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A theoretical framework for analyzing coupled neuronal networks: Application to the olfactory system

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  • Andrea K Barreiro
  • Shree Hari Gautam
  • Woodrow L Shew
  • Cheng Ly

Abstract

Determining how synaptic coupling within and between regions is modulated during sensory processing is an important topic in neuroscience. Electrophysiological recordings provide detailed information about neural spiking but have traditionally been confined to a particular region or layer of cortex. Here we develop new theoretical methods to study interactions between and within two brain regions, based on experimental measurements of spiking activity simultaneously recorded from the two regions. By systematically comparing experimentally-obtained spiking statistics to (efficiently computed) model spike rate statistics, we identify regions in model parameter space that are consistent with the experimental data. We apply our new technique to dual micro-electrode array in vivo recordings from two distinct regions: olfactory bulb (OB) and anterior piriform cortex (PC). Our analysis predicts that: i) inhibition within the afferent region (OB) has to be weaker than the inhibition within PC, ii) excitation from PC to OB is generally stronger than excitation from OB to PC, iii) excitation from PC to OB and inhibition within PC have to both be relatively strong compared to presynaptic inputs from OB. These predictions are validated in a spiking neural network model of the OB–PC pathway that satisfies the many constraints from our experimental data. We find when the derived relationships are violated, the spiking statistics no longer satisfy the constraints from the data. In principle this modeling framework can be adapted to other systems and be used to investigate relationships between other neural attributes besides network connection strengths. Thus, this work can serve as a guide to further investigations into the relationships of various neural attributes within and across different regions during sensory processing.Author summary: Sensory processing is known to span multiple regions of the nervous system. However, electrophysiological recordings during sensory processing have traditionally been limited to a single region or brain layer. With recent advances in experimental techniques, recorded spiking activity from multiple regions simultaneously is feasible. However, other important quantities— such as inter-region connection strengths—cannot yet be measured. Here, we develop new theoretical tools to leverage data obtained by recording from two different brain regions simultaneously. We address the following questions: what are the crucial neural network attributes that enable sensory processing across different regions, and how are these attributes related to one another? With a novel theoretical framework to efficiently calculate spiking statistics, we can characterize a high dimensional parameter space that satisfies data constraints. We apply our results to the olfactory system to make specific predictions about effective network connectivity. Our framework relies on incorporating relatively easy-to-measure quantities to predict hard-to-measure interactions across multiple brain regions. Because this work is adaptable to other systems, we anticipate it will be a valuable tool for analysis of other larger scale brain recordings.

Suggested Citation

  • 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.
  • Handle: RePEc:plo:pcbi00:1005780
    DOI: 10.1371/journal.pcbi.1005780
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    References listed on IDEAS

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    1. Shree Hari Gautam & Thanh T Hoang & Kylie McClanahan & Stephen K Grady & Woodrow L Shew, 2015. "Maximizing Sensory Dynamic Range by Tuning the Cortical State to Criticality," PLOS Computational Biology, Public Library of Science, vol. 11(12), pages 1-15, December.
    2. James Trousdale & Yu Hu & Eric Shea-Brown & Krešimir Josić, 2012. "Impact of Network Structure and Cellular Response on Spike Time Correlations," PLOS Computational Biology, Public Library of Science, vol. 8(3), pages 1-15, March.
    3. Stojan Jovanović & Stefan Rotter, 2016. "Interplay between Graph Topology and Correlations of Third Order in Spiking Neuronal Networks," PLOS Computational Biology, Public Library of Science, vol. 12(6), pages 1-28, June.
    4. Andrea K Barreiro & Cheng Ly, 2017. "When do correlations increase with firing rates in recurrent networks?," PLOS Computational Biology, Public Library of Science, vol. 13(4), pages 1-30, April.
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    6. Michael A Buice & Carson C Chow, 2013. "Dynamic Finite Size Effects in Spiking Neural Networks," PLOS Computational Biology, Public Library of Science, vol. 9(1), pages 1-21, January.
    7. 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.
    8. Ifije E. Ohiorhenuan & Ferenc Mechler & Keith P. Purpura & Anita M. Schmid & Qin Hu & Jonathan D. Victor, 2010. "Sparse coding and high-order correlations in fine-scale cortical networks," Nature, Nature, vol. 466(7306), pages 617-621, July.
    9. Olivier Gschwend & Jonathan Beroud & Alan Carleton, 2012. "Encoding Odorant Identity by Spiking Packets of Rate-Invariant Neurons in Awake Mice," PLOS ONE, Public Library of Science, vol. 7(1), pages 1-12, January.
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