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Formation and disruption of tonotopy in a large-scale model of the auditory cortex

Abstract

There is ample experimental evidence describing changes of tonotopic organisation in the auditory cortex due to environmental factors. In order to uncover the underlying mechanisms, we designed a large-scale computational model of the auditory cortex. The model has up to 100 000 Izhikevich’s spiking neurons of 17 different types, almost 21 million synapses, which are evolved according to Spike-Timing-Dependent Plasticity (STDP) and have an architecture akin to existing observations. Validation of the model revealed alternating synchronised/desynchronised states and different modes of oscillatory activity. We provide insight into these phenomena via analysing the activity of neuronal subtypes and testing different causal interventions into the simulation. Our model is able to produce experimental predictions on a cell type basis. To study the influence of environmental factors on the tonotopy, different types of auditory stimulations during the evolution of the network were modelled and compared. We found that strong white noise resulted in completely disrupted tonotopy, which is consistent with in vivo experimental observations. Stimulation with pure tones or spontaneous activity led to a similar degree of tonotopy as in the initial state of the network. Interestingly, weak white noise led to a substantial increase in tonotopy. As the STDP was the only mechanism of plasticity in our model, our results suggest that STDP is a sufficient condition for the emergence and disruption of tonotopy under various types of stimuli. The presented large-scale model of the auditory cortex and the core simulator, SUSNOIMAC, have been made publicly available.

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Notes

  1. The scalingFactor is equal to 20 and corresponds to scaled density of synapses in comparison to the real cortex and is adopted from (Izhikevich and Edelman 2008).

  2. Licenced under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public Licence. The code is at https://sites.google.com/site/susnoimac, last accessed: July 6, 2015.

  3. Processor: Intel(R) Core(TM) i7 930 @ 2.80 GHz, RAM: 16 GB, OS: Windows 7.

  4. The minute-long window contains sixty values of firing rate, one for each second in the window. The mean and standard deviation are computed from these sixty values.

  5. We focused only on desynchronised states and nearly desynchronised states in this analysis in order to have the largest oscillations in the gamma band.

  6. Input neurons (i.e., neurons from layer L4) are not accounted because they are trivially tonotopic and would skew the results by enforcing that every network has a large quantity of perfectly tonotopic neurons.

  7. This means intensity corresponding to the probability 0.5, i.e., each neuron in the input layer has, in each time step, a probability 0.5 to be externally stimulated, as defined in Section 2.2.

  8. Note that computation of a single 12 h development of a 50k network, along with the measurement of tonotopy, takes circa 10 days; we therefore could not compute more repetitions with different seeds.

  9. The reason for using Kruskal-Wallis ANOVA (instead of standard ANOVA) is the fact that it does not require normally distributed data.

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Acknowledgments

We would like to thank Elisa Brann, Martin Popel and Felix Zhou for help with polishing the manuscript and The Bakala Foundation for financial support of the two first authors during their studies. This work was supported by the Grant Agency of the Czech Republic (P303/12/1347), by PRVOUK “P46 – Informatika”, and by Engineering and Physical Sciences Research Council (EP/F500394/1). This work made use of the Open Science Data Cloud (OSDC) which is an Open Cloud Consortium (OCC)-sponsored project (Grossman et al. 2012); which is a work supported in part by grants from Gordon and Betty Moore Foundation and the National Science Foundation and major contributions from OCC members like the University of Chicago.

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The authors declare that they have no conflict of interest.

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Correspondence to Markéta Tomková or Ondřej Novák.

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Tomková, M., Tomek, J., Novák, O. et al. Formation and disruption of tonotopy in a large-scale model of the auditory cortex. J Comput Neurosci 39, 131–153 (2015). https://doi.org/10.1007/s10827-015-0568-2

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  • DOI: https://doi.org/10.1007/s10827-015-0568-2

Keywords

  • Auditory cortex
  • Large-scale model
  • Spiking neuron
  • Oscillation
  • STDP
  • Tonotopy