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Balanced Synaptic Input Shapes the Correlation between Neural Spike Trains

Author

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  • Ashok Litwin-Kumar
  • Anne-Marie M Oswald
  • Nathaniel N Urban
  • Brent Doiron

Abstract

Stimulus properties, attention, and behavioral context influence correlations between the spike times produced by a pair of neurons. However, the biophysical mechanisms that modulate these correlations are poorly understood. With a combined theoretical and experimental approach, we show that the rate of balanced excitatory and inhibitory synaptic input modulates the magnitude and timescale of pairwise spike train correlation. High rate synaptic inputs promote spike time synchrony rather than long timescale spike rate correlations, while low rate synaptic inputs produce opposite results. This correlation shaping is due to a combination of enhanced high frequency input transfer and reduced firing rate gain in the high input rate state compared to the low state. Our study extends neural modulation from single neuron responses to population activity, a necessary step in understanding how the dynamics and processing of neural activity change across distinct brain states. Author Summary: Neurons in sensory, motor, and cognitive regions of the nervous system integrate synaptic input and output trains of action potentials (spikes). A critical feature of neural computation is the ability for neurons to modulate their spike train response to a given input, allowing task context or past history to affect the flow of information in the brain. The mechanisms that modulate the input-output transfer of single neurons have received significant attention. However, neural computation involves the coordinated activity of populations of neurons, and the mechanisms that modulate the correlation between spike trains from pairs of neurons are relatively unexplored. We show that the level of excitatory and inhibitory input that a neuron receives modulates not only the sensitivity of a single neuron's response to input, but also the magnitude and timescale of correlated spiking activity of pairs of neurons receiving a common synaptic drive. Thus, while modulatory synaptic activity has been traditionally studied from a single neuron perspective, it can also shape the coordinated activity of a population of neurons.

Suggested Citation

  • Ashok Litwin-Kumar & Anne-Marie M Oswald & Nathaniel N Urban & Brent Doiron, 2011. "Balanced Synaptic Input Shapes the Correlation between Neural Spike Trains," PLOS Computational Biology, Public Library of Science, vol. 7(12), pages 1-14, December.
    • Handle: RePEc:plo:pcbi00:1002305
    DOI: 10.1371/journal.pcbi.1002305
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    References listed on IDEAS

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    1. Jon Cafaro & Fred Rieke, 2010. "Noise correlations improve response fidelity and stimulus encoding," Nature, Nature, vol. 468(7326), pages 964-967, December.
    2. Cheng Ly & Brent Doiron, 2009. "Divisive Gain Modulation with Dynamic Stimuli in Integrate-and-Fire Neurons," PLOS Computational Biology, Public Library of Science, vol. 5(4), pages 1-12, April.
    3. Diego A. Gutnisky & Valentin Dragoi, 2008. "Adaptive coding of visual information in neural populations," Nature, Nature, vol. 452(7184), pages 220-224, March.
    4. James F. A. Poulet & Carl C. H. Petersen, 2008. "Internal brain state regulates membrane potential synchrony in barrel cortex of behaving mice," Nature, Nature, vol. 454(7206), pages 881-885, August.
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    6. Jaime de la Rocha & Brent Doiron & Eric Shea-Brown & Krešimir Josić & Alex Reyes, 2007. "Correlation between neural spike trains increases with firing rate," Nature, Nature, vol. 448(7155), pages 802-806, August.
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    Cited by:

    1. Gabriel Koch Ocker & Ashok Litwin-Kumar & Brent Doiron, 2015. "Self-Organization of Microcircuits in Networks of Spiking Neurons with Plastic Synapses," PLOS Computational Biology, Public Library of Science, vol. 11(8), pages 1-40, August.
    2. Andrea K Barreiro & Shree Hari Gautam & Woodrow L Shew & Cheng Ly, 2017. "A theoretical framework for analyzing coupled neuronal networks: Application to the olfactory system," PLOS Computational Biology, Public Library of Science, vol. 13(10), pages 1-37, October.

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