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Neural Network Mechanisms Underlying Stimulus Driven Variability Reduction

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  • Gustavo Deco
  • Etienne Hugues

Abstract

It is well established that the variability of the neural activity across trials, as measured by the Fano factor, is elevated. This fact poses limits on information encoding by the neural activity. However, a series of recent neurophysiological experiments have changed this traditional view. Single cell recordings across a variety of species, brain areas, brain states and stimulus conditions demonstrate a remarkable reduction of the neural variability when an external stimulation is applied and when attention is allocated towards a stimulus within a neuron's receptive field, suggesting an enhancement of information encoding. Using an heterogeneously connected neural network model whose dynamics exhibits multiple attractors, we demonstrate here how this variability reduction can arise from a network effect. In the spontaneous state, we show that the high degree of neural variability is mainly due to fluctuation-driven excursions from attractor to attractor. This occurs when, in the parameter space, the network working point is around the bifurcation allowing multistable attractors. The application of an external excitatory drive by stimulation or attention stabilizes one specific attractor, eliminating in this way the transitions between the different attractors and resulting in a net decrease in neural variability over trials. Importantly, non-responsive neurons also exhibit a reduction of variability. Finally, this reduced variability is found to arise from an increased regularity of the neural spike trains. In conclusion, these results suggest that the variability reduction under stimulation and attention is a property of neural circuits. Author Summary: To understand how neurons encode information, neuroscientists record their firing activity while the animal executes a given task for many trials. Surprisingly, it has been found that the neural response is highly variable, which a priori limits the encoding of information by these neurons. However, recent experiments have shown that this variability is reduced when the animal receives a stimulus or attends to a particular one, suggesting an enhancement of information encoding. It is known that a cause of neural variability resides in the fact that individual neurons receive an input which fluctuates around their firing threshold. We demonstrate here that all the experimental results can naturally arise from the dynamics of a neural network. Using a realistic model, we show that the neural variability during spontaneous activity is particularly high because input noise induces large fluctuations between multiple –but unstable- network states. With stimulation or attention, one particular network state is stabilized and fluctuations decrease, leading to a neural variability reduction. In conclusion, our results suggest that the observed variability reduction is a property of the neural circuits of the brain.

Suggested Citation

  • Gustavo Deco & Etienne Hugues, 2012. "Neural Network Mechanisms Underlying Stimulus Driven Variability Reduction," PLOS Computational Biology, Public Library of Science, vol. 8(3), pages 1-10, March.
  • Handle: RePEc:plo:pcbi00:1002395
    DOI: 10.1371/journal.pcbi.1002395
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    1. József Fiser & Chiayu Chiu & Michael Weliky, 2004. "Small modulation of ongoing cortical dynamics by sensory input during natural vision," Nature, Nature, vol. 431(7008), pages 573-578, September.
    2. Gustavo Deco & Viktor K Jirsa & Peter A Robinson & Michael Breakspear & Karl Friston, 2008. "The Dynamic Brain: From Spiking Neurons to Neural Masses and Cortical Fields," PLOS Computational Biology, Public Library of Science, vol. 4(8), pages 1-35, August.
    3. Lyle J. Borg-Graham & Cyril Monier & Yves Frégnac, 1998. "Visual input evokes transient and strong shunting inhibition in visual cortical neurons," Nature, Nature, vol. 393(6683), pages 369-373, May.
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    2. Adam Ponzi & Jeffery R Wickens, 2013. "Optimal Balance of the Striatal Medium Spiny Neuron Network," PLOS Computational Biology, Public Library of Science, vol. 9(4), pages 1-21, April.

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