide range of stimulus frequencies. Firing patterns were most sensitive to oscillatory components near the cell's own firing rate, even in the presence of noise and other inputs.
Copyright © 2017 the American Physiological Society.

Entities:  

Keywords:  basal ganglia; oscillation; phase resetting; predictability; spike time reliability

Mesh:

Year:  2017        PMID: 28490643      PMCID: PMC5539439          DOI: 10.1152/jn.00143.2017

Source DB:  PubMed          Journal:  J Neurophysiol        ISSN: 0022-3077            Impact factor:   2.714


  40 in total

1.  Biophysical basis of the phase response curve of subthalamic neurons with generalization to other cell types.

Authors:  Michael A Farries; Charles J Wilson
Journal:  J Neurophysiol       Date:  2012-07-11       Impact factor: 2.714

2.  Phase-response curves and synchronized neural networks.

Authors:  Roy M Smeal; G Bard Ermentrout; John A White
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2010-08-12       Impact factor: 6.237

3.  Two distinct mechanisms shape the reliability of neural responses.

Authors:  Susanne Schreiber; Inés Samengo; Andreas V M Herz
Journal:  J Neurophysiol       Date:  2009-02-04       Impact factor: 2.714

4.  Neuronal activity in the striatum and pallidum of primates related to the execution of externally cued reaching movements.

Authors:  D Jaeger; S Gilman; J W Aldridge
Journal:  Brain Res       Date:  1995-10-02       Impact factor: 3.252

5.  Influence of low and high frequency inputs on spike timing in visual cortical neurons.

Authors:  L G Nowak; M V Sanchez-Vives; D A McCormick
Journal:  Cereb Cortex       Date:  1997-09       Impact factor: 5.357

Review 6.  Neurons as oscillators.

Authors:  Klaus M Stiefel; G Bard Ermentrout
Journal:  J Neurophysiol       Date:  2016-09-28       Impact factor: 2.714

7.  Input-output relations in computer-simulated nerve cells. Influence of the statistical properties, strength, number and inter-dependence of excitatory pre-synaptic terminals.

Authors:  J P Segundo; D H Perkel; H Wyman; H Hegstad; G P Moore
Journal:  Kybernetik       Date:  1968-05

8.  Spontaneous subthreshold membrane potential fluctuations and action potential variability of rat corticostriatal and striatal neurons in vivo.

Authors:  E A Stern; A E Kincaid; C J Wilson
Journal:  J Neurophysiol       Date:  1997-04       Impact factor: 2.714

9.  How multiple conductances determine electrophysiological properties in a multicompartment model.

Authors:  Adam L Taylor; Jean-Marc Goaillard; Eve Marder
Journal:  J Neurosci       Date:  2009-04-29       Impact factor: 6.167

10.  Multiple spike time patterns occur at bifurcation points of membrane potential dynamics.

Authors:  J Vincent Toups; Jean-Marc Fellous; Peter J Thomas; Terrence J Sejnowski; Paul H Tiesinga
Journal:  PLoS Comput Biol       Date:  2012-10-18       Impact factor: 4.475

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  7 in total

1.  Frequency-dependent entrainment of striatal fast-spiking interneurons.

Authors:  Matthew H Higgs; Charles J Wilson
Journal:  J Neurophysiol       Date:  2019-07-17       Impact factor: 2.714

2.  Nucleus accumbens core medium spiny neuron electrophysiological properties and partner preference behavior in the adult male prairie vole, Microtus ochrogaster.

Authors:  Jaime A Willett; Ashlyn G Johnson; Andrea R Vogel; Heather B Patisaul; Lisa A McGraw; John Meitzen
Journal:  J Neurophysiol       Date:  2018-01-17       Impact factor: 2.714

Review 3.  Translating striatal activity from brain slice to whole animal neurophysiology: A guide for neuroscience research integrating diverse levels of analysis.

Authors:  Howard Casey Cromwell
Journal:  J Neurosci Res       Date:  2019-06-30       Impact factor: 4.164

4.  Predicting responses to inhibitory synaptic input in substantia nigra pars reticulata neurons.

Authors:  D V Simmons; M H Higgs; S Lebby; C J Wilson
Journal:  J Neurophysiol       Date:  2018-09-12       Impact factor: 2.714

5.  Local inhibition in a model of the indirect pathway globus pallidus network slows and deregularizes background firing, but sharpens and synchronizes responses to striatal input.

Authors:  Erick Olivares; Matthew H Higgs; Charles J Wilson
Journal:  J Comput Neurosci       Date:  2022-03-11       Impact factor: 1.453

6.  Population dynamics and entrainment of basal ganglia pacemakers are shaped by their dendritic arbors.

Authors:  Lior Tiroshi; Joshua A Goldberg
Journal:  PLoS Comput Biol       Date:  2019-02-07       Impact factor: 4.475

7.  Broadband Entrainment of Striatal Low-Threshold Spike Interneurons.

Authors:  Juan C Morales; Matthew H Higgs; Soomin C Song; Charles J Wilson
Journal:  Front Neural Circuits       Date:  2020-06-12       Impact factor: 3.492

  7 in total

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